# TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-
shot Genome Classification

Sathyanarayanan N. Aakur, , Vishalini R. Laguduva, and Priyadharsini
Ramamurthy, and Akhilesh Ramachandran  Submitted on 08 June, 2023. Revised on
27 September, 2023. This research was supported in part by the US Department
of Agriculture (USDA) grants AP20VSD and B000C011. Sathyanarayanan N. Aakur
and Vishalini R. Laguduva are with the Department of Computer Science and
Software Engineering, Auburn University, Auburn, AL 36849 (e-mail:
<EMAIL_ADDRESS>Work was partially done while they were at
OSU.Priyadharsini Ramamurthy is with the Department of Computer Science at
Oklahoma State University, Stillwater, OK 74078 USA. Akhilesh Ramachandran is
with the Oklahoma Animal Disease Diagnostics Laboratory, College of Veterinary
Medicine, Oklahoma State University, Stillwater, OK 74078 (e-mail:
rakhile@okstate.edu)Corresponding authors: SN Aakur, A. Ramachandran

###### Abstract

A species’ genetic code or genome encodes valuable evolutionary, biological,
and phylogenetic information that aids in species recognition, taxonomic
classification, and understanding genetic predispositions like drug resistance
and virulence. However, the vast number of potential species poses significant
challenges in developing a general-purpose whole genome classification tool.
Traditional bioinformatics tools have made notable progress but lack
scalability and are computationally expensive. Machine learning-based
frameworks show promise but must address the issue of large classification
vocabularies with long-tail distributions. In this study, we propose
addressing this problem through zero-shot learning using TEPI, Taxonomy-aware
Embedding and Pseudo-Imaging. We represent each genome as pseudo-images and
map them to a taxonomy-aware embedding space for reasoning and classification.
This embedding space captures compositional and phylogenetic relationships of
species, enabling predictions in extensive search spaces. We evaluate TEPI
using two rigorous zero-shot settings and demonstrate its generalization
capabilities qualitatively on curated, large-scale, publicly sourced data.

###### Index Terms:

Genome Classification, Taxonomic Classification, Zero-shot learning

##  I Introduction

The genome sequence of an organism offers valuable insights into disease
susceptibility (e.g., cancer in humans and animals) and quantitative traits
like milk production in animals [1] or crop yield in plants [2]. Recent
technological advancements have made nucleic acid sequencing (DNA or RNA
sequencing) of animals, plants, and microbes affordable and commonplace [3].
In microbiology, whole and partial genome sequencing is frequently used to
study the origins, pathogenicity, and phylogenetic relationships of bacterial
and viral organisms [4]. Genome sequencing is crucial for phylogenetic
classification and determining virulence potential, such as toxin production,
antibiotic resistance, or invasive abilities, in bacterial organisms [5].

Figure 1: Illustration of the Overall Workflow of the proposed framework. We
learn a taxonomy-aware embedding space during training that is used to predict
taxonomies of query genomes during testing. Dotted red lines indicate
learning.

However, nucleic acid sequencing generates large amounts of microbial genome
data. Traditional bioinformatic pipelines for genome analysis, particularly
for whole-genome-based phylogenetic studies, are computationally intensive and
time-consuming [3, 6]. Developing efficient computational frameworks for
processing whole genomes is essential to enable epidemiological, diagnostic,
source attribution, and phylogenetic applications. Machine learning
approaches, driven by advances in deep learning, offer promising avenues for
genomic data analysis. However, successful machine learning applications face
two key challenges. Machine learning approaches, particularly those based on
deep learning, have been successful in handling classification problems where
the label space is in the order of thousands (under an extreme classification
problem setup [7]). They require thousands of examples per class to achieve
reasonable performance. Genome classification, however, involves an enormous
number of classes, with estimates placing the number of bacterial species
between $10^{7}$ and $10^{9}$ [8]. Hence, scaling machine learning to such a
large label space requires robust representation learning [9]. Additionally,
the distribution of labeled data is highly imbalanced, i.e., not all species
are equally represented in the training and test data. This imbalance poses a
critical challenge to capturing the entire spectrum of species and hence
scaling deep learning algorithms to this important problem.

Figure 2: Overall architecture of the proposed TEPI model. We aim to learn a
common, taxonomy-aware representation space to which each species’ genome
representations can be mapped to ensure generalizable, zero-shot
classification performance.

In this study, we address these challenges through zero-shot learning with
limited labeled data. Zero-shot learning [10] aims to recognize classes that
may not have been seen during training. Given the large number of species, we
adopt a deductive reasoning approach by mapping a species’ whole genome to a
learned, compositional representation within a predefined search space for
accurate identification. Unlike brute-force search-based algorithms, our
scalable framework utilizes zero-shot learning to create taxonomy-aware,
generalizable genome representations. Figure 2 presents an overview of our
approach. During training, we learn a mapping function that projects whole
genomes (represented as “pseudo-images”) into a compositional, hierarchical
embedding space. Genome classification involves traversing the taxonomy-aware
embedding space to find the best match during inference. This zero-shot
mechanism paves the way to build general-purpose diagnostic applications that
can generalize beyond the species seen during training and move beyond
targeted pathogen recognition [11, 12].

A preliminary version of this work appears as MG-NET [12], where we proposed a
novel representation mechanism, called a pseudo-image, for genome sequences.
Building upon the success of visual computing techniques [9, 10], we
structured genomes as images using a co-occurrence-based formulation. This
paper significantly extends the original pseudo-imaging approach. Firstly, we
expand the representation to encompass entire genomes instead of single reads
from metagenomes, which are a collection of DNA sequences extracted from
clinical or environmental samples and can be composed of nucleic acid from
different organisms. Representing whole genomes is challenging since it
requires capturing different phylogenetic properties of the organism in an
expressive representation. Secondly, we introduce zero-shot learning for the
taxonomic classification of bacterial genomes through a hierarchical,
taxonomy-aware embedding space. Lastly, we extensively analyze the
representation learning capability of pseudo-imaging-based frameworks for
extreme multiclass classification.

The proposed TEPI framework offers three significant contributions: (i) we are
among the first to address the challenge of zero-shot genome recognition, (ii)
we demonstrate that constructing a taxonomy-aware, hierarchical embedding
space enables effective generalization to unseen classes with limited labeled
training data, and (iii) through a large-scale whole-genome study, we show
that the proposed TEPI framework can learn compositional representations and
generate highly relevant predictions. This paper is structured as follows.
Section II reviews prior relevant works and their relation to our proposed
techniques. Section III presents the TEPI framework, providing the necessary
background information and mathematical notations. The experimental setup,
evaluation metrics, baselines, and results are outlined in Section IV, along
with a discussion on its limitations and the opportunities in zero-shot genome
analysis. Finally, in Section V, we conclude with a brief discussion of the
approach and future research directions.

Figure 3: Iterative construction of the hierarchical taxonomy graph to capture
phylogenetic relationships between species. We iteratively add nodes based on
the species taxonomy from an empty graph to capture the inherently
compositional relationships.

##  II Related Work

Traditional bioinformatics platforms such as Kraken [13, 14], BLAST [6], Mauve
[15], and Centrifuge [16] have been widely used for genome analysis. These
algorithms involve matching nucleotide sequences to a predetermined search
space and calculating the statistical significance of the matches to infer
phylogenetic relationships between species. For instance, BLAST utilizes a
greedy algorithm to identify the best alignment between query DNA sequences
and those in the search space. The Kraken series [13, 14] employs a
lightweight algorithm to align k-mers against a pathogen database. Centrifuge
[16] utilizes metagenome-specific indexing mechanisms for fast and accurate
classification. However, these approaches are computationally expensive due to
their reliance on costly alignment steps and brute-force matching. While
recent advancements have improved inference speed [3], scalability remains an
ongoing challenge. Furthermore, these methods are limited by the user-defined
search space, which determines the best match based on alignment.

Machine learning approaches have provided valuable opportunities for analyzing
DNA sequences obtained directly from environmental samples [17]. Deep learning
models, in particular, have been extensively investigated for representation
learning in metagenome sequences, addressing various tasks such as capturing
nucleotide representations using reverse complement convolutional neural
networks (CNNs) and Long Short Term Memory networks (LSTMs) [18], predicting
metagenome sequence taxonomy with depth-wise separable convolutions [19],
predicting genomic sub-compartments and disease genes using graph
representations [20, 21], learning representations with bidirectional LSTMs
and _k_ -mer embeddings with self-attention for sequence taxonomy prediction
[22], and employing ResNet to learn metagenome representations for taxonomy
prediction [23]. Supervised learning methods like MGNet [12], GraDL [24], and
MG2V [11] have been proposed for sequence-level identification tasks. Other
approaches, such as Metagenome2Vec [25], focus on disease prediction directly
from metagenome sequences. However, these methods are trained in a supervised
manner and are designed to generalize to unseen species without specifically
addressing the challenges associated with phylogenetic classification using
whole genome sequences.

Pseudo-imaging is a mechanism of representing data from other sensing
modalities, such as ultrasound [26], as images to extract richer contextual
information that is not captured in conventional imaging. It has been
extensively employed in astrophysics [27] and medicine [28], to name a few. In
medicine, pseudo-CT estimations have been extracted from other sensors such as
MRI imaging [29] and ultrasound deformation fields [26]. In astrophysics,
spinning prism-based sensors and signal processing techniques encode star-
specific events such as explosions and are represented as images to extract
scene-specific information about the event and its location [27]. Such
representations of unstructured data as images allow us to leverage advances
in deep learning models, particularly convolutional neural networks (CNNs)
[23, 30], for extracting robust, discriminative features from pseudo-images.
Pseudo-images have yet to be explored as extensively in genome analysis,
although their application holds much promise in extracting structural
properties from sequential data. Some works have used the idea of alternative
representations using images for metagenome-based analysis for disease
classification and host phenotype prediction. For example, Self-Organizing
Maps (SOM) [31, 32] have been proposed to represent metagenomes as images,
which are then processed using CNNs for disease classification. A matrix-based
phylogenetic tree representation [33] was used as a metagenome representation
mechanism for predicting host phenotype using CNNs. However, representing the
whole genome as pseudo-images for species-level recognition and similarity
analysis has not been explored in prior literature.

Zero-shot learning, which has been extensively explored in the computer vision
[34, 35] and text processing communities [36, 37], aims to extend supervised
machine learning to handle novel, unseen classes. In this paradigm, a semantic
vector representation of target classes is learned, and inputs (e.g., images
or text) are mapped to this space for classification. However, traditional
zero-shot learning approaches assume the availability of abundant annotated
training data for the seen classes. This assumption does not hold for genome
sequences due to a significant long-tail distribution problem and an extremely
large number of target classes.

This work addresses the challenge of zero-shot learning in genome
classification, specifically in a sparsely labeled setting. We adopt the
traditional zero-shot learning pipeline but propose leveraging compositional
relationships derived from well-defined taxonomies to learn a hierarchical and
generalizable embedding space. Genome sequences can then be mapped to this
embedding space, enabling classification even for unseen classes.

|  |  |
---|---|---|---
(a) | (b) | (c) | (d)
Figure 4: Genome-level complement-aware, pseudo-images of genomes from (a)
Mycobacterium avium, (b) Mycobacterium tuberculosis, (c) Francisella
tularensis and (d) Clostridium botulinum. Note the similar patterns between
the former two images, which belong to species from the same genus
Mycobacterium.

##  III Proposed Framework: TEPI

Problem statement. This work addresses the problem of scarcely-labeled zero-
shot learning for genome classification. The objective is to learn a
generalizable embedding $\mathcal{E}_{i}$ for a given whole genome sequence
$\mathcal{G}_{i}=\\{b_{1},b_{2},b_{3},\ldots b_{m}\\}$ of a species
$s_{i}\in\mathcal{S}$, where $b_{i}\in\\{A,T,C,G\\}$ represents a nucleotide
base. Each species can be characterized by its taxonomic lineage, which
represents the hierarchical description of the organism based on shared
characteristics with other organisms. The taxonomy assigns organisms to
different levels, including life, domain, kingdom, phylum, class, order,
family, genus, species, and optionally sub-species, reflecting the
phylogenetic relationships between organisms. The aim is to learn a vector
representation that captures this hierarchy and develop a mechanism to map
whole genomes to this vector space, enabling species-level recognition with
very few labeled examples per species (${<=}5$).

Overview. The overall goal of the proposed approach (TEPI), illustrated in
Figure 1, is to learn robust features from genome data that allow for
efficient taxonomic classification with a limited number of training examples.
We propose a genome-to-taxonomy mapping function that facilitates species-
level identification from whole genome sequences. Efficient representations
are formulated for both the genome and the taxonomy to enable the learning of
this mapping. Specifically, complementary-aware pseudo-imaging is used to
represent the genome, and a taxonomy-aware embedding space is learned to
capture the compositional relationships between species based on their
taxonomic lineage. A detailed description of the proposed approach is provided
below.

###  III-A Learning a Taxonomy-aware Embedding Space

TEPI is centered around a taxonomy-aware embedding space that can be used for
species recognition based on genome sequences. This embedding space should
capture the phylogenetic properties of each species by encoding the
hierarchical relationships established by their taxonomic lineage. The higher
the similarity in the embedding space between two genomes, the more closely
related they are in the taxonomy. Capturing this property in the embedding
space is essential for effectively recognizing species outside the training
data. To achieve this, we construct a taxonomic graph
$\mathcal{T}=\\{{V},{E}\\}$, where ${V}$ represents the nodes in the taxonomy
at different levels of the hierarchy, connected by edges ${E}$. The
construction of this taxonomic graph $\mathcal{T}$ is done recursively in a
top-down manner, starting from the kingdom level and progressing to the
species level, and optionally the sub-species level if applicable. Figure 3
provides an example of how such a graph can be constructed. Traversing this
graph from one species node to another using a random walk reveals the
phylogenetic similarity between the two species. The length of the walk
reflects their similarity, while the path taken provides information on their
relationship. For instance, in Figure 3, we can infer that the representations
for Bibersteinia trehalosi and Actinobacillus lignieresii should be closer
together than to Francisella tularensis since the former two belong to the
same family (Pasteurellaceae) and can be reached within three steps. However,
all three species belong to the same class Gammaproteobacteria, and hence,
they can be reached within eight steps. Figure 5 demonstrates this property,
where the proposed embedding successfully guides taxonomy navigation and
provides highly relevant predictions for a genome whose corresponding genus
(Bibersteinia) and species (Bibersteinia trehalosi) were not observed during
training. Section IV-D provides additional discussion on this phenomenon.

Figure 5: Qualitative visualization of the predicted taxonomy tree from top-5
predictions (on the right) for a given query genome (left) for an unseen
genus. All retrieved species belong to the same family, indicating the
compositional structure of the learned embedding space. The top-1 prediction
is underlined, and the correct ones are in green.

With the constructed taxonomic graph and its inherent compositional
properties, we employ the node2vec algorithm [38] to learn a low-rank
embedding space. node2vec offers a flexible objective that preserves the
neighborhood structure for each node and captures it in the embedding space
using simulated biased random walks. This formulation captures the
phylogenetic relationships between species as described above. By
incorporating hyperparameters such as the return and in-out parameters,
node2vec combines depth-first search (DFS)-like and breadth-first search
(BFS)-like neighborhood exploration and employs negative sampling to optimize
a Skip-gram model [39]. After optimization, we obtain a set of embeddings
$\mathcal{E}_{i}$ for each target species $s_{i}\in\mathcal{S}$. These
embeddings provide taxonomy-aware, hierarchy-based representations for each
species.

Algorithm 1 Constructing a pseudo-image from a whole genome sample for a given
k-mer size $k$.

Sequence reads $X_{i}=\\{X_{0},X_{1},\ldots X_{N}\\},\forall
X_{i}\in\mathcal{X}_{i}$

Pseudo-image $I_{r}$

procedure relativeCoOccurrence($x_{i},x_{j}$, $I_{r}$)

$e_{i,j}\leftarrow I_{r}[i,j]$ $\triangleright$ Current co-occurrence
frequency

$e^{\prime}_{i,j}\leftarrow e_{i,j}+1$ $\triangleright$ New co-occurrence
frequency

$r\leftarrow\frac{e_{i,j}}{(\|e_{i,j}-e^{\prime}_{i,j}\|_{2})}$
$\triangleright$ Update co-occurrence

$e_{i,j}=2\sqrt{max(r-1,1)}+(min(r-2,2)+2)$

return $e_{i,j}$$\triangleright$ Relative co-occurrence frequency

procedure NormalizeImage($I_{r},\lambda_{m}$)

$N\leftarrow\sum_{i=0}^{4^{k}}{\sum_{j=0}^{4^{k}}{I[i,j]}}$

for $i\in 4^{k}$ do

for $j\in 4^{k}$ do

if $I_{r}[i,j]>\lambda_{m}$ then

$I_{r}[i,j]\leftarrow 255*I[i,j]/N$

else

$I_{r}[i,j]\leftarrow 0$

return $I_{r}$$\triangleright$ Normalized Pseudo-Image

$I_{r}\leftarrow ones(4^{k},4^{k})$ $\triangleright$ Initial pseudo-image

for $X_{i}\in\mathcal{X}_{i}$ do $\triangleright$ Iterate through each
sequence

for $x_{i},x_{j}\in X_{i}$ do $\triangleright$ Iterate through successive
k-mers

$I_{r}[i,j]\leftarrow$relativeCoOccurrence($x_{i},x_{j}$, $I_{r}$)

$I_{r}\leftarrow$ NormalizeImage($I_{r},\lambda_{m}$)

return $I_{r}$$\triangleright$ Final pseudo-image

###  III-B Representing Genomes as Pseudo-Images

The other key component of our framework involves finding a genome
representation that captures inter- and intra-species variations at the
nucleotide read level. We build upon the pseudo-imaging concept introduced in
our previous work, MGNet [12], to achieve this. We devise a pseudo-imaging
technique to represent genome sequences as images to capture pathogen-specific
fingerprints. To create the pseudo-image of a whole genome sequence, we
capture the relative co-occurrence between k-mers, or smaller sub-sequences of
each genome, in a histogram-based representation and create a pseudo-image,
following the success of Gray Level Co-occurrence Matrices (GLCM) [40, 41].
Figure 4 visualizes some examples of generated pseudo-images. The intensity of
each pixel represents the relative frequency with which two k-mers (or
nucleotide subsequences) co-occur in a genome, with the resulting image
forming a species-specific pattern that captures the taxonomic similarity
between species. We describe the process of building pseudo-images from
genomes in detail below.

We consider each whole genome ($\mathcal{X}_{i}$) to be a collection of
nucleotides drawn from a set of bases $\\{A,T,C,G\\}$, sequenced as “reads”
($X_{0},X_{1},\ldots,X_{n}\in\mathcal{X}_{i}$). Each sequence read is broken
down into smaller atomic sub-sequences of length $k$, called k-mers, using a
sliding window of length $k$ and stride $s$. We denote each k-mer as $x_{j}$,
where $x_{0},x_{1},\ldots,x_{l}\in X_{i}$. We compute the relative co-
occurrence between two k-mers ($x_{i}$ and $x_{j}$) by defining a function
$f_{r}(x_{i},x_{j})$ that iteratively updates their relative co-occurrence
using their observed co-occurrence across all sequence reads in the whole
genome sequence $\mathcal{X}_{i}$. Formally, we define this function as
follows.

$f_{r}(x_{i},x_{j}){=}\sigma(\frac{e_{i,j}}{(\|e_{i,j}-e^{\prime}_{i,j}\|_{2})})$
(1)

where we update the relative co-occurrence using the previously observed co-
occurrence frequency $e_{i,j}$ and the current co-occurrence frequency
$e^{\prime}_{i,j}$ between k-mers $x_{i}$ and $x_{j}$. We bound the output of
this co-occurrence formulation between -2 and 2 using a weighted update
function $\sigma(\cdot)$. In our experiments, we set
$\sigma(r)=2\sqrt{max(r-1,1)}+(min(r{-}2,2){+}2)$. The initial values of the
co-occurrence are set to be $1$ when two k-mers $x_{i}$ and $x_{j}$ are first
observed. If two k-mers never co-occur, then the frequency is $0$. Subsequent
iterations of the update function, as illustrated in Algorithm 1, increase the
relative co-occurrence value to reinforce the presence of recurring k-mer
pairs in the genome. Additionally, this formulation allows us to suppress
spurious patterns that could be a product of noise while highlighting
structures in the genome that are produced due to frequent co-occurrences of
k-mers within the entire genome sequence.

TABLE I: Zero-shot Evaluation. Accuracy is presented at different taxonomy levels for only unseen species genomes. TEPI-MGNet and TEPI-MG2V refer to genome TEPI trained with genome representations from MG-Net [12] and MG2V [11], respectively. TEPI-W2V refers to the use of word2vec embeddings.  Hierarchy-level Acc. | top-K | TEPI-W2V | TEPI-MG2V | TEPI-MGNET | TEPI-WG | TEPI-Comp
---|---|---|---|---|---|---
Species | 1 | 3.90 | 3.66 | 10.39 | 11.69 | 7.79
5 | 19.48 | 18.51 | 55.84 | 53.25 | 59.74
10 | 36.36 | 34.18 | 66.23 | 62.34 | 67.53
| 20 | 42.86 | 36.43 | 72.73 | 74.03 | 70.13
Genus | 1 | 44.16 | 37.09 | 46.75 | 48.05 | 49.35
5 | 53.25 | 42.60 | 59.74 | 58.44 | 64.94
10 | 59.74 | 54.36 | 68.83 | 63.64 | 67.53
| 20 | 67.53 | 67.53 | 72.73 | 74.03 | 70.13
Family | 1 | 54.55 | 49.64 | 62.34 | 62.34 | 57.14
5 | 64.94 | 61.69 | 72.73 | 68.83 | 70.13
10 | 67.53 | 56.05 | 75.32 | 72.73 | 76.62
| 20 | 79.22 | 64.96 | 77.92 | 79.22 | 81.82
Order | 1 | 59.74 | 54.36 | 70.13 | 71.43 | 63.64
5 | 74.03 | 73.29 | 81.82 | 79.22 | 80.52
10 | 77.92 | 69.35 | 85.71 | 85.71 | 83.12
| 20 | 84.42 | 72.60 | 88.31 | 90.91 | 85.71

We construct the pseudo-image $I_{r}$ by considering each pixel $p_{i,j}\in
I_{r}$ to represent the relative co-occurrence of k-mers $x_{i}$ and $x_{j}$.
Algorithm 1 provides a pseudocode for the pseudo-image construction process.
This formulation allows us to represent the whole genome as an image and hence
will enable us to capture long-range properties that have proven to be hard
for sequence-based learning models such as LSTMs [42] and Transformers [43].
Additionally, it allows us to leverage the advances in computer vision, such
as convolutional neural networks (CNNs) [23], that can effectively capture the
spatial relationships for effective representation learning. The resulting
pseudo-image representation is given by

$I_{r}(i,j)=\sum_{i=1}^{4^{k}}\sum_{j=i+s}^{4^{k}}\begin{cases}255*f_{r}(i,j)/N,&\text{if
}f_{r}(x_{i},x_{j})>\lambda_{m}\\\ 0,&\text{otherwise}\end{cases}$ (2)

where the pseudo-image is constructed by iterating over all possible k-mer
combinations for a given window length $k$ and stride $s$. Each pixel
$I_{r}(i,j)$ is assigned the output of $f_{r}(x_{i},x_{j})$ from Equation 1.
The co-occurrences are normalized to be between $0$ and $1$ using $N$, which
is the sum of all k-mer co-occurrences from all sequences in the genome; A
cutoff parameter, $\lambda_{m}$, is introduced to remove any spurious patterns
that can be introduced due to sequencing errors [44] which can flip base pairs
randomly. We simulate an RGB image to leverage standard CNN architectures [23,
30] for image processing by duplicating each pixel value to create an image of
depth $3$. We set $\lambda_{m}=0$ in our experiments.

The original MGNet [12] formulation was done at a sequence level for
metagenomes, where the pseudo-image was constructed for each sequence read.
While useful in metagenomes (where each sequence can belong to a different
species), this formulation does not consider global correlations between
k-mers, essential in whole genome representations. For genome-level modeling,
we explore three strategies for capturing the phylogenetic properties using
pseudo-images. The first strategy, called TEPI-MGNet, constructs a pseudo-
image for each sequence read in the genome and then combines them by summing
the pixel values individually. The resulting image is normalized by the sum of
all genome-level k-mer co-occurrences, scaling each pixel value between 0 and
1. The second strategy, known as TEPI-WG, involves concatenating all sequence
reads into a single, genome-level sequence. A pseudo-image is then constructed
for each genome sample using this concatenated sequence. The third strategy,
named TEPI-Comp, extends TEPI-MGNet by considering both the sequence read and
its reverse complement to capture the symmetry inherent in DNA structures.
This strategy allows for additional cues in capturing phylogenetic variations
at the genome level. Some examples of the pseudo-images generated using TEPI-
Comp are shown in Figure 4, demonstrating the distinctive species-specific
global patterns represented in the pseudo-images. Empirically, in Section IV,
it is found that incorporating the reverse complement improves the performance
of TEPI-Comp for unseen pathogens, with higher genome similarity of the
predicted and actual species (Table IV).

TABLE II: Generalized zero-shot evaluation. Accuracy is presented at different levels of the taxonomy.  All the approaches are evaluated on genomes from both seen and unseen species for generalized genome recognition. Hierarchy-level Acc. | top-K | TEPI-W2V | TEPI-MG2V | TEPI-MGNET | TEPI-WG | TEPI-Comp
---|---|---|---|---|---|---
Species | 1 | 49.74 | 38.80 | 51.29 | 51.29 | 67.78
5 | 72.94 | 68.56 | 90.21 | 86.86 | 92.01
10 | 80.41 | 63.53 | 92.27 | 91.75 | 93.56
20 | 84.54 | 73.55 | 94.07 | 94.59 | 94.07
Genus | 1 | 72.94 | 68.56 | 83.51 | 80.93 | 89.95
5 | 86.60 | 84.87 | 90.98 | 87.89 | 93.04
10 | 89.95 | 87.25 | 92.78 | 91.75 | 93.56
20 | 92.53 | 90.68 | 94.33 | 94.59 | 94.07
Family | 1 | 78.61 | 63.67 | 89.43 | 86.34 | 91.49
5 | 90.72 | 80.74 | 94.33 | 90.46 | 94.07
10 | 92.78 | 74.23 | 94.85 | 94.07 | 95.36
20 | 95.36 | 74.38 | 95.36 | 95.62 | 96.39
Order | 1 | 81.19 | 65.76 | 91.24 | 88.40 | 92.78
5 | 92.78 | 73.30 | 96.13 | 92.78 | 96.13
10 | 95.36 | 87.73 | 96.91 | 96.65 | 96.65
20 | 96.91 | 76.56 | 97.42 | 97.94 | 97.16

###  III-C Genome-to-Taxonomy Mapping

Given the two representations, i.e., genome-level pseudo-images to capture
phylogenetic variations at the nucleotide level and the taxonomy-aware,
hierarchical embedding space, we need to find a mapping function
$\phi:I(\mathcal{G}_{i})\rightarrow\mathcal{E}_{i}$ that can map from the
genome-level pseudo-image $I(\mathcal{G}_{i})$ of each species
$s_{i}\in\mathcal{S}$ to its corresponding taxonomic embedding
$\mathcal{E}_{i}$, such that the genome representations possess the same
compositional properties. While more complicated mechanisms, such as
attention-based reasoning [12] and vision transformers [45], can be used, we
use convolutional neural networks (CNNs) [30, 23] to learn our mapping
function $\phi(\cdot)$ for two primary reasons. First, CNNs have been shown to
require significantly less training data to learn robust representations
compared to vision transformers. Second, they are faster to train due to their
inherent inductive biases to capture spatially relevant features. Empirically,
we find CNNs to perform better in our ablation studies (Section IV-E). In this
work, we use a 10-layer CNN network inspired by VGG-16 [30], where 5 blocks of
2 CNN layers each are interspersed with a max-pooling operation. A global
average pooling layer is added at the end of the last convolutional block,
followed by three fully connected layers to project down to the same dimension
as the species-specific embedding. The network is trained to predict the
taxonomy-aware embedding and capture the inter- and intra-class variations
encoded within the taxonomy-aware embedding. We formulate the mapping function
as a regression function from the pseudo-images $I(\mathcal{G}_{i})$ to the
embedding space $\mathcal{E}_{i}$. Hence, the training is an optimization for

$\mathcal{L}=\min_{\mathbf{W}_{\phi}}||\mathbf{W}_{\phi}I(\mathcal{G}_{i})-\mathcal{E}_{i}||^{2}_{d}$
(3)

where $\mathbf{W}_{\phi}$ is the set of learnable weights for the mapping
function $\phi(\cdot)$ and $\mathcal{E}_{i}$ is a real-valued species-level
$d$-dimension embedding. To aid in the computation, we add an L2
regularization layer to both the learned embedding $\mathcal{E}_{i}$ and the
output of the mapping function $\phi(\cdot)$. This formulation, while simple,
has several advantages. First, we offload the task of capturing
phylogenetically relevant features to learn the embedding space. Hence, during
training, we do not need to compute expensive mining of hard negative (or
positive) pairs. Second, by formulating the mapping function as regressing to
the hierarchical taxonomic embedding space, we can expand the vocabulary of
the unseen classes by adding the relevant embedding during inference. Finally,
since the target embedding space is computed independently and fixed when
training the mapping function, there is no need for additional supervision,
such as in center loss [46].

###  III-D Implementation Details

To learn the taxonomy-aware embedding, we use the $72,378$ species from the
“bacteria” subset from NCBI Taxonomy database [47] as our embedding search
space to build our taxonomy tree to capture the compositional, phylogenetic
relationships among all bacterial species. We generate 10 random walks for
each species, with a maximum length of 100 visited nodes in each walk to
generate the context. Having longer random walks allows us to capture the
similarity in neighborhood structure for both highly related (such as from the
same genus) and distantly related (i.e., from a different family) species. We
set the initial learning rate to be $4\times 10^{-5}$, and we first perform a
cold start, i.e., for five epochs, the learning rate is set to be $4\times
10^{-12}$ and then increased. We use a k-mer of size 6 and stride of 10 to
build the pseudo-images and resize the image to $512\times 512$. All
hyperparameters are found using a grid search and kept constant for all
experiments. All networks are trained for 500 epochs with early stopping.

##  IV Experimental Evaluation

In this section, we present the results from the experimental evaluation of
the proposed approach. We begin with a description of the data, followed by a
discussion on the various evaluation settings and considered baselines for
comparison. We follow with the presentation of the quantitative and
qualitative results in various evaluation settings.

###  IV-A Data Collection

Data Collection. For our evaluation, we primarily focus on the bacterial
genomes associated with common veterinary diseases, whose recognition is vital
in diagnosing diseases that can yield severe economic distress. Table III
summarizes the dataset statistics. Specifically, we choose $93$ species, split
into seen ($58$ species) and unseen ($35$ species). We downloaded complete
genomes for each species from the NCBI RefSeq [48] database, a publicly
available database of annotated genomic sequences. Each genome is annotated
using NCBI’s prokaryotic genome annotation pipeline [49]. We obtained a
maximum of $10$ genomes per species from the RefSeq database and split them
into train and test sets. If a species has at least ten genomes, five were
chosen for evaluation as part of the seen group of species, and the remaining
five were used for training. Species with less than ten genomes were used as
part of the unseen classes for testing. To ensure proper coverage, we
considered species with different phylogenetic characteristics such as gram
stain (positive vs. negative), shape (Cocci vs. Bacilli vs. Spirilla), and
oxygen dependency (aerobic vs. anaerobic), among others.

TABLE III: Dataset Statistics. We cover a wide range of seen and unseen
genomes with coverage at different levels of the hierarchy.

Split | Class | Order | Family | Genus | Species | # Train/Test
---|---|---|---|---|---|---
Seen | 15 | 25 | 36 | 52 | 58 | 290/290
Unseen | 11 | 16 | 17 | 19 | 35 | -/143

Figure 6: Qualitative visualization of the predicted taxonomy tree from top-5
predictions (on the right) for a given query genome (left) for an unseen sub-
species. The top-1 prediction is underlined, while the correct predictions are
in green. Note: Francisella tularensis was also part of the top-10
predictions, along with the two sub-species, while subsp. novicida was unseen
during training.

###  IV-B Evaluation Settings and Baseline Approaches

Evaluation settings. To evaluate the generalization capabilities of the
approach, we adopt two settings following prior zero-shot learning works [37,
35]. In the first setting, i.e., zero-shot evaluation, the approaches were
trained on a set of seen classes whose reference genomes and labels are
available. During the evaluation phase, the frameworks are presented with
genomes from unseen classes, i.e., the genomes are not part of the training
process. The goal is to classify the genome into one of the unseen species. In
the generalized zero-shot setting, genomes from both seen and unseen species
are presented, and the goal is to recognize the species corresponding to each
genome correctly. The accuracy for top-K predictions is used to quantify the
performance. Due to the large search space and limited labeled data, we
evaluate at $K=\\{1,5,10\\}$. Additionally, for a fair comparison, we use a
standard k-Nearest neighbor classifier from Sci-Kit Learn [50], trained on the
embedding space with a neighborhood of 20.

Baselines. Since this is the first attempt at zero-shot genome classification,
we propose a variety of baselines to compare against the proposed framework.
First, in TEPI-W2V, we train a word2vec model [39] on simple chains of
taxonomic lineages for each species to obtain the latent embedding space and
use the proposed genome-wide pseudo-imaging as the genome representation. We
pursue state-of-the-art deep learning models based on transformers [43] as
proposed in MG2V [11] for learning representations from genome sequences.
Finally, we use different variations of genome-wide pseudo-imaging proposed in
Section III-B. Considered together, these baselines provide a first look at
how deep learning models can be used to tackle zero-shot genome recognition.

###  IV-C Quantitative Evaluation

Zero-shot Evaluation. We first present the results of the zero-shot evaluation
in Table I. We evaluate the ability of the approaches to recognize the query
genome at different levels of the hierarchy, i.e., at the species, genus,
family, and order raGENOMEnks of the taxonomy. While we are more interested in
the species level accuracy, the accuracy at the other levels provides a
measure of how well the proposed embedding space (Section III-A) captures the
taxonomic hierarchy. As can be seen, the approaches with the proposed TEPI
embedding space significantly outperform the word2vec baseline (TEPI-W2V) at
all accuracy metrics, indicating that naïve taxonomic embeddings do not
generalize well to unseen species. Similarly, other genome representations,
such as MG2V, perform poorly while performing better than the word2vec model,
indicating that the local contexts from the sequence level do not scale to
genome-wide representations. Interestingly, the complement-aware pseudo-
imaging has poor top-1 accuracy at the species level but improves dramatically
at the genus and family levels. We also find that although the performance of
all approaches is similar at the higher levels of the hierarchy, such as order
and family, TEPI-MGNET with the composite MGNET representations does better.
Beyond the order level of the taxonomy, i.e., at the class, phyla, and kingdom
levels, the performance of all TEPI-based models reaches $100\%$ at $K{=}5$,
indicating that the learned embedding captures the compositional relationship
between the species.

Note that MG2V and MG-NET are supervised learning frameworks that take several
hours (almost 20 for MG2V and 10 for MG-NET) of pre-training and 4 hours of
fine-tuning to learn robust features and do not generalize to unseen genomes
since the predictions are limited to those seen during training. Our
modification to MG2V, TEPI-MG2V, allows us to scale the framework to unseen
genomes, although it does struggle with learning long-range dependencies found
in whole genome sequences. Using metadata such as structural features used in
MG2V and MG-NET could potentially improve the performance of TEPI. Similarly,
integrating the taxonomy-aware embedding into supervised learning using
semantic codes or knowledge-infused learning techniques can help machine
learning-based models generalize beyond a limited vocabulary. Section IV-D
discusses some interesting success and failure cases of TEPI and comparative
frameworks.

TABLE IV: Whole Genome similarity based on BLAST [6] for the top-1 predicted species for seen and unseen query genomes. * indicates ($p{>}0.05$) Approach | Genome Similarity | Spearman
---|---|---
Seen | Unseen | Overall | Coefficient
TEPI-W2V | 77.93 | 78.51 | 78.04 | 14.25
TEPI-MG2V | 79.91 | 79.31 | 79.79 |  10.26*
TEPI-MGNET | 97.18 | 87.58 | 95.27 | 77.55
TEPI-WG | 97.92 | 87.43 | 95.89 | 73.95
TEPI-Comp | 99.59 | 86.97 | 97.04 | 57.47

Generalized Zero-shot Evaluation Table II summarizes the performance of the
considered baselines in the generalized zero-shot learning setting, where the
query genome at test time can come from both seen and unseen species. It is
interesting to note that TEPI-Comp, the complement-aware genome representation
trained with the proposed taxonomy-aware embedding space, significantly
outperforms all baselines. We attribute it to the fact that it obtains
$82.7\%$ top-1 accuracy at the species level and $100\%$ top-1 accuracy at all
other taxonomy levels for seen classes, even when training with only $5$
reference genomes per class. TEPI-WG and TEPI-MGNET achieve $61.5\%$ and
$61.5\%$ top-1 accuracy at the species level and an average of $90.7\%$ and
$95.1\%$ top-1 accuracy at other levels, respectively. We hypothesize that the
reverse complement representation carries distinguishing characteristics that
allow easier translation from the genome to the taxonomy-aware embedding space
for genomes from seen classes. We find that the word2vec embedding space works
better in the generalized zero-shot setting than the MG2V representations but
does not outperform the pseudo-image-based approaches. We again observe the
performance saturation for all approaches at the family and order levels for
top-5 and top-10 predictions, indicating that the taxonomy is preserved in the
embedding space.

Improved Latency. We also compared the computational efficiency of using the
proposed TEPI framework for zero-shot genome recognition. B. We observe that,
on average, the time taken to compare the query genome with other reference
genomes is lower for TEPI than BLAST. This difference is more apparent as the
number of reference genomes for comparison increases. In TEPI, the significant
cost is the construction of the pseudo-image, which takes 3 seconds per
genome. The cost for extracting features is 300 ms on an NVIDIA RTX 3060 GPU,
and the kNN-based search takes approximately 800 ms using Sci-Kit Learn for
searching over 1000 candidate genomes. The total execution time per query is,
on average, 8 seconds, averaged over 100 runs. For BLAST, the gold standard
bioinformatics platform, searching over 1000 candidate genomes takes around 25
minutes (averaged over 100 runs) on a 32-core AMD ThreadRipper CPU. Similarly,
the closely related MG2V, titled TEPI-MG2V, takes 10 seconds to extract
features per query and around 5 minutes to search over 1000 candidate genomes.

###  IV-D Qualitative Analysis.

In addition to the quantitative analysis, we present some qualitative results
to dissect the performance of the proposed TEPI approach. Specifically, we
present two cases - (i) genome classification at the unseen sub-species level
and (ii) classification of a genome from an unseen genus and species. Figure 6
presents a query genome from Francisella tularensis subsp. novicida, whose
taxonomy is shown on the left. The consolidated taxonomy tree from the top-5
predictions made by TEPI is shown on the right. The correct and incorrect
classifications at each level are highlighted in green and red, respectively,
whereas the top-1 prediction is underlined. Although the subspecies novicida
is unseen, the approach was able to retrieve very relevant neighbors (top 3
predictions were Francisella tularensis subsp. novicida, Francisella
tularensis and Francisella tularensis subsp. holarctica) while placing the
correct subspecies at the top-1 prediction. This performance is remarkable
considering that the other subspecies holarctica was part of the training set
yet was not the top prediction. This result indicates that subtle variations
at both genome and taxonomy levels are captured in TEPI. Similarly, in Figure
5, TEPI was able to retrieve highly relevant species to a genome query from an
unseen genus (Biberstinia). Interestingly, all top-5 predictions were placed
within the same family Pasteurellaceae of the target species, with the correct
prediction at the third position on the prediction order.

Genome Similarity. To qualitatively analyze the predictions of the approach,
we use BLAST [6] to align and compute whole genome similarity between the
predicted and actual species during inference. Table IV summarizes the
results. It can be seen that the genome similarity between the predictions of
the pseudo-image-based approaches is significantly better than that from MG2V.
Similarly, the word2vec-based model performs considerably worse than the
proposed taxonomy-aware embedding. TEPI-Comp’s predictions have significantly
higher genome similarity for seen classes, whereas, for unseen, TEPI-MGNET
performs better. From these results, we can see that even though the top-1
performance of the TEPI-based approaches can be lower at the species level
($11.69$ for TEPI-WG), the genome similarity of the top-1 is more than $87\%$,
which indicates that the pseudo-imaging and taxonomy-based embedding functions
capture the underlying relationships from the genome sequences, which is our
primary focus.

We also evaluate whether the distance between the predicted embeddings for two
genomes can serve as a proxy for BLAST-based similarity measures, which can be
costly due to expensive alignment steps. We take the dot product between the
embedding of the predicted label and the actual label under the generalized
zero-shot setting and compute Spearman’s rank correlation coefficient to
ascertain its correlation with BLAST-based genome similarity scores. A higher
correlation indicates better alignment between actual genome similarity and
the model’s notion of similarity. As seen from Table IV, TEPI-MGNET has the
highest correlation, followed by TEPI-WG. Interestingly, TEPI-Comp, which has
the highest performance, has a poor correlation with the BLAST-based
similarity scores. All numbers reported are statistically significant
($p{<}0.05$), except TEPI-MG2V, which has poor performance but is not
statistically significant ($p{=}0.219$). The word2vec-based embedding model
(TEPI-W2V) does poorly on this as well, indicating that it does not capture
the genome-level similarity effectively in its embedding function. Note that
this distance is computed based on the mapping function, defined in Section
III-C, and not on the pseudo-image itself. In ablation studies (Section IV-E),
we see that using a supervised contrastive learning loss [51] in addition to
the loss defined in Equation 3 without hard negative mining improves the
genome similarity of predicted classes. Contrastive learning [52] with a
nuanced negative mining scheme could potentially improve this performance.
Additionally, exploiting the structural properties encoded in the pseudo-image
using self-supervised representation learning [9] could provide a better
correlation with genome similarity.

###  IV-E Ablation Studies.

TABLE V: Ablation studies to evaluate the effect of genome encoder, embedding
functions, and using a contrastive learning loss.

Genome | Taxonomy | Contrastive | Species | Unseen Genome
---|---|---|---|---
Encoder | Embedding | Learning? | Unseen | Seen | Similarity
MG-Net [12] | TEPI-Comp | ✗ | 7.79 | 61.42 | 86.97
MG-Net [12] | TEPI-MGNET | ✗ | 10.39 | 51.29 | 87.58
MG-Net [12] | TEPI-Comp | ✓ | 6.09 | 83.62 | 86.67
MG-Net [12] | TEPI-MGNET | ✓ | 5.19 | 62.38 | 87.05
ResNet [23] | TEPI-Comp | ✗ | 8.09 | 62.01 | 87.09
ViT [45] | TEPI-Comp | ✗ | 7.93 | 83.75 | 86.99

In addition to quantitative and qualitative evaluation, we systematically
analyze the effect of TEPI’s components on its performance. Table V summarizes
the results. First, we evaluate the impact of using different visual encoder
architectures for genome-to-taxonomy mapping (Section III-C). In our primary
experiments (Section IV-C), we used the VGG-style encoder proposed in MG-Net
[12] for fair comparison with prior approaches. However, research in the
computer vision community has resulted in more nuanced architectures such as
the ResNet [23], a convolutional neural network that has been successfully
scaled to a larger, more efficient architecture, and the vision transformer
(ViT) [45], an alternative to CNN-based architectures that uses transformers
[43] to learn robust features from images. Using ResNet and ViT results in
marginal improvements over the simpler VGG-style encoders. We attribute this
improvement to the features learned from the large-scale pre-training on
natural image data. However, we do not observe considerable improvements,
possibly due to the out-of-domain nature of pseudo-imaging compared to the
natural images found in ImageNet. Large-scale pre-training can help improve
the performance of larger vision encoders on genome-based pseudo-images.

In addition to the mapping loss proposed in Equation 3, we experiment with
adding a supervised contrastive learning [51] loss to encourage the network to
learn more discriminative features for each species. This loss function is a
form of metric learning [53] where the goal is to reorganize the latent space
by forcing the representations from similar inputs together and pushing inputs
from other classes away. We see that the addition of this contrastive learning
loss significantly affects TEPI’s performance on species seen during training,
providing gains of almost $20\%$ on TEPI-Comp and $10\%$ on TEPI-MGNET.
However, the performance on unseen classes, i.e., the generalization to novel
species, takes a severe hit ($5\%$ on unseen classes and $2\%$ on seen
classes). This effect could be due to the fact that supervised contrastive
loss uses multiple positive samples and does not use hard negatives to
reorganize the latent space. The performance could possibly be improved with a
more nuanced selection of negative examples for contrastive learning by
leveraging the highly complex similarity measures across species.

###  IV-F Limitations and Opportunities

While the proposed TEPI framework does well on zero-shot and generalized zero-
shot recognition of whole genomes (Section IV-C) and provides interesting
insights on capturing the genome similarity (Section IV-D) using pseudo-
imaging, we observe that there are some limitations that can be the source of
future work in zero-shot genome classification. First, our experiments with
contrastive learning (Section IV-E) yielded interesting results that emerge
when adding a metric learning loss to TEPI. While the performance did not
improve much on unseen genomes, the nature of failures provides critical
insights into how the framework could benefit from metric learning.
Specifically, we observe that the accuracy at the genus level is improved on
unseen genomes, indicating that the clustering occurs at higher levels of the
taxonomy. Hence, introducing a hard negative sampling at the fine-grained
level can lead to better performance. Second, the pseudo-image formalism
offers a new way of representing genomes but suffers from the limitation that
the image captures patterns at a single stride length. Extending pseudo-
imaging to handle multiple stride lengths could lead to more robust features
that can yield better performance on taxonomic profiling. Finally, TEPI is
currently designed to work with whole genomes, whereas critical diagnostic
applications could benefit from performance on 16S and 23S gene sequencing
reads and raw metagenome sequences. Whole genome sequences generally possess
longer, more extensive sequences than 16S/23S reads and metagenome sequences,
yielding richer context in pseudo-imaging. Metagenome sequences and 16S/23S
sequences have shorter context windows that can require re-formulation of the
pseudo-image representation to enable its functionality.

TEPI’s robust representation learning paradigm and generalization capabilities
offer several opportunities for integration into bioinformatics pipelines such
as BLAST. For example, we can envision TEPI as an initial, coarse filter to
reduce BLAST’s search space and overall latency in taxonomic profiling.
Section IV-D shows that the computational overhead is significantly lower for
TEPI compared to BLAST and other machine learning models such as MG2V. Given
that top-10 genus-level prediction for seen classes is more than $90\%$, using
TEPI as a filter to narrow down BLAST’s search space could considerably reduce
the latency for taxonomic profiling while improving performance. While we only
explored the taxonomic classification of whole genome sequences, we envision
TEPI as a learning paradigm for capturing semantically relevant features for
downstream tasks such as disease and pathogen characterization tasks such as
predicting potential drug resistance, pathogenic potential, and disease
progression. For example, disease characterization models such as
Metagenome2Vec [25, 31] could benefit from the recognition of pathogens
present in the genome sequences and result in more nuanced prediction of
disease outcomes from raw genome data. Our future works are focused on
capturing the genome-level similarities between species for more robust
recognition and zero-shot transfer.

##  V Conclusion and Future Work

In this work, we presented TEPI, one of the first works to address the problem
of zero-shot genome recognition from extremely scarce data. Using a
hierarchical graph structure extracted from the species taxonomy, the proposed
embedding space provides a compositional representation that can efficiently
be used for zero-shot recognition of whole genomes. An image-based
representation of the whole genome captures subtle intra- and inter-species
variations that can be translated to the learned embedding space in a simple
yet highly effective framework with only five labeled examples per species. We
aim to extend TEPI to handle zero-shot recognition of 16S and 23S rRNA gene
sequencing, which are easier to sequence and more readily available compared
to whole genome sequences, to drive the development of the next generation of
point-of-care diagnostic platforms.

## Acknowledgements.

This research was supported in part by the US Department of Agriculture (USDA)
grants AP20VSD and B000C011. We also thank Dr. Arunkumar Bagavathi, Dr. Sai
Narayanan, and Mr. Udhav Ramachandran for thoughtful discussion on pseudo-
imaging for zero-shot taxonomic profiling.

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Sathyanarayanan N. Aakur is an Assistant Professor at the Department of
Computer Science and Software Engineering at Auburn University. He received
his B.Eng. degree in Electronics and Communication Engineering from Anna
University, Chennai, India, in 2013. He received an M.S. in Management
Information Systems and a Ph.D. in Computer Science and Engineering from the
University of South Florida, Tampa, in 2015 and 2019, respectively. He
received the prestigious National Science Foundation CAREER award in 2022 and
has been Associate Editor for IEEE Robotics and Automation Letters since 2021.
His research interests lie in neuro-symbolic reasoning for open-world visual
understanding and deep learning applications for computational biology.
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Vishalini Ramnath is the Assistant Director of Undergraduate Programs at the
Department of Computer Science and Software Engineering at Auburn University.
She received her B.Eng. degree in Electronics and Communication Engineering
from Anna University, Chennai, India 2013. She received an M.S. in Computer
Science from the University of Illinois, Chicago, and a Ph.D. in Computer
Science and Engineering from the University of South Florida, Tampa, in 2016
and 2020, respectively. Her research interests include machine learning for
the Internet of Things and deep learning applications for genomics.
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Priyadharsini Ramamurthy is currently pursuing her Ph.D. degree in Computer
Science from Oklahoma State University. She received a B.Eng. degree in
Computer Science from Anna University, Chennai, India, in 2006. She received
an M.S. degree in Engineering Management from International Technological
University. Her research interests include Natural Language Understanding and
deep learning applications.
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Akhilesh Ramachandran is an Associate Professor with the Department of
Veterinary Pathobiology at Oklahoma State University. He also serves as the
Head of the Microbiology and Molecular Diagnostics Sections at the Oklahoma
Animal Disease Diagnostic Laboratory. He received the B.V.Sc. & A.H. degree
from Kerala Agricultural University, Kerala, India, in 1998. He received a
Ph.D. in Veterinary Biomedical Sciences in 2003 from Oklahoma State
University. His research interests include the development of novel molecular
diagnostic protocols and platforms for infectious disease diagnosis.
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