Expression Data
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2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Gene Regulatory Networks (GRNs) are the pioneering methodology for finding new gene interactions getting insights of the biological processes using time series gene expression data. It remains a challenge to study the temporal nature of gene expression data that mimic complex non-linear dynamics of the network. In this paper, an intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data. A novel PSO algorithm enhanced by human cognition influenced by the ideology of Bhagavad Gita is employed for improved learning of RNN. RNN guided by the proposed algorithm simulates the nonlinear and dynamic gene interactions to a greater extent. The proposed method shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks. The strength of the method is verified by analyzing the small artificial network and real data of Escherichia coli with improved accuracy.

2021 ◽  
Xiangyu Liu ◽  
Zhengchang Su ◽  
Guojun Li

Abstract Background: Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, existing algorithms are limited to finding either broad or narrow biclusters but both due to failure of balancing between effectiveness and efficiency. Methods: We developed a new algorithm ARBic which can accurately identify any meaningful biclusters of shape no matter broad or narrow in a large scale gene expression data matrix, even when the values in the biclusters to be identified have the same distribution as that the background data has. ARBic is developed by integrating column-based and row-based strategies into biclustering procedure. The column-based strategy borrowed from ReBic, a recently published biclustering tool, prefers to narrow bicluters. The row-based strategy newly designed in this article by repeatedly finding a longest path in a specific directed graph prefers to broader ones. Result and Conclusion: When tested and compared to other seven salient biclustering algorithms on simulated datasets, ARBic achieved recovery, relevance and f1-scores 29% higher than the second best algorithm. Furthermore, ARBic substantially outperforms all of them on real datasets and robusts to noises, shapes of biclusters and types of datasets.Code:

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258326
Wen Bo Liu ◽  
Sheng Nan Liang ◽  
Xi Wen Qin

Gene expression data has the characteristics of high dimensionality and a small sample size and contains a large number of redundant genes unrelated to a disease. The direct application of machine learning to classify this type of data will not only incur a great time cost but will also sometimes fail to improved classification performance. To counter this problem, this paper proposes a dimension-reduction algorithm based on weighted kernel principal component analysis (WKPCA), constructs kernel function weights according to kernel matrix eigenvalues, and combines multiple kernel functions to reduce the feature dimensions. To further improve the dimensional reduction efficiency of WKPCA, t-class kernel functions are constructed, and corresponding theoretical proofs are given. Moreover, the cumulative optimal performance rate is constructed to measure the overall performance of WKPCA combined with machine learning algorithms. Naive Bayes, K-nearest neighbour, random forest, iterative random forest and support vector machine approaches are used in classifiers to analyse 6 real gene expression dataset. Compared with the all-variable model, linear principal component dimension reduction and single kernel function dimension reduction, the results show that the classification performance of the 5 machine learning methods mentioned above can be improved effectively by WKPCA dimension reduction.

Abhirup Shaw ◽  
Beáta B. Tóth ◽  
Róbert Király ◽  
Rini Arianti ◽  
István Csomós ◽  

Thermogenic brown and beige adipocytes might open up new strategies in combating obesity. Recent studies in rodents and humans have indicated that these adipocytes release cytokines, termed “batokines”. Irisin was discovered as a polypeptide regulator of beige adipocytes released by myocytes, primarily during exercise. We performed global RNA sequencing on adipocytes derived from human subcutaneous and deep-neck precursors, which were differentiated in the presence or absence of irisin. Irisin did not exert an effect on the expression of characteristic thermogenic genes, while upregulated genes belonging to various cytokine signaling pathways. Out of the several upregulated cytokines, CXCL1, the highest upregulated, was released throughout the entire differentiation period, and predominantly by differentiated adipocytes. Deep-neck area tissue biopsies also showed a significant release of CXCL1 during 24 h irisin treatment. Gene expression data indicated upregulation of the NFκB pathway upon irisin treatment, which was validated by an increase of p50 and decrease of IκBα protein level, respectively. Continuous blocking of the NFκB pathway, using a cell permeable inhibitor of NFκB nuclear translocation, significantly reduced CXCL1 release. The released CXCL1 exerted a positive effect on the adhesion of endothelial cells. Together, our findings demonstrate that irisin stimulates the release of a novel adipokine, CXCL1, via upregulation of NFκB pathway in neck area derived adipocytes, which might play an important role in improving tissue vascularization.

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5052
Eva Coll-de la Rubia ◽  
Elena Martinez-Garcia ◽  
Gunnar Dittmar ◽  
Petr V. Nazarov ◽  
Vicente Bebia ◽  

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

2021 ◽  
Teresa Shippy ◽  
Prashant S Hosmani ◽  
Mirella Flores-Gonzalez ◽  
Lukas A Mueller ◽  
Wayne B Hunter ◽  

Hox genes and their cofactors are essential developmental genes that specify regional identity in animals, including insects. A particularly interesting feature of Hox genes is their conserved arrangement in clusters in the same order in which they specify identity along the anterior-posterior axis. Among insects, breaks in the cluster have been reported in a few species, but these seem to be the exception rather than the rule. We have annotated the ten Hox genes of the Asian citrus psyllid, Diaphorina citri, and determined that there is a split in its Hox cluster between the Deformed and Sex combs reduced genes. This is the first time a break at this position has been observed in an insect Hox cluster. We have also annotated the D. citri orthologs of the Hox cofactor genes homothorax, PKNOX and extradenticle. Interestingly, we found an additional copy of extradenticle in D. citri that appears to be a retrogene. Expression data and sequence conservation suggest that the extradenticle retrogene may have retained the original extradenticle function and allowed the parental extradenticle gene to diverge.

2021 ◽  
pp. 096366252110496
Fotini Bonoti ◽  
Vasilia Christidou ◽  
Penelope Papadopoulou

The present study aimed to examine children’s conceptions of coronavirus as denoted in their verbal descriptions and drawings and whether these vary as a function of children’s age and the mode of expression. Data were collected in Greece during spring 2020 and 344 children aged 4 to 10 years were first asked to verbally describe coronavirus and then to produce a drawing of it. Content analysis of data revealed the following main themes: (a) Coronavirus, (b) Medical, (c) Psychological, and (d) Social. Results showed that children from an early age present a remarkable level of understanding of coronavirus and the COVID-19 disease as a multidimensional construct, which can be designated not only through characteristics of the Sars-Cov-2 but also through its medical, social, and psychological consequences on people’s lives. Moreover, children were found to emphasize different aspects of this construct depending on their age and the mode of expression.

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Maria Moksnes Bjaanæs ◽  
Gro Nilsen ◽  
Ann Rita Halvorsen ◽  
Hege G. Russnes ◽  
Steinar Solberg ◽  

Abstract Background Genetic alterations are common in non-small cell lung cancer (NSCLC), and DNA mutations and translocations are targets for therapy. Copy number aberrations occur frequently in NSCLC tumors and may influence gene expression and further alter signaling pathways. In this study we aimed to characterize the genomic architecture of NSCLC tumors and to identify genomic differences between tumors stratified by histology and mutation status. Furthermore, we sought to integrate DNA copy number data with mRNA expression to find genes with expression putatively regulated by copy number aberrations and the oncogenic pathways associated with these affected genes. Methods Copy number data were obtained from 190 resected early-stage NSCLC tumors and gene expression data were available from 113 of the adenocarcinomas. Clinical and histopathological data were known, and EGFR-, KRAS- and TP53 mutation status was determined. Allele-specific copy number profiles were calculated using ASCAT, and regional copy number aberration were subsequently obtained and analyzed jointly with the gene expression data. Results The NSCLC tumors tissue displayed overall complex DNA copy number profiles with numerous recurrent aberrations. Despite histological differences, tissue samples from squamous cell carcinomas and adenocarcinomas had remarkably similar copy number patterns. The TP53-mutated lung adenocarcinomas displayed a highly aberrant genome, with significantly altered copy number profiles including gains, losses and focal complex events. The EGFR-mutant lung adenocarcinomas had specific arm-wise aberrations particularly at chromosome7p and 9q. A large number of genes displayed correlation between copy number and expression level, and the PI(3)K-mTOR pathway was highly enriched for such genes. Conclusions The genomic architecture in NSCLC tumors is complex, and particularly TP53-mutated lung adenocarcinomas displayed highly aberrant copy number profiles. We suggest to always include TP53-mutation status when studying copy number aberrations in NSCLC tumors. Copy number may further impact gene expression and alter cellular signaling pathways.

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

2021 ◽  
Vol 11 ◽  
Xisheng Fang ◽  
Xia Liu ◽  
Lin Lu ◽  
Guolong Liu

BackgroundRenal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.MethodsClinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.ResultsA total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, ‘PRDM16-dt’, AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients’ overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.ConclusionOur study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

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