we used protein sequences from the swiss - prot database release 45.0 ( http://www.ebi.ac.uk/swissprot ) for training and testing purposes in this study . to obtain high - quality datasets , we filtered the data as follows : ( 1 ) include sequences only from the animal species that have experimentally derived annotations for  subcellular localization  .
( 2 ) remove sequences with ambiguous and uncertain annotations , such as  by similarity 
,  potential  ,  probable  ,  possible  , and so on .
( 3 ) remove sequences known to exist in more than one subcellular localization , such as those that shuttle between the cytoplasm and the nucleus .
these localizations include ( the number of sequences are shown in parentheses ) : cyt - cytoplasm ( 2,673 ) , end - endoplasmic reticulmn ( 794 ) , exc - extracellular / secretory compartment ( 7,077 ) , gol - golgi complex ( 253 ) , lys - lysosome ( 179 ) , mit - mitochondrion ( 2,019 ) , nuc - nucleus ( 4,112 ) , pla - plasma membrane ( 5,273 ) , and pox - peroxisome ( 185 ) . from these datasets , we separated a subset of 3,749 proteins belonging to human .
three classes of features of amino acid sequences were used in the current study , including composition , transition , and distribution .
composition is a reference to the proportions of amino acid types contributing to the protein sequence .
transition represents the frequency with which specific amino acid types are followed or preceded by other amino acid types within the sequence .
distribution captures the dissemination of specific amino acid types within specific portions of the sequence ( or the entire sequence ) .
these feature types have been used in previous ml algorithms to characterize amino acid sequences based on hydrophobicity , nvwv , polarity , polarizability , and charge ( table 1 ) . based on numerical attributes characterizing amino acid composition , transition , and distribution along with the categories just outlined (
table 1 ) , a common lisp algorithm ( 33 ) was used to generate a vector of size 125 for each protein .
a matrix consisting of a vector of each of the proteins ( figure 6b ) was thus generated and used as a training set for ml ( 32 ) .
based on the data , predictive classifications ( based on instances derived from the training set alone as well as the training set in conjunction with ten - fold cross validations ) were made by using j48 , svm , mlp , and nb classifier .
the mp algorithm ( 25 ) was used along with boxplots ( 35 ) in these studies to help establish effects .
the mp procedure fits an additive model : response  variable = common  value+row  effect+column  effect+residualwhere the common value is constant throughout the table ; the row effect is constant by rows ; the column effect is constant by columns ; and the residuals or remaining effects represent departures of each data array element from the purely additive model .
mp works iteratively on a data table , alternatively finding and subtracting column medians and row medians until all columns and rows have zero medians .
the residuals , row effects , or column effects may then be illustrated graphically by the way of a stem - and - leaf display or boxplot .
boxplots depict the distribution s central tendency ( median ) , spread ( fourth - spread ) , skewness ( based on the relative positions of the median , lower fourth , and upper fourth ) , tail length , as well as outliers .
the r language ( http://www.r-project.org/ ) statistical environment was used to implement the eda aspects of the study .
furthermore , for each subcellular compartment , boxplots ( 36 ) were generated for each amino acid category and feature .
gka conducted the machine learning and exploratory data analysis experiments and co - wrote the draft manuscript .
sml conceived the original idea of using this approach of protein characterization , wrote the initial code implementing it and wrote portions of the manuscript .
cg collected the dataset used for the experiments , wrote the code for cleaning up the data to render them useful for the experiments , co - wrote and edited the various drafts of the manuscript .
we used protein sequences from the swiss - prot database release 45.0 ( http://www.ebi.ac.uk/swissprot ) for training and testing purposes in this study . to obtain high - quality datasets , we filtered the data as follows : ( 1 ) include sequences only from the animal species that have experimentally derived annotations for  subcellular localization  .
( 2 ) remove sequences with ambiguous and uncertain annotations , such as  by similarity 
,  potential  ,  probable  ,  possible  , and so on .
( 3 ) remove sequences known to exist in more than one subcellular localization , such as those that shuttle between the cytoplasm and the nucleus .
these localizations include ( the number of sequences are shown in parentheses ) : cyt - cytoplasm ( 2,673 ) , end - endoplasmic reticulmn ( 794 ) , exc - extracellular / secretory compartment ( 7,077 ) , gol - golgi complex ( 253 ) , lys - lysosome ( 179 ) , mit - mitochondrion ( 2,019 ) , nuc - nucleus ( 4,112 ) , pla - plasma membrane ( 5,273 ) , and pox - peroxisome ( 185 ) . from these datasets , we separated a subset of 3,749 proteins belonging to human .
three classes of features of amino acid sequences were used in the current study , including composition , transition , and distribution .
composition is a reference to the proportions of amino acid types contributing to the protein sequence .
transition represents the frequency with which specific amino acid types are followed or preceded by other amino acid types within the sequence .
distribution captures the dissemination of specific amino acid types within specific portions of the sequence ( or the entire sequence ) .
these feature types have been used in previous ml algorithms to characterize amino acid sequences based on hydrophobicity , nvwv , polarity , polarizability , and charge ( table 1 ) . based on numerical attributes characterizing amino acid composition , transition , and distribution along with the categories just outlined ( table 1 ) ,
a common lisp algorithm ( 33 ) was used to generate a vector of size 125 for each protein .
the breakdown of the elements of each vector is outlined as in figure 6a . a matrix consisting of a vector of each of the proteins ( figure 6b ) was thus generated and used as a training set for ml ( 32 ) .
based on the data , predictive classifications ( based on instances derived from the training set alone as well as the training set in conjunction with ten - fold cross validations ) were made by using j48 , svm , mlp , and nb classifier .
was used along with boxplots ( 35 ) in these studies to help establish effects .
the mp procedure fits an additive model : response  variable = common  value+row  effect+column  effect+residualwhere the common value is constant throughout the table ; the row effect is constant by rows ; the column effect is constant by columns ; and the residuals or remaining effects represent departures of each data array element from the purely additive model .
the residuals , row effects , or column effects may then be illustrated graphically by the way of a stem - and - leaf display or boxplot .
boxplots depict the distribution s central tendency ( median ) , spread ( fourth - spread ) , skewness ( based on the relative positions of the median , lower fourth , and upper fourth ) , tail length , as well as outliers .
the r language ( http://www.r-project.org/ ) statistical environment was used to implement the eda aspects of the study .
furthermore , for each subcellular compartment , boxplots ( 36 ) were generated for each amino acid category and feature .
gka conducted the machine learning and exploratory data analysis experiments and co - wrote the draft manuscript .
sml conceived the original idea of using this approach of protein characterization , wrote the initial code implementing it and wrote portions of the manuscript .
cg collected the dataset used for the experiments , wrote the code for cleaning up the data to render them useful for the experiments , co - wrote and edited the various drafts of the manuscript .
 identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products . in this study , we use machine learning and exploratory data analysis ( eda ) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments . 
 a dataset of 3,749 protein sequences representing human proteins was extracted from the swiss - prot database . 
 feature vectors were created to capture specific amino acid sequence characteristics . 
 relative to a support vector machine , a multi - layer perceptron , and a nave bayes classifier , the c4.5 decision tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences ( average precision=0.88 ; average sensitivity=0.86 ) . 
 furthermore , eda graphics characterized essential features of proteins in each compartment . as examples , proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids ; cytoplasmic proteins had higher proportions of neutral amino acids ; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids . 
 these data showed that the c4.5 classifier and eda tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences . 