two datasets ( table s5 ) were compiled for this study , which are denoted as asn_g 1756 ( human ) and asn_g 1008 ( eukaryote ) . asn_g ( human ) is collected from a manually curated database for the subcellular localizations of proteins in human   and asn_g ( eukaryote ) , which is from esldb , is a database for eukaryotic organisms . these are the only two manually curated public databases with experimental annotations reported in www.psort.org   for eukaryotes . asn_g ( human ) and asn_g ( eukaryote ) is maintained by the rost lab of columbia university bioinformatics centre and the bologna biocomputing group , university of bologna , respectively . these experimentally annotated proteins were finalized by verifying with uniprot ( www.uniprot.org , release 2011 - 02 sept ,  potential  ,  possible  and  by similarity  were eliminated to remove sequences with ambiguous and uncertain annotations . we used the cluster database at high identity with tolerance ( cd - hit-2d )   web server to eliminate sequences in both datasets that displayed a similarity greater than or equal to 30% . the program ( cd - hit ) takes a fasta format sequence database as input and produces a set of  non - redundant representative sequences as output by removing the highly similar sequences . for comparing our results with the loctree2 , we downloaded 1682 sequences from the loctree2 publication site   and generated a dataset with 1677 sequences ( table s7 ) after verifying the subcellular localizations with uniprot ( march 2013 ) . the first group consists of sequence driven features , which are generated directly from sequence through converting the protein sequence into a numeric sequence by replacing each amino acid with equivalent numeric values , counts , etc . the second group consists of sequence mapped features , which are generated by mapping amino acids into sub groups and the third group contains sequence autocorrelation features , which are obtained from calculations based on three types of spatial autocorrelation ( moreau - broto , moran and geary ) . there are two composition features considered , which include amino acid dipeptide composition ( dipeptide descriptors ) and composition of physico - chemical properties ( amino acid index ) . dipeptide composition , which gives a fixed pattern length of 400 ( 20    20 ) , encapsulates the global information about each protein sequence and the order it contains . for example , in the sample protein sequence gcatggtgcgaaactttggctg , 400 pairs of dipeptide occurrence frequency with no skips c0 , are calculated by counting its presence in the sequence with no gaps . in figure 2 , the count of c0gc is 3 , one skip c1gc is 1 and two skips c2gc is 1 . the feature vector using the dipeptide occurrence frequency count for a protein sequence is represented as three separate numeric counts of its dipeptide c0 , c1 and c2 , each having 400 components . the final feature vector of 1200 components is formed by concatenating the corresponding vectors c0 , c1 and c2 . the amino acid index ( aaindex -1,2,3 ) is a database of numerical indices representing various physico - chemical and biochemical properties of amino acids and pairs of amino acids . physico - chemical properties derived from the aaindex1 database having 544 indices are used to compute the features . f544 } where f1 is the physico - chemical property value for all residues of the sequence divided by the length of the sequence . structural variation in the r groups of amino acids is considered as the main factor for its difference in properties . from side chains we can classify amino acids into four groups ( 1 ) non - polar and neutral , ( 2 ) polar and neutral , ( 3 ) acidic and polar , and ( 4 ) basic and polar . the 20 amino acids forming the protein sequence can also be divided into several groups based on their other properties like ( 5 ) charge , ( 6 ) hydrophilicity or hydrophobicity , ( 7 ) size , and ( 8) functional groups . twenty amino acids can be mapped into 13 groups by replacing each amino acid code with its group code . from the mapped sequence , composition is determined as the number of amino acids of a particular property divided by total number of amino acids , whereas transition is determined as the number of transition from a particular property to different property divided by ( total number of amino acids    1 ) . distribution is the chain length within which the first , 25% , 50% , 75% and 100% of the amino acids of a particular property are located . according to the property types , amino acids are divided into three groups and are marked as numeric indices 1 , 2 and 3 ( table s6 ) . properties whose attributes can be grouped perfectly into three sets like charge , hydrophobicity , normalized van der waals volume , polarity , polarizability , secondary structure and solvent accessibility are used for this mapping [ 3235 ] . for example , according to secondary structure property grouping , the sample protein sequence heamrqltifvcywnspddg is coded as  22222223333333111111  . in this example with the property of secondary structure , the total count of the coil is 6 , the helix is 7 and the strand is 7 . hence the composition is calculated as 6/20 , 7/20 and 7/20 , where 20 is the total length of the sequence . the transition from class 1 to 2 is the percentage frequency with which class 1 is followed by class 2 or class 2 is followed by class 1 in the encoded sequence , likewise the transition from class 3 to class 1 or class 1 to class 3 , etc . for the sample sequence , the sum of transition from 2 to 3 and 3 to 2 is 1 . five distribution descriptors are formed for each group , including the position percentages in the sequence for the first residue , 25% of the residues , 50% of the residues , 75% of the residues and 100% of the residues . is performed for 7 properties for each protein sequence after dividing each sequence into three equal segments . in total , 21    3 attributes for a sequence and 441 attributes for 7 properties compose the final feature vector . sequence autocorrelation - based features are based on the tobler s first law of geography everything is related to everything else but nearby things are more related than distant things  . sequence autocorrelation - based features also assume that  the disturbances in each area are systematically related to those in adjacent areas  . spatial autocorrelation measures the degree to which near and distant things are related , which is positive when nearby things are similar and negative when they are dissimilar . this concept helps to analyze the dependency among the features of sequences in each location . autocorrelation features are calculated based on the distribution of amino acid properties along the sequence . thirty nine amino acid indices related to hydrophobicity are used for calculation after replacing each amino acid with its equivalent normalized index as pi . three autocorrelation descriptors are used as features , including normalized moreau - broto autocorrelation descriptors , moran auto - correlation descriptors   and geary autocorrelation descriptors . the moreau - broto autocorrelation descriptor is defined asmb(d)=i=1n - dpipi+dwhered=1,2,3upto max.lagwhere d is the lag of the autocorrelation , n is the length of the sequence , and pi and pi+d are the amino acid index value of the selected property at position i and i  +  d , respectively . the normalized moreau - broto autocorrelation descriptors are defined as mb(d)/(n    d ) . the moran autocorrelation descriptor is defined asmoran(d)=1n - di=1n - d(pi - p)(pi+d - p)1ni=1n(pi - p)2d=1,2,3 the geary autocorrelation descriptor is defined asgeary(d)=12(n - d)i=1n - d(pi - pi+d)21n-1i=1n(pi - p)2d=1,2,3 3510 attributes from 39 amino acid properties with 30 lags compose the sequence feature vector for autocorrelation . among prediction algorithms , ensemble learning is a process by which multiple models such as classifiers are generated and combined to improve overall prediction accuracy . multiple learners ( base learners ) are trained to solve the same problem by averaging over multiple classification models with different input feature vectors . these ensemble techniques reduce the small sample size problem which is critical in biological applications . the three most popular classifiers based on the ensemble method , are bagging , adaboost m1   and random forest . in this study , two methods bagging and adaboost were used to predict protein trafficking at all levels of protein sorting pathway . this method uses multiple versions of a training set on different models by using the bootstrap ( sampling with replacement ) . the outputs of the models are combined ( average or vote ) to create a single output . adaboost m1 adopts an adaptive sampling by using all instances of each iteration . in bagging , each classifier has the vote of the same strength , whereas adaboost m1 assigns different voting strengths to classifiers based on their accuracy . the classifier performance evaluation parameters specificity , sensitivity , accuracy , mcc , ppv , npv   and roc   were calculated at all levels as per the below equations . specificity ( sp ) is determined as ( tn)/(tn  +  fp ) , where tn indicates true negative and fp means false positive . sensitivity is defined as ( tp)/(tp  +  fn ) , where tp means true positive and fn means false negative . accuracy is defined as ( tp  +  tn)/(tp  +  tn  +  fp  +  fn ) . ppv and npv is calculated as ( tp)/(tp  +  fp ) and ( tn)/(tn  +  fn ) , respectively . two datasets ( table s5 ) were compiled for this study , which are denoted as asn_g 1756 ( human ) and asn_g 1008 ( eukaryote ) . asn_g ( human ) is collected from a manually curated database for the subcellular localizations of proteins in human   and asn_g ( eukaryote ) , which is from esldb , is a database for eukaryotic organisms . these are the only two manually curated public databases with experimental annotations reported in www.psort.org   for eukaryotes . asn_g ( human ) and asn_g ( eukaryote ) is maintained by the rost lab of columbia university bioinformatics centre and the bologna biocomputing group , university of bologna , respectively . these experimentally annotated proteins were finalized by verifying with uniprot ( www.uniprot.org , release 2011 - 02 sept ,  potential  ,  possible  and  by similarity  were eliminated to remove sequences with ambiguous and uncertain annotations . we used the cluster database at high identity with tolerance ( cd - hit-2d )   web server to eliminate sequences in both datasets that displayed a similarity greater than or equal to 30% . the program ( cd - hit ) takes a fasta format sequence database as input and produces a set of  non - redundant representative sequences as output by removing the highly similar sequences . for comparing our results with the loctree2 , we downloaded 1682 sequences from the loctree2 publication site   and generated a dataset with 1677 sequences ( table s7 ) after verifying the subcellular localizations with uniprot ( march 2013 ) . the first group consists of sequence driven features , which are generated directly from sequence through converting the protein sequence into a numeric sequence by replacing each amino acid with equivalent numeric values , counts , etc . the second group consists of sequence mapped features , which are generated by mapping amino acids into sub groups and the third group contains sequence autocorrelation features , which are obtained from calculations based on three types of spatial autocorrelation ( moreau - broto , moran and geary ) . there are two composition features considered , which include amino acid dipeptide composition ( dipeptide descriptors ) and composition of physico - chemical properties ( amino acid index ) . dipeptide composition , which gives a fixed pattern length of 400 ( 20    20 ) , encapsulates the global information about each protein sequence and the order it contains . for example , in the sample protein sequence gcatggtgcgaaactttggctg , 400 pairs of dipeptide occurrence frequency with no skips c0 , are calculated by counting its presence in the sequence with no gaps . in figure 2 , the count of c0gc is 3 , one skip c1gc is 1 and two skips c2gc is 1 . the dipeptide count , the feature vector using the dipeptide occurrence frequency count for a protein sequence is represented as three separate numeric counts of its dipeptide c0 , c1 and c2 , each having 400 components . the final feature vector of 1200 components is formed by concatenating the corresponding vectors c0 , c1 and c2 . the amino acid index ( aaindex -1,2,3 ) is a database of numerical indices representing various physico - chemical and biochemical properties of amino acids and pairs of amino acids . physico - chemical properties derived from the aaindex1 database having 544 indices are used to compute the features . f544 } where f1 is the physico - chemical property value for all residues of the sequence divided by the length of the sequence . structural variation in the r groups of amino acids is considered as the main factor for its difference in properties . from side chains we can classify amino acids into four groups ( 1 ) non - polar and neutral , ( 2 ) polar and neutral , ( 3 ) acidic and polar , and ( 4 ) basic and polar . the 20 amino acids forming the protein sequence can also be divided into several groups based on their other properties like ( 5 ) charge , ( 6 ) hydrophilicity or hydrophobicity , ( 7 ) size , and ( 8) functional groups . twenty amino acids can be mapped into 13 groups by replacing each amino acid code with its group code . from the mapped sequence , composition is determined as the number of amino acids of a particular property divided by total number of amino acids , whereas transition is determined as the number of transition from a particular property to different property divided by ( total number of amino acids    1 ) . distribution is the chain length within which the first , 25% , 50% , 75% and 100% of the amino acids of a particular property are located . according to the property types , amino acids are divided into three groups and are marked as numeric indices 1 , 2 and 3 ( table s6 ) . properties whose attributes can be grouped perfectly into three sets like charge , hydrophobicity , normalized van der waals volume , polarity , polarizability , secondary structure and solvent accessibility are used for this mapping [ 3235 ] . for example , according to secondary structure property grouping , the sample protein sequence heamrqltifvcywnspddg is coded as  22222223333333111111  . in this example with the property of secondary structure , the total count of the coil is 6 , the helix is 7 and the strand is 7 . hence the composition is calculated as 6/20 , 7/20 and 7/20 , where 20 is the total length of the sequence . the transition from class 1 to 2 is the percentage frequency with which class 1 is followed by class 2 or class 2 is followed by class 1 in the encoded sequence , likewise the transition from class 3 to class 1 or class 1 to class 3 , etc . for the sample sequence , the sum of transition from 2 to 3 and 3 to 2 is 1 . five distribution descriptors are formed for each group , including the position percentages in the sequence for the first residue , 25% of the residues , 50% of the residues , 75% of the residues and 100% of the residues . is performed for 7 properties for each protein sequence after dividing each sequence into three equal segments . in total , 21    3 attributes for a sequence and 441 attributes for 7 properties compose the final feature vector . sequence autocorrelation - based features are based on the tobler s first law of geography everything is related to everything else but nearby things are more related than distant things  . sequence autocorrelation - based features also assume that  the disturbances in each area are systematically related to those in adjacent areas  . spatial autocorrelation measures the degree to which near and distant things are related , which is positive when nearby things are similar and negative when they are dissimilar . this concept helps to analyze the dependency among the features of sequences in each location . autocorrelation features are calculated based on the distribution of amino acid properties along the sequence . thirty nine amino acid indices related to hydrophobicity are used for calculation after replacing each amino acid with its equivalent normalized index as pi . three autocorrelation descriptors are used as features , including normalized moreau - broto autocorrelation descriptors , moran auto - correlation descriptors   and geary autocorrelation descriptors . the moreau - broto autocorrelation descriptor is defined asmb(d)=i=1n - dpipi+dwhered=1,2,3upto max.lagwhere d is the lag of the autocorrelation , n is the length of the sequence , and pi and pi+d are the amino acid index value of the selected property at position i and i  +  d , respectively . the normalized moreau - broto autocorrelation descriptors are defined as mb(d)/(n    d ) . the moran autocorrelation descriptor is defined asmoran(d)=1n - di=1n - d(pi - p)(pi+d - p)1ni=1n(pi - p)2d=1,2,3 the geary autocorrelation descriptor is defined asgeary(d)=12(n - d)i=1n - d(pi - pi+d)21n-1i=1n(pi - p)2d=1,2,3 3510 attributes from 39 amino acid properties with 30 lags compose the sequence feature vector for autocorrelation . there are two composition features considered , which include amino acid dipeptide composition ( dipeptide descriptors ) and composition of physico - chemical properties ( amino acid index ) . dipeptide composition , which gives a fixed pattern length of 400 ( 20    20 ) , encapsulates the global information about each protein sequence and the order it contains . for example , in the sample protein sequence gcatggtgcgaaactttggctg , 400 pairs of dipeptide occurrence frequency with no skips c0 , are calculated by counting its presence in the sequence with no gaps . in figure 2 , the count of c0gc is 3 , one skip c1gc is 1 and two skips c2gc is 1 . the dipeptide count , the feature vector using the dipeptide occurrence frequency count for a protein sequence is represented as three separate numeric counts of its dipeptide c0 , c1 and c2 , each having 400 components . the final feature vector of 1200 components is formed by concatenating the corresponding vectors c0 , c1 and c2 . the amino acid index ( aaindex -1,2,3 ) is a database of numerical indices representing various physico - chemical and biochemical properties of amino acids and pairs of amino acids . physico - chemical properties derived from the aaindex1 database having 544 indices are used to compute the features . f544 } where f1 is the physico - chemical property value for all residues of the sequence divided by the length of the sequence . structural variation in the r groups of amino acids is considered as the main factor for its difference in properties . from side chains we can classify amino acids into four groups ( 1 ) non - polar and neutral , ( 2 ) polar and neutral , ( 3 ) acidic and polar , and ( 4 ) basic and polar . the 20 amino acids forming the protein sequence can also be divided into several groups based on their other properties like ( 5 ) charge , ( 6 ) hydrophilicity or hydrophobicity , ( 7 ) size , and ( 8) functional groups . twenty amino acids can be mapped into 13 groups by replacing each amino acid code with its group code . from the mapped sequence , composition is determined as the number of amino acids of a particular property divided by total number of amino acids , whereas transition is determined as the number of transition from a particular property to different property divided by ( total number of amino acids    1 ) . distribution is the chain length within which the first , 25% , 50% , 75% and 100% of the amino acids of a particular property are located . according to the property types , amino acids are divided into three groups and are marked as numeric indices 1 , 2 and 3 ( table s6 ) . properties whose attributes can be grouped perfectly into three sets like charge , hydrophobicity , normalized van der waals volume , polarity , polarizability , secondary structure and solvent accessibility are used for this mapping [ 3235 ] . for example , according to secondary structure property grouping , the sample protein sequence heamrqltifvcywnspddg is coded as  22222223333333111111  . in this example with the property of secondary structure , the total count of the coil is 6 , the helix is 7 and the strand is 7 . hence the composition is calculated as 6/20 , 7/20 and 7/20 , where 20 is the total length of the sequence . the transition from class 1 to 2 is the percentage frequency with which class 1 is followed by class 2 or class 2 is followed by class 1 in the encoded sequence , likewise the transition from class 3 to class 1 or class 1 to class 3 , etc . for the sample sequence , the sum of transition from 2 to 3 and 3 to 2 is 1 . five distribution descriptors are formed for each group , including the position percentages in the sequence for the first residue , 25% of the residues , 50% of the residues , 75% of the residues and 100% of the residues . is performed for 7 properties for each protein sequence after dividing each sequence into three equal segments . in total , 21    3 attributes for a sequence and 441 attributes for 7 properties compose the final feature vector . sequence autocorrelation - based features are based on the tobler s first law of geography everything is related to everything else but nearby things are more related than distant things  . sequence autocorrelation - based features also assume that  the disturbances in each area are systematically related to those in adjacent areas  . spatial autocorrelation measures the degree to which near and distant things are related , which is positive when nearby things are similar and negative when they are dissimilar . this concept helps to analyze the dependency among the features of sequences in each location . autocorrelation features are calculated based on the distribution of amino acid properties along the sequence . thirty nine amino acid indices related to hydrophobicity are used for calculation after replacing each amino acid with its equivalent normalized index as pi . three autocorrelation descriptors are used as features , including normalized moreau - broto autocorrelation descriptors , moran auto - correlation descriptors   and geary autocorrelation descriptors . the moreau - broto autocorrelation descriptor is defined asmb(d)=i=1n - dpipi+dwhered=1,2,3upto max.lagwhere d is the lag of the autocorrelation , n is the length of the sequence , and pi and pi+d are the amino acid index value of the selected property at position i and i  +  d , respectively . the normalized moreau - broto autocorrelation descriptors are defined as mb(d)/(n    d ) . the moran autocorrelation descriptor is defined asmoran(d)=1n - di=1n - d(pi - p)(pi+d - p)1ni=1n(pi - p)2d=1,2,3 the geary autocorrelation descriptor is defined asgeary(d)=12(n - d)i=1n - d(pi - pi+d)21n-1i=1n(pi - p)2d=1,2,3 3510 attributes from 39 amino acid properties with 30 lags compose the sequence feature vector for autocorrelation . among prediction algorithms , ensemble learning is a process by which multiple models such as classifiers are generated and combined to improve overall prediction accuracy . multiple learners ( base learners ) are trained to solve the same problem by averaging over multiple classification models with different input feature vectors . these ensemble techniques reduce the small sample size problem which is critical in biological applications . the three most popular classifiers based on the ensemble method , are bagging , adaboost m1   and random forest . in this study , two methods bagging and adaboost were used to predict protein trafficking at all levels of protein sorting pathway . this method uses multiple versions of a training set on different models by using the bootstrap ( sampling with replacement ) . the outputs of the models are combined ( average or vote ) to create a single output . adaboost m1 adopts an adaptive sampling by using all instances of each iteration . in bagging , each classifier has the vote of the same strength , whereas adaboost m1 assigns different voting strengths to classifiers based on their accuracy . the classifier performance evaluation parameters specificity , sensitivity , accuracy , mcc , ppv , npv   and roc   were calculated at all levels as per the below equations . specificity ( sp ) is determined as ( tn)/(tn  +  fp ) , where tn indicates true negative and fp means false positive . sensitivity is defined as ( tp)/(tp  +  fn ) , where tp means true positive and fn means false negative . accuracy is defined as ( tp  +  tn)/(tp  +  tn  +  fp  +  fn ) . ppv and npv is calculated as ( tp)/(tp  +  fp ) and ( tn)/(tn  +  fn ) , respectively . gg collected the dataset , conducted the data analysis , did machine learning experiments and wrote the manuscript . asn conceived the original idea of using ensemble classifiers for the prediction of protein localization hierarchically . table s1performance evaluation summary of classifiers against features for the 6-fold cross validation test at all levels of hierarchy for datasets 1 & 2table s2performance evaluation summary of classifiers against features for the independent data test at all levels of hierarchy for datasets 1 & 2table s3comparison of the 6-fold cross validation results of datasets 1 & 2 with the published results of loctreetable s4comparison of the 5-fold cross validation results with the published results of loctree2 ( dataset of loctree 2)table s5the subcellular location distribution of the datasets asn_g 1756 & asn_g 1008table s6amino acid attributes and grouping of the amino acidstable s7loctree2 data performance evaluation summary of classifiers against features for the 6-fold cross validation test at all levels of hierarchy for datasets 1 & 2 performance evaluation summary of classifiers against features for the independent data test at all levels of hierarchy for datasets 1 & 2 comparison of the 6-fold cross validation results of datasets 1 & 2 with the published results of loctree comparison of the 5-fold cross validation results with the published results of loctree2 ( dataset of loctree 2 ) the subcellular location distribution of the datasets asn_g 1756 & asn_g 1008 amino acid attributes and grouping of the amino acids
protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contained in the protein . 
 many methods reported prediction of the subcellular location of proteins from sequence information . 
 however , most of these prediction methods use a flat structure or parallel architecture to perform prediction . in this work , 
 we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein - sorting pathway hierarchically . sequence driven features , sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared . when evaluated by independent data testing , ensemble based - bagging algorithms with sequence feature composition , transition and distribution ( ctd ) successfully classified two datasets with accuracies greater than 90% . 
 we compared our results with similar published methods , and our method equally performed with the others at two levels in the secreted pathway . 
 this study shows that the feature ctd extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways .