Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location

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Prediction of Protein Structural Classes

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Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine

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Large-Scale Predictions of Gram-Negative Bacterial Protein Subcellular Locations

Chou, K., & Shen, H. (2006) · Journal of Proteome Research · doi:10.1021/pr060404b · Cited by 132

Using the Concept of Chous Pseudo Amino Acid Composition to Predict Enzyme Family Classes: An Approach with Support Vector Machine Based on Discrete Wavelet Transform

Qiu, J., Huang, J., Shi, S., & Liang, R. (2010) · Protein and Peptide Letters · doi:10.2174/092986610791190372 · Cited by 128

Using cellular automata to generate image representation for biological sequences

Xiao, X., Shao, S., Ding, Y., Huang, Z., Chen, X., & Chou, K.-C. (2005) · Amino Acids · doi:10.1007/s00726-004-0154-9 · Cited by 129

Identification of Secretory Proteins inMycobacterium tuberculosisUsing Pseudo Amino Acid Composition

Yang, H., Tang, H., Chen, X.-X., Zhang, C.-J., Zhu, P.-P., Ding, H., Chen, W., & Lin, H. (2016) · BioMed Research International · doi:10.1155/2016/5413903 · Cited by 127

Subcellular fractionation methods and strategies for proteomics

Lee, Y. H., Tan, H. T., & Chung, M. C. M. (2010) · PROTEOMICS · doi:10.1002/pmic.201000289 · Cited by 127

GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions

Xiao, X., Wang, P., & Chou, K. (2010) · Molecular BioSystems · doi:10.1039/c0mb00170h · Cited by 125

Prediction of protein structural classes using support vector machines

Sun, X., & Huang, R. (2006) · Amino Acids · doi:10.1007/s00726-005-0239-0 · Cited by 125

A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine

Zhang, Z., Wang, Z., Zhang, Z., & Wang, Y. (2006) · FEBS Letters · doi:10.1016/j.febslet.2006.10.017 · Cited by 124

Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines

Yu, X., Cao, J., Cai, Y., Shi, T., & Li, Y. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.09.018 · Cited by 122

SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition

Yu, L., Guo, Y., Li, Y., Li, G., Li, M., Luo, J., Xiong, W., & Qin, W. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.08.001 · Cited by 121

Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition

Shi, J., Zhang, S.-W., Pan, Q., Cheng, Y., & Xie, J. (2007) · Amino Acids · doi:10.1007/s00726-006-0475-y · Cited by 121

Protein Remote Homology Detection by Combining Chou’s Pseudo Amino Acid Composition and Profile‐Based Protein Representation

Liu, B., Wang, X., Zou, Q., Dong, Q., & Chen, Q. (2013) · Molecular Informatics · doi:10.1002/minf.201300084 · Cited by 118

pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC

Xiao, X., Cheng, X., Chen, G.-Q., Mao, Q., & Chou, K. (2018) · Genomics · doi:10.1016/j.ygeno.2018.05.017 · Cited by 117

Predicting Subcellular Localization via Protein Motif Co-OccurrencePreprint

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Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition

Zhu, P.-P., Li, W., Zhong, Z.-J., Deng, E.-Z., Ding, H., Chen, W., & Lin, H. (2014) · Molecular BioSystems · doi:10.1039/c4mb00645c · Cited by 113

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning

Mei, S. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.06.028 · Cited by 113

Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition

Cai, Y., & Chou, K. (2003) · Biochemical and Biophysical Research Communications · doi:10.1016/s0006-291x(03)00775-7 · Cited by 110

PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies

Cao, D., Liang, Y., Yan, J., Tan, G., Xu, Q., & Liu, S. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400127q · Cited by 110

Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM

Mundra, P. A., Kumar, M., Kumar, K. K., Valadi, J. K., & Kulkarni, B. D. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.04.001 · Cited by 109

An Efficient Classifier for Alzheimer’s Disease Genes Identification

Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107

Efficacy of different protein descriptors in predicting protein functional families

Ong, S. A. K., Lin, H., Chen, Y., Li, Z. R., & Cao, Z. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-300 · Cited by 105

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features

Zhou, H., Yang, Y., & Shen, H. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw723 · Cited by 105

Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes

Zhang, T.-L., & Ding, Y.-S. (2007) · Amino Acids · doi:10.1007/s00726-007-0496-1 · Cited by 104

Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC

Khan, M., Hayat, M., Khan, S. A., & Iqbal, N. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.12.004 · Cited by 103

Predicting protein localization in budding Yeast

Chou, K., & Cai, Y. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti104 · Cited by 103

Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition

Fan, G.-L., & Li, Q. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.03.017 · Cited by 102

Predicting membrane protein type by functional domain composition and pseudo-amino acid composition

Cai, Y., & Chou, K. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.05.035 · Cited by 102

Bioinformatics with soft computing

Mitra, S., & Hayashi, Y. (2006) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2006.879384 · Cited by 103

An SVM-based system for predicting protein subnuclear localizations

Lei, Z., & Dai, Y. D. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-291 · Cited by 101

iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition

Xiao, X., Xu, Z., Qiu, W., Wang, P., Ge, H.-T., & Chou, K. (2018) · Genomics · doi:10.1016/j.ygeno.2018.12.001 · Cited by 100

Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs

Chou, K. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190507082559 · Cited by 97

Predicting protein subcellular location by fusing multiple classifiers

Chou, K., & Shen, H. (2006) · Journal of Cellular Biochemistry · doi:10.1002/jcb.20879 · Cited by 97

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Xu, L., Liang, G., Shi, S., & Liao, C. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061773 · Cited by 95

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

Xu, R., Zhou, J., Liu, B., He, Y., Zou, Q., Wang, X., & Chou, K. (2014) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2014.968624 · Cited by 95

pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset

Chou, K., Cheng, X., & Xiao, X. (2018) · Genomics · doi:10.1016/j.ygeno.2018.08.007 · Cited by 94

Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition

Kong, L., Zhang, L., & Lv, J. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.11.021 · Cited by 92

Bibliometric analysis of support vector machines research trend: a case study in China

Yu, D., Xu, Z., & Wang, X. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01028-y · Cited by 92

A domain-based approach to predict protein-protein interactions

Singhal, M., & Resat, H. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-199 · Cited by 92

pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties

Sarda, D., Chua, G. H., Li, K.-B., & Krishnan, A. V. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-152 · Cited by 90

Amino Acid Principal Component Analysis (AAPCA) and its Applications in Protein Structural Class Prediction

Du, Q.-S., Jiang, Z., He, W., Li, D.-P., & Chou, K.-C. (2006) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2006.10507088 · Cited by 90

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

Fan, G.-L., & Li, Q. (2011) · Amino Acids · doi:10.1007/s00726-011-1143-4 · Cited by 89

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions

Ding, C., Yuan, L., Guo, S.-H., Lin, H., & Chen, W. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.09.006 · Cited by 89

Predicting subcellular localization of proteins in a hybridization space

Cai, Y., & Chou, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth054 · Cited by 89

Classifying G protein-coupled receptors and nuclear receptors on the basis of protein power spectrum from fast Fourier transform

Guo, Y.-Z., Li, M., Lu, M., Wen, Z., Wang, K., Li, G., & Wu, J. (2006) · Amino Acids · doi:10.1007/s00726-006-0332-z · Cited by 89

iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix

Xiao, X., Wang, P., & Chou, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030869 · Cited by 87

Bio-support vector machines for computational proteomics

Yang, Z. R., & Chou, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg477 · Cited by 87

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides

Pandey, P., Patel, V., George, N. V., & Mallajosyula, S. S. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00322 · Cited by 91

EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou’s PseAAC

Chang, T., Wu, L., Lee, T.-Y., Chen, S. P., Huang, H.-D., & Horng, J. T. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9628-0 · Cited by 86

Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC

Zhang, S., & Duan, X. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.10.030 · Cited by 85

Prediction of membrane proteins using split amino acid and ensemble classification

Hayat, M., Khan, A., & Yeasin, M. (2011) · Amino Acids · doi:10.1007/s00726-011-1053-5 · Cited by 85

Prediction of protein subcellular location using a combined feature of sequence

Gao, Q.-B., Wang, Z., Yan, C. G., & Du, Y. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.05.021 · Cited by 85

Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC

Mandal, M., Mukhopadhyay, A., & Maulik, U. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1238-7 · Cited by 84

Better prediction of sub‐cellular localization by combining evolutionary and structural information

Nair, R. P., & Rost, B. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10507 · Cited by 81

Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo‐amino acid composition

Chou, K., & Cai, Y. (2004) · Journal of Cellular Biochemistry · doi:10.1002/jcb.10790 · Cited by 80

Multitask Learning for Protein Subcellular Location Prediction

Xu, Q., Pan, S. J., Xue, H., & Yang, Q. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.22 · Cited by 79

Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers

Mei, J., & Zhao, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-20819-x · Cited by 78

Critical evaluation of web-based prediction tools for human protein subcellular localization

Shen, Y., Ding, Y., Tang, J., Zou, Q., & Guo, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz106 · Cited by 78

Heuristic molecular lipophilicity potential (HMLP): A 2D‐QSAR study to LADH of molecular family pyrazole and derivatives

Du, Q.-S., Mezey, P. G., & Chou, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20174 · Cited by 78

Identifying anticancer peptides by using improved hybrid compositions

Li, F., & Wang, X. (2016) · Scientific Reports · doi:10.1038/srep33910 · Cited by 77

iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

Fan, Y.-N., Xiao, X., Min, J., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15034915 · Cited by 75

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Bulashevska, A., & Eils, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-298 · Cited by 74

PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles

Wang, Y., Xue, Z., Shen, G., & Xu, J. (2008) · Amino Acids · doi:10.1007/s00726-007-0634-9 · Cited by 74

Prediction of protease types in a hybridization space

Chou, K., & Cai, Y. (2005) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2005.10.196 · Cited by 72

Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC

Pacharawongsakda, E., & Theeramunkong, T. (2013) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2013.2272014 · Cited by 71

Prediction of Enzyme Subfamily Class via Pseudo Amino Acid Composition by Incorporating the Conjoint Triad Feature

Wang, Y., Wang, X., Yang, Z., & Deng, N.-Y. (2010) · Protein and Peptide Letters · doi:10.2174/0929866511009011441 · Cited by 71

Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location

Jiang, X., Wei, R., Zhao, Y., & Zhang, T. (2008) · Amino Acids · doi:10.1007/s00726-008-0034-9 · Cited by 69

Gene ontology based transfer learning for protein subcellular localization

Mei, S., Fei, W., & Zhou, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-44 · Cited by 69

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

He, J., Gu, H., & Liu, W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037155 · Cited by 69

Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition

Chen, L., Feng, K., Cai, Y., Chou, K., & Li, H. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-293 · Cited by 69

Predicting protease types by hybridizing gene ontology and pseudo amino acid composition

Zhou, G., & Cai, Y. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20898 · Cited by 69

Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC

Butt, A. H., Rasool, N., & Khan, Y. D. (2018) · Molecular Biology Reports · doi:10.1007/s11033-018-4391-5 · Cited by 68

Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition

Wen, Z., Li, M., Li, Y., Guo, Y., & Wang, K. (2006) · Amino Acids · doi:10.1007/s00726-006-0341-y · Cited by 68

Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types

Liu, H., Yang, J., Wang, M., Li, X., & Chou, K. (2005) · The Protein Journal · doi:10.1007/s10930-005-7592-4 · Cited by 68

Predicting the quaternary structure attribute of a protein by hybridizing functional domain composition and pseudo amino acid composition

Xiao, X., Wang, P., & Chou, K. (2009) · Journal of Applied Crystallography · doi:10.1107/s0021889809002751 · Cited by 67

Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence

Zhang, T., Ding, Y., & Chou, K. (2006) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2006.08.003 · Cited by 67

Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression

Su, Q., Lu, W., Du, D., Chen, F., Niu, B., & Chou, K. (2017) · Oncotarget · doi:10.18632/oncotarget.17210 · Cited by 66

Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach

Li, F.-M., & Li, Q.-Z. (2007) · Amino Acids · doi:10.1007/s00726-007-0545-9 · Cited by 66

Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation

Yu, X., Zheng, X., Liu, T., Dou, Y., & Wang, J. (2011) · Amino Acids · doi:10.1007/s00726-011-0848-8 · Cited by 65

Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC

Butt, A. H., Rasool, N., & Khan, Y. D. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.04.019 · Cited by 64

Identification of Protein Subcellular Localization With Network and Functional Embeddings

Pan, X., Li, H., Zeng, T., Li, Z., Chen, L., Huang, T., & Cai, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.626500 · Cited by 63

Identification of proteases and their types

Shen, H., & Chou, K. (2008) · Analytical Biochemistry · doi:10.1016/j.ab.2008.10.020 · Cited by 63

Assessment of chemical libraries for their druggability

Sirois, S., Hatzakis, G., Wei, D., Du, Q.-S., & Chou, K. (2005) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2004.11.003 · Cited by 63

Predicting cancerlectins by the optimal g-gap dipeptides

Lin, H., Liu, W., He, J., Liu, X.-H., Ding, H., & Chen, W. (2015) · Scientific Reports · doi:10.1038/srep16964 · Cited by 62

Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm

Li, Z., Zhou, X., Dai, Z., & Zou, X. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-325 · Cited by 60

Virus-ECC-mPLoc: A Multi-Label Predictor for Predicting the Subcellular Localization of Virus Proteins with Both Single and Multiple Sites Based on a General Form of Chou's Pseudo Amino Acid Composition

Wang, X. R., Li, G., & Lu, W. (2013) · Protein and Peptide Letters · doi:10.2174/0929866511320030009 · Cited by 59

Identify catalytic triads of serine hydrolases by support vector machines

Cai, Y., Zhou, G., Jen, C.-H., Lin, S.-L., & Chou, K. (2004) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.02.019 · Cited by 59

GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors

Bhasin, M., & Raghava, G. P. S. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki351 · Cited by 59

pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset

Xiao, X., Cheng, X., Chen, G.-Q., Mao, Q., & Chou, K. (2018) · Medicinal Chemistry · doi:10.2174/1573406415666181217114710 · Cited by 58

Prediction of membrane protein types from sequences and position-specific scoring matrices

Pu, X., Guo, J., Leung, H. W. H., & Lin, Y. (2007) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.01.016 · Cited by 58

Support Vector Machine for predicting α-turn types

Cai, Y., Feng, K., Li, Y., & Chou, K. (2003) · Peptides · doi:10.1016/s0196-9781(03)00100-1 · Cited by 57

Predicting Protein-Protein Interactions by Combing Various Sequence- Derived Features into the General Form of Chou’s Pseudo Amino Acid Composition

Zhao, X., Ma, Z., & Yin, M. (2012) · Protein and Peptide Letters · doi:10.2174/092986612800191080 · Cited by 56

Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution

Shi, J., Zhang, S.-W., Pan, Q., & Zhou, G. (2008) · Amino Acids · doi:10.1007/s00726-007-0623-z · Cited by 56

Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks

Peng, X., Wang, J., Wang, J., Wu, F., & Pan, Y. F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130743 · Cited by 56

Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity

Diao, Y., Ma, D., Wen, Z., Yin, J., Xiang, J., & Li, M. (2007) · Amino Acids · doi:10.1007/s00726-007-0550-z · Cited by 55

A Review of Protein Function Prediction Under Machine Learning Perspective

Bernardes, J., & Pedreira, C. E. (2013) · Recent Patents on Biotechnology · doi:10.2174/18722083113079990006 · Cited by 55

2D‐RNA‐coupling numbers: A new computational chemistry approach to link secondary structure topology with biological function

González‐Díaz, H., Agüero-Chapin, G., Varona, J., Molina, R., Delogu, G., Santana, L., Uriarte, E., & Podda, G. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20576 · Cited by 55

Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins

Niu, B., Jin, Y., Feng, K., Lu, W., Cai, Y., & Li, G. (2008) · Molecular Diversity · doi:10.1007/s11030-008-9073-0 · Cited by 54

Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions

Ahmad, A., Lin, H., & Shatabda, S. (2020) · Genomics · doi:10.1016/j.ygeno.2020.02.011 · Cited by 56

GNBSL: A new integrative system to predict the subcellular location for Gram‐negative bacteria proteins

Guo, J., Lin, Y., & Liu, X. (2006) · PROTEOMICS · doi:10.1002/pmic.200600064 · Cited by 54

Machines for Prediction of Protein Subcellular

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Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs

Park, K.-J., & Kanehisa, M. I. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg222 · Cited by 367

Support Vector Machines for Prediction of Protein Subcellular Location

Cai, Y., Liu, X., Xu, X.-B., & Chou, K. (2000) · Molecular Cell Biology Research Communications · doi:10.1006/mcbr.2001.0285 · Cited by 43

Protein subcellular location prediction

Chou, K., & Elrod, D. W. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.2.107 · Cited by 375

Support vector machine approach for protein subcellular localization prediction

Hua, S., & Sun, Z. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.721 · Cited by 856