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

Basic local alignment search tool

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809

The Nature of Statistical Learning Theory

Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 5,528

The SWISS-PROT protein sequence data bank and its supplement TrEMBL

Bairoch, A., & Apweiler, R. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.1.31 · Cited by 3,594

Prediction of protein cellular attributes using pseudo‐amino acid composition

Chou, K. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.1035 · Cited by 1,954

Prediction of Protein Structural Classes

Chou, K., & Zhang, C. (1995) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409239509083488 · Cited by 1,054

Utilizing shared interacting domain patterns and Gene Ontology information to improve protein–protein interaction prediction

Roslan, R., Othman, R. M., Shah, Z. A., Kasim, S. B., Asmuni, H., Taliba, J., hassan, R. B., & Zakaria, Z. (2010) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2010.03.009 · Cited by 17

Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions

Zhao, W., Li, G., Wang, J., Zhou, Y.-K., Gao, Y., & Du, P.-F. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.04.025 · Cited by 17

A segmentation based model for subcellular location prediction of apoptosis protein

Dai, Q., Ma, S., Hai, Y., Yao, Y., & Liu, X. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.09.005 · Cited by 17

Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix

Xiong, W., Guo, Y., & Li, M. (2010) · The Protein Journal · doi:10.1007/s10930-010-9269-x · Cited by 16

Prediction of Protein Subcellular Localizations

Yu, C., & Hwang, J. (2008) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2008.306 · Cited by 16

Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines

Tahir, M., Jan, B., Hayat, M., Shah, S. U., & Amin, M. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.021 · Cited by 16

A Linear Regression Predictor for Identifying N6-Methyladenosine Sites Using Frequent Gapped K-mer Pattern

Zhuang, Y., Liu, H. J., Song, X., Ju, Y., & Peng, H. (2019) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2019.10.001 · Cited by 16

SVM ensemble based transfer learning for large-scale membrane proteins discrimination

Mei, S. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.09.007 · Cited by 16

Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

Shameer, K., Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., Archunan, G., & Sowdhamini, R. (2010) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s4464 · Cited by 16

DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins

Meher, P. K., Sahu, T. K., Banchariya, A., & Rao, A. R. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1587-y · Cited by 17

Prediction of Protein Subcellular Localizations Using Moment Descriptors and Support Vector Machine

Shi, J., Zhang, S., Liang, Y., & Pan, Q. (2006) · Lecture notes in computer science · doi:10.1007/11818564_12 · Cited by 16

Using predicted shape string to enhance the accuracy of γ-turn prediction

Zhu, Y., Li, T., Li, D., Zhang, Y., Xiong, W., Sun, J., Tang, Z., & Chen, G. (2011) · Amino Acids · doi:10.1007/s00726-011-0889-z · Cited by 15

Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures

Melidis, D. P., Malone, B., & Nejdl, W. (2021) · Algorithms · doi:10.3390/a14010028 · Cited by 15

Predicting Gram-positive bacterial protein subcellular localization based on localization motifs

Hu, Y., Li, T., Sun, J., Tang, S.-N., Xiong, W., Li, D., Chen, G., & Cong, P. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.05.031 · Cited by 15

Improved Indoor positioning algorithm using KPCA and ELM

Lian, L., Xia, S., Zhang, S., Wu, Q., & Jing, C. (2019) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2019.8928106 · Cited by 15

A hybrid machine-learning approach for segmentation of protein localization data

Kasson, P. M., Huppa, J. B., Davis, M. M., & Brünger, A. T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti615 · Cited by 15

Prediction of Peptidase Category Based on Functional Domain Composition

Xu, X., Yu, D. S., Wei, F., Cheng, Y., Qian, Z., Lu, W., Cai, Y., & Feng, K. (2008) · Journal of Proteome Research · doi:10.1021/pr800292w · Cited by 15

Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: An approach from discrete wavelet transformation

Qiu, J., Sun, X., Suo, S., Shi, S., Huang, S., Liang, R., & Zhang, L. (2011) · Biochimie · doi:10.1016/j.biochi.2011.03.010 · Cited by 14

Using Nearest Feature Line and Tunable Nearest Neighbor methods for prediction of protein subcellular locations

Gao, Q.-B., & Wang, Z. (2005) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2005.08.002 · Cited by 14

GFO: A data driven approach for optimizing the Gaussian function based similarity metric in computational biology

Lei, J.-B., Yin, J., & Shen, H. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.07.003 · Cited by 14

In silico identification of Gram-negative bacterial secreted proteins from primary sequence

Yu, L., Luo, J., Guo, Y., Li, Y., Pu, X., & Li, M. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.06.001 · Cited by 14

NRPred-FS: A Feature Selection based Two-level Predictor for Nuclear Receptors

Xiao, P. W. X. (2014) · Journal of Proteomics & Bioinformatics · doi:10.4172/jpb.s9-002 · Cited by 14

Identification of Human Protein Subcellular Location with Multiple Networks

Chen, L., & Wang, R. (2022) · Current Proteomics · doi:10.2174/1570164619666220531113704 · Cited by 14

Prediction of interactiveness between small molecules and enzymes by combining gene ontology and compound similarity

Chen, L., Qian, Z., Fen, K., & Cai, Y. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21467 · Cited by 14

An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques

Wang, S., Li, W., Fei, Y., Cao, Z., Xu, D., & Guo, H. (2019) · IEEE Access · doi:10.1109/access.2019.2907642 · Cited by 14

Some illuminating remarks on molecular genetics and genomics as well as drug development

Chou, K. (2020) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01634-z · Cited by 13

Predicting Sumoylation Site by Feature Selection Method

Cai, Y., He, J., & Lü, L. (2011) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2011.10508607 · Cited by 13

Multi-Label Learning With Fuzzy Hypergraph Regularization for Protein Subcellular Location Prediction

Chen, J., Tang, Y. Y., Chen, C. L. P., Fang, B., Lin, Y., & Shang, Z. (2014) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2014.2341111 · Cited by 13

Improve the Prediction of RNA-Binding Residues Using Structural Neighbours

Li, Q., Cao, Z., & Liu, H. (2010) · Protein and Peptide Letters · doi:10.2174/092986610790780279 · Cited by 13

TESTLoc: protein subcellular localization prediction from EST data

Shen, Y.-Q., & Burger, G. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-563 · Cited by 12

iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content

Chen, K., Stach, W., Homaeian, L., & Kurgan, L. (2010) · Amino Acids · doi:10.1007/s00726-010-0721-1 · Cited by 12

Using Compound Similarity and Functional Domain Composition for Prediction of Drug-Target Interaction Networks

Chen, L., He, Z., Huang, T., & Cai, Y. (2010) · Medicinal Chemistry · doi:10.2174/157340610793563983 · Cited by 12

Human proteins characterization with subcellular localizations

Yang, L., Lv, Y., Li, T., Zuo, Y., & Jiang, W. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.05.008 · Cited by 13

Prediction of human protein–protein interaction by a domain-based approach

Zhang, X., Jiao, X., Song, J., & Chang, S. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.02.026 · Cited by 12

Evolution of multicellularity in Metazoa: comparative analysis of the subcellular localization of proteins in Saccharomyces, Drosophila and Caenorhabditis

Hazkani‐Covo, E., Levanon, E. Y., Rotman, G., Graur, D., & Novik, A. (2004) · Cell Biology International · doi:10.1016/j.cellbi.2003.11.016 · Cited by 12

Expanding the Landscape of Chromatin Modification (CM)-Related Functional Domains and Genes in Human

Pu, S., Turinsky, A. L., Vlasblom, J., On, T., Xiong, X., Emili, A., Zhang, Z., Greenblatt, J. F., Parkinson, J., & Wodak, S. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014122 · Cited by 11

A Computational Method for the Identification of Endolysins and Autolysins

Xu, L., Liang, G., Chen, B., Tan, X., Xiang, H., & Liao, C. (2019) · Protein and Peptide Letters · doi:10.2174/0929866526666191002104735 · Cited by 11

A novel over-segmentation method for polarimetric SAR images classification

He, C., Deng, J., Xu, L., Li, S., Duan, M., & Liao, M. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6351717 · Cited by 11

A Modified SVM Classifier Based on RS in Medical Disease Prediction

Zhang, G. (2009) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2009.43 · Cited by 11

Extracting Features from Protein Sequences Using Chinese Segmentation Techniques for Subcellular Localization

Yang, Y., & Lu, B. (2005) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2005.1594931 · Cited by 11

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations

Ranganarayanan, P., Thanigesan, N., Vivek-Ananth, R. P., Valadi, J. K., & Ramakrishnan, V. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2415806 · Cited by 11

Prediction of Mitochondrial Proteins Using Discrete Wavelet Transform

Jiang, L., Li, M., Wen, Z., Wang, K., & Diao, Y. (2006) · The Protein Journal · doi:10.1007/s10930-006-9007-6 · Cited by 11

SVM Model for Amino Acid Composition Based Prediction of Mycobacterium tuberculosis

Pillai, L., Pant, B., Chauhan, U., & Pardasani, K. R. (2011) · Journal of Computer Science & Systems Biology · doi:10.4172/jcsb.1000075 · Cited by 10

A complexity-based method for predicting protein subcellular location

Zheng, X., Liu, T., & Wang, J. (2008) · Amino Acids · doi:10.1007/s00726-008-0172-0 · Cited by 10

The computational model to predict accurately inhibitory activity for inhibitors towardsCYP3A4

Xie, Z., Zhang, T., Wang, J., Chou, K., & Wei, D. (2010) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2010.09.004 · Cited by 10

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

Xiao, R.-Q., Guo, Y., Zeng, Y. H., Tan, H., Tan, H., Pu, X., & Li, M.-L. (2009) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2009.21009 · Cited by 10

Consistent prediction of GO protein localization

Spetale, F. E., Arce, D. P., Krsticevic, F., Bulacio, P., & Tapia, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-26041-z · Cited by 10

Machine learning approches and pattern recognition for spectral data.

Villmann, T., Merényi, E., & Udo Seiffert, A. (2008) · The European Symposium on Artificial Neural Networks · Cited by 10

γ-turn types prediction in proteins using the two-stage hybrid neural discriminant model

Jahandideh, S. S., Hoseini, S., Jahandideh, M., Hoseini, A., & Disfani, F. M. (2009) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2009.04.016 · Cited by 10

Prediction of Protein Function Improving Sequence Remote Alignment Search by a Fuzzy Logic Algorithm

Gómez, A., Cedano, J. A., Espadaler, J., Hermoso, A., Piñol, J., & Querol, E. (2007) · The Protein Journal · doi:10.1007/s10930-007-9116-x · Cited by 10

A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences

Lu, J.-L., Hu, X., & Donggang, H. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.09.028 · Cited by 9

Use of Chou’s 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment

Bouziane, H., & Chouarfia, A. (2020) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2019-0091 · Cited by 10

A New Multi-label Classifier for Identifying the Functional Types of Singleplex and Multiplex Antimicrobial Peptides

Zou, H. (2016) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-015-9511-7 · Cited by 9

Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts

GaoShan, K., & Feng, Y. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.06.006 · Cited by 9

In-silico screening of potential target transporters for glycyrrhetinic acid (GA) via deep learning prediction of drug-target interactions

Alkhadrawi, A. M., Wang, Y., & Li, C. (2022) · Biochemical Engineering Journal · doi:10.1016/j.bej.2022.108375 · Cited by 8

PSCL: Predicting Protein Subcellular Localization Based on Optimal Functional Domains

Wang, K., Hu, L., Shi, X., Dong, Y.-S., Li, H., & Wen, T. (2012) · Protein and Peptide Letters · doi:10.2174/092986612798472820 · Cited by 8

New Feature Vector for Apoptosis Protein Subcellular Localization Prediction

Govindan, G., & Nair, A. S. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22709-7_30 · Cited by 8

An improved range-based localization algorithm in wireless sensor network

Jian-guo, W., Wang, Z., Zhang, L., & Fei, S. (2011) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2011.6098666 · Cited by 8

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

Naik, P. K., Ranjan, P., Kesari, P., & Jain, S. (2011) · Journal of Biophysical Chemistry · doi:10.4236/jbpc.2011.22014 · Cited by 8

Double-layer clustering method to predict protein complexes based on power-law distribution and protein sublocalization

Peng, X., Wang, J., Huan, J., & Wu, F. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.01.043 · Cited by 7

Combining Support Vector Machine with Dual g-gap Dipeptides to Discriminate between Acidic and Alkaline Enzymes

Wang, X., Li, H., Gao, P., Liu, Y., & Zeng, W. (2018) · Letters in Organic Chemistry · doi:10.2174/1570178615666180925125912 · Cited by 7

PROTEIN SUBCELLULAR LOCALIZATION BASED ON PSI-BLAST AND MACHINE LEARNING

Guo, J., PU, X., Lin, Y., & Leung, H. W. H. (2006) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720006002405 · Cited by 7

Correlation of Metabolic Pathways with the Primary Structure in Acetylated Proteins

Yang, Z. R., & Chou, K. (2008) · The Open Bioinformatics Journal · doi:10.2174/1875036200802010090 · Cited by 7

Multi-label Learning for Protein Subcellular Location Prediction

Wang, X., Li, G., Liu, J., & Zhao, R.-W. (2011) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2011.36 · Cited by 6

Classification of apoptosis proteins by discriminant analysis

Çavaş, Ç. K., Nasibov, E. N., & Nasibov, E. N. (2012) · Turkish Journal of Biochemistry · doi:10.5505/tjb.2012.44153 · Cited by 6

Bioinformatics resources for pollen

Ambrosino, L., Bostan, H., Ruggieri, V., & Chiusano, M. L. (2016) · Plant Reproduction · doi:10.1007/s00497-016-0284-8 · Cited by 5

High-pass filters for spectral background suppression in airborne passive Fourier transform infrared spectrometry

Tarumi, T., Small, G. W., Combs, R. J., & Kroutil, R. T. (2003) · Analytica Chimica Acta · doi:10.1016/j.aca.2003.09.037 · Cited by 6

Predicting the state of cysteines based on sequence information

Guang, X., Guo, Y., Xiao, J., Wang, X., Sun, J., Xiong, W., & Li, M. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.09.002 · Cited by 6

Prediction of Four Kinds of Supersecondary Structures in Enzymes by Using Ensemble Classifier Based on SVM

高, 苏. (2014) · Hans Journal of Computational Biology · doi:10.12677/hjcb.2014.41001 · Cited by 6

Cooperativity of the oxidization of cysteines in globular proteins

Song, J., Li, W., & Xu, W. (2004) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.06.002 · Cited by 6

Prediction of presynaptic and postsynaptic neurotoxins by bi-layer support vector machine with multi-features

Song, C. (2012) · African Journal of Microbiology Research · doi:10.5897/ajmr11.1536 · Cited by 5

Recent Advances on Prediction of Protein Subcellular Localization

Yao, Y., Xu, H., He, P., & Dai, Q. (2015) · Mini-Reviews in Organic Chemistry · doi:10.2174/1570193x13666151218191932 · Cited by 5

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

Tahir, M., Hayat, M., Khan, S., & Chong, K. T. (2021) · IEEE Access · doi:10.1109/access.2021.3070083 · Cited by 5

Characterization of proteins in different subcellular localizations for Escherichia coli K12

Zhang, Q., Wang, S., Pan, Y., Su, D., Lu, Q., Zuo, Y., & Yang, L. (2018) · Genomics · doi:10.1016/j.ygeno.2018.07.008 · Cited by 5

Optimal QSAR Analysis of the Carcinogenic Activity of Aromatic and Heteroaromatic Amines

Fang, Y., Feng, Y., & Li, M. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200710077 · Cited by 5

SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information

Pugalenthi, G., Kandaswamy, K. K., Suganthan, P. N., Sowdhamini, R., Martinetz, T., & Kolatkar, P. R. (2010) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2010.10507369 · Cited by 5

Protein Subcellular Location Prediction Based on Pseudo Amino Acid Composition and Immune Genetic Algorithm

Zhang, T., Ding, Y., & Shao, S. (2006) · Lecture notes in computer science · doi:10.1007/11816102_57 · Cited by 5

Predicting protein subcellular location using learned distributed representations from a protein-protein networkPreprint

Pan, X., Chen, L., Liu, M., Huang, T., & Cai, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/768739 · Cited by 5

Computational Modeling of Proteins based on Cellular Automata

Madain, A., Latif, A., & Sleit, A. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070768 · Cited by 4

Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence

Zou, K., Wang, S., Wang, Z., Zou, H., & Yang, F. (2023) · Sensors · doi:10.3390/s23229014 · Cited by 4

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties

Cao, Y., Yu, C., Huang, S., Wang, S., Zuo, Y., & Yang, L. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200707150512 · Cited by 4

Prediction of melatonin excretion patterns in the rat exposed to ELF magnetic fields based on support vector machine and linear discriminant analysis

Jahandideh, S. S., & Abdolmaleki, P. (2010) · Micron · doi:10.1016/j.micron.2010.04.002 · Cited by 4

A novel approach for prediction of protein subcellular localization from sequence using Fourier analysis and support vector machines

Lei, Z., & Dai, Y. D. (2004) · Cited by 4

Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes

Li, D., Hu, X., Liu, X., Feng, Z., & Ding, C. (2016) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2016.11.014 · Cited by 4

A novel domain-based method for predicting the functional classes of proteins

Yu, X., Lin, J., Shi, T., & Li, Y. (2004) · Chinese Science Bulletin · doi:10.1007/bf03183426 · Cited by 4

MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteomePreprint

Biggar, K. K., Ruiz‐Blanco, Y. B., Charih, F., Fang, Q., Connolly, J., Frensemier, K., Adhikary, H., Li, S. S. ‐., & Green, J. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · Cited by 4

Proteome-wide Prediction of Lysine Methylation Reveals Novel Histone Marks and Outlines the Methyllysine ProteomePreprint

Biggar, K. K., Charih, F., Liu, H., Ruiz‐Blanco, Y. B., Stalker, L., Chopra, A., Connolly, J., Adhikary, H., Frensemier, K., Galka, M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/274688 · Cited by 4

Enzyme Reaction Annotation Using Cloud Techniques

Huang, C., Lin, C., Chang, C.-W., & Tang, C. Y. (2013) · BioMed Research International · doi:10.1155/2013/140237 · Cited by 3

Prediction of neurotoxins by support vector machine based on multiple feature vectors

Guang, X., Guo, Y., Wang, X., & Li, M. (2010) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-010-0044-7 · Cited by 3

Automated Interpretation of Subcellular Location Patterns from Three-Dimensional Confocal Microscopy

Zhao, T., & Murphy, R. F. (2006) · Springer eBooks · doi:10.1007/978-0-387-45524-2_47 · Cited by 3

DDAG K-TIPCAC: an ensemble method for protein subcellular localization

Rozza, A., Lombardi, G., Casiraghi, E., & Valentini, G. (2010) · Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) · Cited by 3

Identification and Expression Pattern of EZH2 in Pig Developing Fetuses

Tan, B., Hong, L., Qiao, J., Zhou, J., Xing, P., Yan, G., Zheng, E., Cai, G., Huang, S., Wu, Z., et al. (2020) · BioMed Research International · doi:10.1155/2020/5315930 · Cited by 3

TPPRED: A TOOL FOR HIERARCHICAL PREDICTION OF TRANSPORT PROTEINS USING CLUSTER OF NEURAL NETWORKS AND SEQUENCE DERIVED FEATURES

Jain, S., Ranjan, P., Sengupta, D., & Naik, P. K. (2012) · International Journal for Computational Biology · doi:10.34040/ijcb.1.1.2012.18 · Cited by 3

Prediction of Subcellular Localization of Apoptosis Proteins by Dipeptide Composition

Song, C., & Shi, F. (2010) · International Journal of Digital Content Technology and its Applications · doi:10.4156/jdcta.vol4.issue1.4 · Cited by 3

An Improved Hypertension Prediction Model Based on RS and SVM in the Three Gorges Area

Zhang, G. (2008) · doi:10.1109/csse.2008.664 · Cited by 3

Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network

Wu, Z. Y., & Chen, Y. H. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.756-759.3760 · Cited by 3

Improving the Prediction of Protein Structural Class for Low-Similarity Sequences by Incorporating Evolutionaryand Structural Information

Kong, L., Kong, L., & Jing, R. (2016) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2016.p0402 · Cited by 3

Framework to identify protein complexes based on similarity preclustering

Peng, X., Yan, X., & Wang, J. (2017) · Tsinghua Science & Technology · doi:10.1109/tst.2017.7830894 · Cited by 3

SVM model for amino acid composition based classification of HIV-1 groups

Dubey, A., Pant, B., & Adlakha, N. (2010) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1109/icbbt.2010.5478996 · Cited by 3

Recent Progresses for Computationally Identifying N6-methyladenosine Sites in Saccharomyces cerevisiae

Chou, K. (2020) · Journal of Applied Mathematics and Computation · doi:10.26855/jamc.2020.12.007 · Cited by 3

Machines for Prediction of Protein Subcellular

(2014)

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

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