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A Cellular Function for the RNA-Interference Enzyme Dicer in the Maturation of the <i>let-7</i> Small Temporal RNA

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Statistical learning theory

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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 1,947 · see citing works

A uniform system for microRNA annotation

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Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs

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Genes and Mechanisms Related to RNA Interference Regulate Expression of the Small Temporal RNAs that Control C. elegans Developmental Timing

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Cellular automata as models of complexity

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Exportin 5 is a RanGTP-dependent dsRNA-binding protein that mediates nuclear export of pre-miRNAs

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Some remarks on protein attribute prediction and pseudo amino acid composition

Chou, K.-C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.024 · Cited by 1,225 · see citing works

Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes

Chou, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth466 · Cited by 994 · see citing works

The History of the Cluster Heat Map

Wilkinson, L., & Friendly, M. L. (2009) · The American Statistician · doi:10.1198/tas.2009.0033 · Cited by 952 · see citing works

A Role for the RNase III Enzyme DCR-1 in RNA Interference and Germ Line Development in Caenorhabditis elegans

Knight, S. W., & Bass, B. (2001) · Science · doi:10.1126/science.1062039 · Cited by 844 · see citing works

Recent progress in protein subcellular location prediction

Chou, K., & Shen, H. (2007) · Analytical Biochemistry · doi:10.1016/j.ab.2007.07.006 · Cited by 769 · see citing works

iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Chen, W., Feng, P., Lin, H., & Chou, K. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1450 · Cited by 611 · see citing works

Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences

Bonnet, É., Wuyts, J., Rouzé, P., & de Peer, Y. V. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth374 · Cited by 554 · see citing works

Impacts of Bioinformatics to Medicinal Chemistry

Chou, K. (2015) · Medicinal Chemistry · doi:10.2174/1573406411666141229162834 · Cited by 518 · see citing works

Ipro54-pseknc: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

Lin, H., Deng, E.-Z., Ding, H., Chen, W., & Chou, K.-C. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1019 · Cited by 508 · see citing works

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

Jiang, P., Wu, H., Wang, W., Ma, W., Sun, X. S., & Lu, Z. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm368 · Cited by 492 · see citing works

Using functional domain composition and support vector machines for prediction of protein subcellular location

Chou, K.-C., & Cai, Y.-D. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m204161200 · Cited by 483 · see citing works

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Xue, C., Li, F., He, T., Liu, G.-P., Li, Y., & Zhang, X. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-310 · Cited by 473 · see citing works

PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition

Chen, W., Lei, T., Jin, D., Lin, H., & Chou, K. (2014) · Analytical Biochemistry · doi:10.1016/j.ab.2014.04.001 · Cited by 429 · see citing works

Some remarks on predicting multi-label attributes in molecular biosystems

Chou, K. (2013) · Molecular BioSystems · doi:10.1039/c3mb25555g · Cited by 413 · see citing works

iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

Xu, Y., Ding, J., Wu, L., & Chou, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055844 · Cited by 389 · see citing works

Inuc-pseknc: A sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

Guo, S.-H., Deng, E.-Z., Xu, L.-Q., Ding, H., Lin, H., Chen, W., & Chou, K.-C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu083 · Cited by 375 · see citing works

Subcellular location prediction of apoptosis proteins

Zhou, G., & Doctor, K. S. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10251 · Cited by 353 · see citing works

iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition

Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X., & Chou, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106691 · Cited by 306 · see citing works

Predicting anticancer peptides with Chou′s pseudo amino acid composition and investigating their mutagenicity via Ames test

Hajisharifi, Z., Piryaiee, M., Beigi, M. M., Behbahani, M., & Mohabatkar, H. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.08.037 · Cited by 294 · see citing works

iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

Lin, W., Fang, J., Xiao, X., & Chou, K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024756 · Cited by 284 · see citing works

PseAAC-General: Fast Building Various Modes of General Form of Chou’s Pseudo-Amino Acid Composition for Large-Scale Protein Datasets

Du, P.-F., Gu, S., & Jiao, Y.-S. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15033495 · Cited by 282 · see citing works

repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects

Liu, B., Liu, F., Fang, L., Wang, X., & Chou, K. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu820 · Cited by 274 · see citing works

Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses

Esmaeili, M., Mohabatkar, H., & Mohsenzadeh, S. (2009) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2009.11.016 · Cited by 271 · see citing works

Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection

Liu, B., Zhang, D., Xu, R., Xu, J., Wang, X., Chen, Q., Dong, Q., & Chou, K. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt709 · Cited by 270 · see citing works

Using subsite coupling to predict signal peptides

Chou, K.-C. (2001) · Protein Engineering, Design and Selection · doi:10.1093/protein/14.2.75 · Cited by 255 · see citing works

iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition

Liu, Z., Xiao, X., Qiu, W., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2014.12.009 · Cited by 252 · see citing works

Identification of clustered microRNAs using an ab initio prediction method

Sewer, A., Paul, N., Landgraf, P., Aravin, A. A., Pfeffer, S., Brownstein, M., Tuschl, T., van Nimwegen, E., & Zavolan, M. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-267 · Cited by 245 · see citing works

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Dehzangi, A., Heffernan, R., Sharma, A. K., Lyons, J., Paliwal, K. K., & Sattar, A. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.09.029 · Cited by 240 · see citing works

iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components

Qiu, W., Xiao, X., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15021746 · Cited by 236 · see citing works

AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties

Kandaswamy, K. K., Chou, K., Martinetz, T., Möller, S., Suganthan, P. N., Sridharan, S., & Pugalenthi, G. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.10.037 · Cited by 236 · see citing works

Prediction of Cyclin Proteins Using Chous Pseudo Amino Acid Composition

Mohabatkar, H. (2010) · Protein and Peptide Letters · doi:10.2174/092986610792231564 · Cited by 229 · see citing works

PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions

Chen, W., Zhang, X., Brooker, J., Lin, H., Zhang, L., & Chou, K. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu602 · Cited by 227 · see citing works

Human microRNA prediction through a probabilistic co-learning model of sequence and structure

Nam, J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki668 · Cited by 216 · see citing works

A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction

Sahu, S. S., & Panda, G. (2010) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2010.09.002 · Cited by 207 · see citing works

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

Chen, W., Lin, H., Feng, P., Ding, C., Zuo, Y., & Chou, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047843 · Cited by 201 · see citing works

iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition

Xu, Y., Wen, X. L., Shao, X., Deng, N.-Y., & Chou, K. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms15057594 · Cited by 199 · see citing works

MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

Wu, Y., Wei, B., Liu, H., Li, T., & Rayner, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-107 · Cited by 186 · see citing works

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

Georgiou, D. N., Karakasidis, T. E., Nieto, J. J., & Torres, Á. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.11.003 · Cited by 186 · see citing works

An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways

Zhou, G., & Deng, M. H. (1984) · Biochemical Journal · doi:10.1042/bj2220169 · Cited by 185 · see citing works

PseDNA‐Pro: DNA‐Binding Protein Identification by Combining Chou’s PseAAC and Physicochemical Distance Transformation

Liu, B., Xu, J., Fan, S., Xu, R., Zhou, J., & Wang, X. (2014) · Molecular Informatics · doi:10.1002/minf.201400025 · Cited by 184 · see citing works

A Key Driving Force in Determination of Protein Structural Classes

Chou, K. (1999) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1999.1325 · Cited by 181 · see citing works

Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier

Yousef, M., Nebozhyn, M., Shatkay, H., Kanterakis, S., Showe, L. C., & Showe, M. K. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl094 · Cited by 180 · see citing works

Prediction of Allergenic Proteins by Means of the Concept of Chou's Pseudo Amino Acid Composition and a Machine Learning Approach

Mohabatkar, H., Beigi, M. M., Abdolahi, K., & Mohsenzadeh, S. (2012) · Medicinal Chemistry · doi:10.2174/157340613804488341 · Cited by 177 · see citing works

Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou's Pseudo Amino Acid Composition and on Evolutionary Information

Nanni, L., Lumini, A., Gupta, D. K., & Garg, A. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.117 · Cited by 175 · see citing works

Graphical rules for enzyme-catalysed rate laws

Chou, K.-C., & Forsén, S. (1980) · Biochemical Journal · doi:10.1042/bj1870829 · Cited by 173 · see citing works

Computational approaches for microRNA studies: a review

Li, L., Xu, J., Yang, D., Tan, X., & Wang, H. (2009) · Mammalian Genome · doi:10.1007/s00335-009-9241-2 · Cited by 167 · see citing works

Graphic Rule for Drug Metabolism Systems

Chou, K. (2010) · Current Drug Metabolism · doi:10.2174/138920010791514261 · Cited by 164 · see citing works

Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E.

Althaus, I. W., Chou, J. J., Gonzales, A. J., Deibel, M. R., Chou, K., Kézdy, F. J., Romero, D. L., Aristoff, P. A., Tarpley, W. G., & Reusser, F. (1993) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)53227-0 · Cited by 163 · see citing works

The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein–protein interaction mechanism

Zhou, G. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.06.006 · Cited by 159 · see citing works

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition

Hayat, M., & Khan, A. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.11.017 · Cited by 157 · see citing works

Theoretical and experimental biology in one —A symposium in honour of Professor Kuo-Chen Chou’s 50th anniversary and Professor Richard Giegé’s 40th anniversary of their scientific careers

Lin, S., & Lapointe, J. (2013) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2013.64054 · Cited by 148 · see citing works

Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition

Chen, Y.-K., & Li, K.-B. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.10.033 · Cited by 147 · see citing works

MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans

Huang, T., Fan, B., Rothschild, M. F., Hu, Z., Li, K., & Zhao, S. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-341 · Cited by 147 · see citing works

Bioinformatics

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Wenxiang: a web-server for drawing wenxiang diagrams

Chou, K., Lin, W., & Xiao, X. (2011) · Natural Science · doi:10.4236/ns.2011.310111 · Cited by 118 · see citing works

Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach

Beigi, M. M., Behjati, M., & Mohabatkar, H. (2011) · Journal of Structural and Functional Genomics · doi:10.1007/s10969-011-9120-4 · Cited by 113 · see citing works

Predicting Membrane Protein Types by the LLDA Algorithm

Wang, T., Yang, J., Shen, H., & Chou, K. (2008) · Protein and Peptide Letters · doi:10.2174/092986608785849308 · Cited by 108 · see citing works

An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation

Xiao, X., Shao, S., Ding, Y., Huang, Z.-D., Chen, X., & Chou, K. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.02.008 · Cited by 96 · see citing works

Reliable prediction of Drosha processing sites improves microRNA gene prediction

Helvik, S. A., Snøve, O., & Sætrom, P. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl570 · Cited by 96 · see citing works

A probability cellular automaton model for hepatitis B viral infections

Xiao, X., Shao, S., & Chou, K. (2006) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2006.01.166 · Cited by 95 · see citing works

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 · see citing works

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 · see citing works

Molecular Science for Drug Development and Biomedicine

Zhong, W.-Z., & Zhou, S. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms151120072 · Cited by 88 · see citing works

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 · see citing works

An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition

Gupta, M. K., Niyogi, R., & Misra, M. (2013) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2013.773378 · Cited by 67 · see citing works

Prediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM)

Agarwal, S., Vaz, C., Bhattacharya, A., & Srinivasan, A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s1-s29 · Cited by 66 · see citing works

Predicting human microRNA precursors based on an optimized feature subset generated by GA–SVM

Wang, Y., Chen, X., Jiang, W., Li, L., Li, W., Yang, L., Liao, M., Lian, B., Lv, Y., Wang, S., et al. (2011) · Genomics · doi:10.1016/j.ygeno.2011.04.011 · Cited by 63 · see citing works

Ab initio identification of human microRNAs based on structure motifs

Brameier, M., & Wiuf, C. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-478 · Cited by 61 · see citing works

The pH-Triggered Conversion of the PrPc to PrPsc

Zhou, G., & Huang, R. (2013) · Current Topics in Medicinal Chemistry · doi:10.2174/15680266113139990003 · Cited by 57 · see citing works

Prediction of protein submitochondria locations based on data fusion of various features of sequences

Zakeri, P., Moshiri, B., & Sadeghi, M. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.10.026 · Cited by 53 · see citing works

Curious structure in “canonical” alanine‐based peptides

Shirley, W., & Brooks, C. L. (1997) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/(sici)1097-0134(199705)28:1 · Cited by 49 · see citing works

Predict potential drug targets from the ion channel proteins based on SVM

Huang, C. L., Zhang, R., Chen, Z., Jiang, Y., Shang, Z., Sun, P. G., Zhang, X., & Li, X. (2009) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2009.11.002 · Cited by 43 · see citing works

Imatinib induces a cytogenetic response in blast crisis or interferon failure chronic myeloid leukemia patients with e19a2 BCR-ABL transcripts

Lee, J.-J., Kim, H.-J., Kim, Y., Lee, S., Hwang, J.-Y., Yl, K., & Kim, D.-W. (2004) · Leukemia · doi:10.1038/sj.leu.2403454 · Cited by 22 · see citing works

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., & Chou, K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv458 · Cited by 723 · see citing works

iACP: a sequence-based tool for identifying anticancer peptides

Chen, W., Ding, H., Feng, P., Lin, H., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.7815 · Cited by 434 · see citing works

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

Chen, Z., Zhao, P., Li, F., Marquez‐Lago, T. T., Leier, A., Revote, J., Zhu, Y. L., Powell, D., Akutsu, T., Webb, G. I., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz041 · Cited by 418 · see citing works

iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition

Chen, W., Feng, P., Ding, H., Lin, H., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.08.021 · Cited by 350 · see citing works

iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC

Liu, B., Yang, F., Huang, D.-S., & Chou, K. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx579 · Cited by 333 · see citing works

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.01.020 · Cited by 322 · see citing works

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches

Liu, B. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx165 · Cited by 322 · see citing works

Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences

Chen, W., Lin, H., & Chou, K. (2015) · Molecular BioSystems · doi:10.1039/c5mb00155b · Cited by 312 · see citing works

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

Alghamdi, M., Al-Mallah, M. H., Keteyian, S. J., Brawner, C. A., Ehrman, J. K., & Sakr, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179805 · Cited by 303 · see citing works

iRNA-PseU: Identifying RNA pseudouridine sites

Chen, W., Tang, H., Ye, J., Lin, H., & Chou, K.-C. (2016) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2016.37 · Cited by 291 · see citing works

Finding the Best Classification Threshold in Imbalanced Classification

Zou, Q., Xie, S., Lin, Z., Wu, M., & Ju, Y. (2016) · Big Data Research · doi:10.1016/j.bdr.2015.12.001 · Cited by 288 · see citing works

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

Feng, P., Ding, H., Yang, H. Q., Chen, W., Lin, H., & Chou, K. (2017) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2017.03.006 · Cited by 288 · see citing works

iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.04.011 · Cited by 275 · see citing works

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.12.009 · Cited by 270 · see citing works

BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models

Li, H., Pang, Y., & Liu, B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab829 · Cited by 271 · see citing works

pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties

Liu, Z., Xiao, X., Yu, D., Jia, J., Qiu, W., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.12.017 · Cited by 257 · see citing works

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

Liu, B., Wu, H., & Chou, K. (2017) · Natural Science · doi:10.4236/ns.2017.94007 · Cited by 252 · see citing works

Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

Chen, X., Yan, C. C., Luo, C. L., Ji, W., Zhang, Y., & Dai, Q. (2015) · Scientific Reports · doi:10.1038/srep11338 · Cited by 241 · see citing works

Similarity computation strategies in the microRNA-disease network: a survey

Zou, Q., Li, J., Li, S., Zeng, X., & Wang, G. (2015) · Briefings in Functional Genomics · doi:10.1093/bfgp/elv024 · Cited by 238 · see citing works

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function

Liu, B., Yang, F., & Chou, K. (2017) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2017.04.008 · Cited by 238 · see citing works

iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

Chen, W., Feng, P., Yang, H. Q., Ding, H., Lin, H., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.13758 · Cited by 229 · see citing works

iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework

Liu, B., Long, R., & Chou, K. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw186 · Cited by 209 · see citing works

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

Qiu, W., Jiang, S., Xu, Z., Xiao, X., & Chou, K. (2017) · Oncotarget · doi:10.18632/oncotarget.17104 · Cited by 207 · see citing works

iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition

Chen, W., Ding, H., Zhou, X., Lin, H., & Chou, K. (2018) · Analytical Biochemistry · doi:10.1016/j.ab.2018.09.002 · Cited by 199 · see citing works

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC

Jia, J., Zhang, L., Liu, Z., Xiao, X., & Chou, K. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw387 · Cited by 192 · see citing works

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Wei, L., Chen, H., & Su, R. (2018) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2018.07.004 · Cited by 179 · see citing works

A learning-based framework for miRNA-disease association identification using neural networks

Peng, J., Hui, W., Li, Q., Chen, B., Hao, J., Jiang, Q., Shang, X., & Wei, Z. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz254 · Cited by 180 · see citing works

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.9148 · Cited by 176 · see citing works

iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

Zhang, C.-J., Tang, H., Li, W., Lin, H., Chen, W., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.11975 · Cited by 176 · see citing works

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier

Qiu, W., Sun, B., Xiao, X., Xu, Z., Jia, J., & Chou, K. (2017) · Genomics · doi:10.1016/j.ygeno.2017.10.008 · Cited by 172 · see citing works

Classification of membrane protein types using Voting Feature Interval in combination with Chou׳s Pseudo Amino Acid Composition

Ali, F., & Hayat, M. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.07.034 · Cited by 160 · see citing works

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC

Qiu, W., Sun, B., Xiao, X., Xu, Z., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.10027 · Cited by 160 · see citing works

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

Qiu, W., Sun, B., Xiao, X., Xu, D. H., & Chou, K. (2016) · Molecular Informatics · doi:10.1002/minf.201600010 · Cited by 155 · see citing works

repRNA: a web server for generating various feature vectors of RNA sequences

Liu, B., Liu, F., Fang, L., Wang, X., & Chou, K. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1078-7 · Cited by 152 · see citing works

iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier

Qiu, W., Xiao, X., Xu, Z., & Chou, K. (2016) · Oncotarget · doi:10.18632/oncotarget.9987 · Cited by 150 · see citing works

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

Jia, J., Liu, Z., Xiao, X., Liu, B., & Chou, K. (2015) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2015.1095116 · Cited by 145 · see citing works

Predicting lncRNA–miRNA interactions based on logistic matrix factorization with neighborhood regularized

Liu, H., Ren, G., Chen, H., Liu, Q., Yang, Y., & Zhao, Q. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105261 · Cited by 141 · see citing works

CPPred: coding potential prediction based on the global description of RNA sequence

Tong, X., & Liu, S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz087 · Cited by 140 · see citing works

iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples

Kabir, M., & Hayat, M. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1108-5 · Cited by 133 · see citing works

iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC

Khan, Y. D., Rasool, N., Hussain, W., Khan, S. A., & Chou, K. (2018) · Analytical Biochemistry · doi:10.1016/j.ab.2018.04.021 · Cited by 129 · see citing works

Constructing prediction models from expression profiles for large scale lncRNA–miRNA interaction profiling

Huang, Y., Chan, K. C. C., & You, Z. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx672 · Cited by 128 · see citing works

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation

Liu, B., Wang, S., & Wang, X. (2015) · Scientific Reports · doi:10.1038/srep15479 · Cited by 124 · see citing works

Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer

Zhou, M., Sun, Y., Sun, Y., Xu, W., Zhang, Z., Zhao, H., Zhong, Z., & Sun, J. (2016) · Oncotarget · doi:10.18632/oncotarget.8653 · Cited by 124 · see citing works

MethyRNA: a web server for identification of N6-methyladenosine sites

Chen, W., Tang, H., & Lin, H. (2016) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2016.1157761 · Cited by 122 · see citing works

Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods

Liu, B., Wu, H., Zhang, D., Wang, X., & Chou, K. (2017) · Oncotarget · doi:10.18632/oncotarget.14524 · Cited by 122 · see citing works

Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression

Zhou, S., Wang, S., Wu, Q., Azim, R., & Wen, L. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2020.107200 · Cited by 121 · see citing works

Using deformation energy to analyze nucleosome positioning in genomes

Chen, W., Feng, P., Ding, H., Lin, H., & Chou, K. (2015) · Genomics · doi:10.1016/j.ygeno.2015.12.005 · Cited by 118 · see citing works

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

Huang, Y., You, Z., Gao, X., Wong, L., & Wang, L. (2015) · BioMed Research International · doi:10.1155/2015/902198 · Cited by 119 · see citing works

iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

Arif, M., Hayat, M., & Jan, Z. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.01.008 · Cited by 114 · see citing works

iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC

Jia, J., Li, X., Qiu, W., Xiao, X., & Chou, K. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.10.021 · Cited by 114 · see citing works

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 · see citing works

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018

Thamrin, S. A., Arsyad, D. S., Kuswanto, H., Lawi, A., & Nasir, S. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.669155 · Cited by 100 · see citing works

Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC

Ju, Z., Cao, J., & Gu, H. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.02.020 · Cited by 98 · see citing works

Prediction of N-linked glycosylation sites using position relative features and statistical moments

Akmal, M. A., Rasool, N., & Khan, Y. D. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181966 · Cited by 98 · see citing works

iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features

Chowdhury, S. Y., Shatabda, S., & Dehzangi, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-14945-1 · Cited by 98 · see citing works

A Review of DNA-binding Proteins Prediction Methods

Qu, K., Wei, L., & Zou, Q. (2018) · Current Bioinformatics · doi:10.2174/1574893614666181212102030 · Cited by 98 · see citing works

Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique

Wei, L., Liao, M., Gao, X. H., & Zou, Q. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2450233 · Cited by 97 · see citing works

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 · see citing works

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 · see citing works

Overlapping Community Detection based on Network Decomposition

Ding, Z., Zhang, X., Sun, D., & Luo, B. (2016) · Scientific Reports · doi:10.1038/srep24115 · Cited by 94 · see citing works

Progresses in Predicting Post-translational Modification

Chou, K. (2019) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-019-09893-5 · Cited by 86 · see citing works

pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC

Cheng, X., Xiao, X., & Chou, K. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.09.005 · Cited by 83 · see citing works

PPUS: a web server to predict PUS-specific pseudouridine sites

Li, Y.-H., Zhang, G., & Cui, Q. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv366 · Cited by 82 · see citing works

Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou's General PseAAC

Sharma, R., Dehzangi, A., Lyons, J., Paliwal, K. K., Tsunoda, T., & Sharma, A. K. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2500186 · Cited by 82 · see citing works

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties

Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T., & Shoombuatong, W. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms21010075 · Cited by 82 · see citing works

Relapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinoma

Zhou, M., Xu, W., Yue, X., Zhao, H., Wang, Z., Shi, H., Cheng, L., & Sun, J. (2016) · Oncotarget · doi:10.18632/oncotarget.8825 · Cited by 81 · see citing works

Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning

Liu, B., Wang, S., Dong, Q., Li, S., & Liu, X. (2016) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2016.2555951 · Cited by 80 · see citing works

Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods

Zhai, X., Chen, M., & Lu, W. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.04.031 · Cited by 80 · see citing works

Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

Waris, M. A., Ahmad, K., Kabir, M., & Hayat, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.025 · Cited by 80 · see citing works

Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia

Cai, L., Huang, T., Su, J., Zhang, X., Chen, W., Zhang, F., He, L., & Chou, K. (2018) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2018.05.026 · Cited by 79 · see citing works

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 · see citing works

Identifying anticancer peptides by using improved hybrid compositions

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

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains

Krishnan, S. M. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.02.008 · Cited by 74 · see citing works

Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

Qiu, W., Li, S., Cui, X., Yu, Z., Wang, M., Du, J., Peng, Y., & Yu, B. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.04.026 · Cited by 74 · see citing works

Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh

Khan, J. R., Chowdhury, S., Islam, H., & Raheem, E. (2021) · Journal of Data Science · doi:10.6339/jds.201901_17(1).0009 · Cited by 74 · see citing works

Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis

Liu, B., Chen, J., & Wang, X. (2015) · Molecular Genetics and Genomics · doi:10.1007/s00438-015-1044-4 · Cited by 71 · see citing works

miRNA-dis: microRNA precursor identification based on distance structure status pairs

Liu, B., Fang, L., Chen, J., Liu, F., & Wang, X. (2015) · Molecular BioSystems · doi:10.1039/c5mb00050e · Cited by 70 · see citing works

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 · see citing works

iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition

Tahir, M., Hayat, M., & Khan, S. A. (2018) · Molecular Genetics and Genomics · doi:10.1007/s00438-018-1498-2 · Cited by 67 · see citing works

CrossNorm: a novel normalization strategy for microarray data in cancers

Cheng, L., Lo, L.-Y., Tang, N. L. S., Wang, D., & Leung, K. (2016) · Scientific Reports · doi:10.1038/srep18898 · Cited by 67 · see citing works

Improving tRNAscan‐SE Annotation Results via Ensemble Classifiers

Zou, Q., Guo, J., Ju, Y., Wu, M., Zeng, X., & Hong, Z. (2015) · Molecular Informatics · doi:10.1002/minf.201500031 · Cited by 65 · see citing works

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Dao, F., Yang, H. Q., Su, Z.-D., Yang, W., Wu, Y., Hui, D., Chen, W., Tang, H., & Lin, H. (2017) · Molecules · doi:10.3390/molecules22071057 · Cited by 64 · see citing works

Synergy evaluation by a pathway–pathway interaction network: a new way to predict drug combination

Chen, D., Zhang, H., Lu, P., Liu, X., & Cao, H. (2015) · Molecular BioSystems · doi:10.1039/c5mb00599j · Cited by 64 · see citing works

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 · see citing works

iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components

Zhang, L., & Kong, L. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.12.025 · Cited by 62 · see citing works

iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters

Amin, R., Rahman, C. R., Ahmed, S., Sifat, M. H. R., Liton, M. N. K., Rahman, M. M., Khan, M. Z. H., & Shatabda, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa609 · Cited by 61 · see citing works

pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

Chou, K., Cheng, X., & Xiao, X. (2018) · Medicinal Chemistry · doi:10.2174/1573406415666181218102517 · Cited by 59 · see citing works

Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data

Jabeen, A., Ahmad, N., & Raza, K. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_6 · Cited by 57 · see citing works

iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions

Chen, J., Wang, X., & Liu, B. (2016) · Scientific Reports · doi:10.1038/srep19062 · Cited by 58 · see citing works

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 · see citing works

Survey of Natural Language Processing Techniques in Bioinformatics

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Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions

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Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features

Mei, J., & Zhao, J. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.03.034 · Cited by 57 · see citing works

Diagnosis of patients with chronic kidney disease by using two fuzzy classifiers

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RNA-MethylPred: A high-accuracy predictor to identify N6-methyladenosine in RNA

Jia, C., Zhang, J., & Gu, W.-Z. (2016) · Analytical Biochemistry · doi:10.1016/j.ab.2016.06.012 · Cited by 57 · see citing works

Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition

Tahir, M., Hayat, M., & Kabir, M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.008 · Cited by 55 · see citing works

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC

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MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components

Ahmad, J., & Hayat, M. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.12.017 · Cited by 56 · see citing works

CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

Niu, M., Zhang, J., Li, Y., Wang, C., Liu, Z.-Q., Ding, H., Zou, Q., & Ma, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.028 · Cited by 53 · see citing works

Neuromodulation of the Feedforward Dentate Gyrus-CA3 Microcircuit

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RESEARCH ARTICLE Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

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iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

Liu, B., Fang, L., Liu, F., Wang, X., & Chou, K. (2015) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2015.1014422 · Cited by 148 · see citing works

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

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Classification of Real and Pseudo miRNA Precursors Using Local Structure-Sequence Features and Flexible Flexible Neural Tree

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Identification of microRNA precursors using reduced and hybrid features

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