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Current Topics in Medicinal Chemistry


ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

Author(s): Ruirui Liang, Jiayang Xie, Chi Zhang, Mengying Zhang, Hai Huang, Haizhong Huo*, Xin Cao* and Bing Niu*

Volume 19, Issue 25, 2019

Page: [2301 - 2317] Pages: 17

DOI: 10.2174/1568026619666191016155543

Price: $65


In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Keywords: Big data, Machine learning, Next generation sequencing, High-through sequence, Support vector machine, Naïve Bayes classifier, Artifical neural work, Ensemble learning, Adaboost, bagging.

Zou, Q. Latest machine learning techniques for biomedicine and bioinformatics. Curr. Bioinform., 2019, 14(3), 176-177.
Liu, L.; Wang, H. The recent applications and developments of bioinformatics and omics technologies in traditional chinese medicine. Curr. Bioinform., 2019, 14(3), 200-210.
Chen, W.; Feng, P-M.; Lin, H.; Chou, K-C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res., 2013, 41(6)e68
[] [PMID: 23303794]
Feng, P-M.; Chen, W.; Lin, H.; Chou, K-C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442(1), 118-125.
[] [PMID: 23756733]
Lin, H.; Deng, E-Z.; Ding, H.; Chen, W.; Chou, K-C. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res., 2014, 42(21), 12961-12972.
[] [PMID: 25361964]
Chen, W.; Feng, P-M.; Deng, E-Z.; Lin, H.; Chou, K-C. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem., 2014, 462, 76-83.
[] [PMID: 25016190]
Ding, H.; Deng, E-Z.; Yuan, L-F.; Liu, L.; Lin, H.; Chen, W.; Chou, K-C. iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res. Int., 2014.2014286419
[] [PMID: 24991545]
Liu, B.; Fang, L.; Wang, S.; Wang, X.; Li, H.; Chou, K-C. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J. Theor. Biol., 2015, 385, 153-159.
[] [PMID: 26362104]
Liu, Z.; Xiao, X.; Qiu, W-R.; Chou, K-C. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal. Biochem., 2015, 474, 69-77.
[] [PMID: 25596338]
Xiao, X.; Min, J-L.; Lin, W-Z.; Liu, Z.; Cheng, X.; Chou, K-C. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn., 2015, 33(10), 2221-2233.
[] [PMID: 25513722]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal. Biochem., 2016, 497, 48-56.
[] [PMID: 26723495]
Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K-C. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics, 2016, 32(3), 362-369.
[] [PMID: 26476782]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K-C. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget, 2017, 8(3), 4208-4217.
[] [PMID: 27926534]
Chen, W.; Ding, H.; Zhou, X.; Lin, H.; Chou, K-C. iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal. Biochem., 2018, 561-562, 59-65.
[] [PMID: 30201554]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Jia, J-H.; Chou, K-C. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics, 2018, 110(5), 239-246.
[] [PMID: 29107015]
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K-C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics, 2019, 111(1), 96-102.
[] [PMID: 29360500]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem., 2019, 568, 14-23.
[] [PMID: 30593778]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J. Theor. Biol., 2019, 468, 1-11.
[] [PMID: 30768975]
Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K-C. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J. Theor. Biol., 2019, 460, 195-203.
[] [PMID: 30312687]
Khan, Y.D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S.A.; Chou, K-C. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J. Theor. Biol., 2019, 463, 47-55.
[] [PMID: 30550863]
Lu, Y.; Wang, S.; Wang, J.; Zhou, G.; Zhang, Q.; Zhou, X.; Niu, B.; Chen, Q.; Chou, K-C. An epidemic avian influenza prediction model based on google trends. Lett. Org. Chem., 2019, 16(4), 303-310.
Khan, Y.D.; Batool, A.; Rasool, N.; Khan, S.A.; Chou, K-C. Prediction of nitrosocysteine sites using position and composition variant features. Lett. Org. Chem., 2019, 16(4), 283-293.
Cheng, X.; Xiao, X.; Chou, K-C. pLoc_bal-mPlant: Predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr. Pharm. Des., 2018, 24(34), 4013-4022.
[] [PMID: 30451108]
Li, J-X.; Wang, S-Q.; Du, Q-S.; Wei, H.; Li, X-M.; Meng, J-Z.; Wang, Q-Y.; Xie, N-Z.; Huang, R-B.; Chou, K-C. Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from bacillus deramificans. Curr. Pharm. Des., 2018, 24(34), 4023-4033.
[] [PMID: 30421671]
Ghauri, A.W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K-C. pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into chou’s general PseAAC. Curr. Pharm. Des., 2018, 24(34), 4034-4043.
[] [PMID: 30479209]
Chou, K-C.; Cheng, X.; Xiao, X. pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med. Chem., 2019, 15(5), 472-485.
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K-C. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics, 2018, 111(4), 886-892.
[]] [PMID: 29842950]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K-C. pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med. Chem., 2019, 15(5), 496-509.
Chou, K-C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol., 2011, 273(1), 236-247.
[] [PMID: 21168420]
Chou, K-C. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr. Med. Chem., 2019. Epub ahead of print
[] [PMID: 31060481]
Fritsche, L.G.; Gruber, S.B.; Wu, Z.; Schmidt, E.M.; Zawistowski, M.; Moser, S.E.; Blanc, V.M.; Brummett, C.M.; Kheterpal, S.; Abecasis, G.R.; Mukherjee, B. Association of polygenic risk scores for multiple cancers in a phenome-wide study: Results from the michigan genomics initiative. Am. J. Hum. Genet., 2018, 102(6), 1048-1061.
[] [PMID: 29779563]
Cordero, F.; Beccuti, M.; Donatelli, S.; Calogero, R.A. Large disclosing the nature of computational tools for the analysis of next generation sequencing data. Curr. Top. Med. Chem., 2012, 12(12), 1320-1330.
[] [PMID: 22690679]
Li, H.; Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics, 2010, 26(5), 589-595.
[] [PMID: 20080505]
Langmead, B.; Schatz, M.C.; Lin, J.; Pop, M.; Salzberg, S.L. Searching for SNPs with cloud computing. Genome Biol., 2009, 10(11), R134.
[] [PMID: 19930550]
Ning, Z.; Cox, A.J.; Mullikin, J.C. SSAHA: a fast search method for large DNA databases. Genome Res., 2001, 11(10), 1725-1729.
[] [PMID: 11591649]
Li, H.; Ruan, J.; Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res., 2008, 18(11), 1851-1858.
[] [PMID: 18714091]
Li, R.; Yu, C.; Li, Y.; Lam, T-W.; Yiu, S-M.; Kristiansen, K.; Wang, J. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 2009, 25(15), 1966-1967.
[] [PMID: 19497933]
McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; DePristo, M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res., 2010, 20(9), 1297-1303.
[] [PMID: 20644199]
Cibulskis, K.; Lawrence, M.S.; Carter, S.L.; Sivachenko, A.; Jaffe, D.; Sougnez, C.; Gabriel, S.; Meyerson, M.; Lander, E.S.; Getz, G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol., 2013, 31(3), 213-219.
[] [PMID: 23396013]
Wang, J.; Mullighan, C.G.; Easton, J.; Roberts, S.; Heatley, S.L.; Ma, J.; Rusch, M.C.; Chen, K.; Harris, C.C.; Ding, L.; Holmfeldt, L.; Payne-Turner, D.; Fan, X.; Wei, L.; Zhao, D.; Obenauer, J.C.; Naeve, C.; Mardis, E.R.; Wilson, R.K.; Downing, J.R.; Zhang, J. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods, 2011, 8(8), 652-654.
[] [PMID: 21666668]
Fromer, M.; Moran, J.L.; Chambert, K.; Banks, E.; Bergen, S.E.; Ruderfer, D.M.; Handsaker, R.E.; McCarroll, S.A.; O’Donovan, M.C.; Owen, M.J.; Kirov, G.; Sullivan, P.F.; Hultman, C.M.; Sklar, P.; Purcell, S.M. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am. J. Hum. Genet., 2012, 91(4), 597-607.
[] [PMID: 23040492]
Trapnell, C.; Williams, B.A.; Pertea, G.; Mortazavi, A.; Kwan, G.; van Baren, M.J.; Salzberg, S.L.; Wold, B.J.; Pachter, L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 2010, 28(5), 511-515.
[] [PMID: 20436464]
Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1), 139-140.
[] [PMID: 19910308]
Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol., 2010, 11(10), R106.
[] [PMID: 20979621]
Jia, W.; Qiu, K.; He, M.; Song, P.; Zhou, Q.; Zhou, F.; Yu, Y.; Zhu, D.; Nickerson, M.L.; Wan, S.; Liao, X.; Zhu, X.; Peng, S.; Li, Y.; Wang, J.; Guo, G. SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol., 2013, 14(2), R12.
[] [PMID: 23409703]
Kim, D.; Pertea, G.; Trapnell, C.; Pimentel, H.; Kelley, R.; Salzberg, S.L. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol., 2013, 14(4), R36.
[] [PMID: 23618408]
McPherson, A.; Hormozdiari, F.; Zayed, A.; Giuliany, R.; Ha, G.; Sun, M.G.F.; Griffith, M.; Heravi Moussavi, A.; Senz, J.; Melnyk, N.; Pacheco, M.; Marra, M.A.; Hirst, M.; Nielsen, T.O.; Sahinalp, S.C.; Huntsman, D.; Shah, S.P. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLOS Comput. Biol., 2011, 7(5)e1001138
[] [PMID: 21625565]
Liao, Z.; Li, D.; Wang, X.; Li, L.; Zou, Q. Cancer Diagnosis Through IsomiR Expression with Machine Learning Method. Curr. Bioinform., 2018, 13(1), 57-63.
Langley, P. Elements of machine learning; Morgan Kaufmann Publishers Inc., 1995.
Dietterich, T.G. Machine-learning research - Four current directions. AI Mag., 1997, 18(4), 97-136.
Alpaydin, E. Introduction to Machine Learning (Adaptive Computation and Machine Learning), 3rd Ed.; MIT Press: Cambridge, 2004, p. 28.
Chen, L.; Song, J. Network mining and machine learning methods of the analysis of the large-scale data in biology, medicine and pharmacy. Curr. Bioinform., 2018, 13(1), 2-2.
Zhang, X.; Acencio, M.L.; Lemke, N. Predicting essential genes and proteins based on machine learning and network topological features: a comprehensive review. Front. Physiol., 2016, 7.
Oxenoid, K.; Dong, Y.; Cao, C.; Cui, T.; Sancak, Y.; Markhard, A.L.; Grabarek, Z.; Kong, L.; Liu, Z.; Ouyang, B.; Cong, Y.; Mootha, V.K.; Chou, J.J. Architecture of the mitochondrial calcium uniporter. Nature, 2016, 533(7602), 269-273.
[] [PMID: 27135929]
Dev, J.; Park, D.; Fu, Q.; Chen, J.; Ha, H.J.; Ghantous, F.; Herrmann, T.; Chang, W.; Liu, Z.; Frey, G.; Seaman, M.S.; Chen, B.; Chou, J.J. Structural basis for membrane anchoring of HIV-1 envelope spike. Science, 2016, 353(6295), 172-175.
[] [PMID: 27338706]
Bjorndahl, T.C.; Zhou, G-P.; Liu, X.; Perez-Pineiro, R.; Semenchenko, V.; Saleem, F.; Acharya, S.; Bujold, A.; Sobsey, C.A.; Wishart, D.S. Detailed biophysical characterization of the acid-induced PrP(c) to PrP(β) conversion process. Biochemistry, 2011, 50(7), 1162-1173.
[] [PMID: 21189021]
Peng, L-X.; Liu, X-H.; Lu, B.; Liao, S-M.; Zhou, F.; Huang, J-M.; Chen, D.; Troy Ii, F.A.; Zhou, G-P.; Huang, R-B. The Inhibition of Polysialyltranseferase ST8SiaIV through Heparin binding to Polysialyltransferase Domain (PSTD). Med. Chem., 2019, 15(5), 486-495.
Zhou, G-P.; Chen, D.; Liao, S.; Huang, R-B. Recent progresses in studying helix-helix interactions in proteins by incorporating the wenxiang diagram into the NMR spectroscopy. Curr. Top. Med. Chem., 2016, 16(6), 581-590.
[] [PMID: 26286215]
Zhou, G-P. The structural determinations of the leucine zipper coiled-coil domains of the cGMP-dependent protein kinase Iα and its interaction with the myosin binding subunit of the myosin light chains phosphase. Protein Pept. Lett., 2011, 18(10), 966-978.
[] [PMID: 21592084]
Schnell, J.R.; Chou, J.J. Structure and mechanism of the M2 proton channel of influenza A virus. Nature, 2008, 451(7178), 591-595.
[] [PMID: 18235503]
Berardi, M.J.; Shih, W.M.; Harrison, S.C.; Chou, J.J. Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature, 2011, 476(7358), 109-113.
[] [PMID: 21785437]
Chou, J.J.; Li, S.; Klee, C.B.; Bax, A. Solution structure of Ca(2+)-calmodulin reveals flexible hand-like properties of its domains. Nat. Struct. Biol., 2001, 8(11), 990-997.
[] [PMID: 11685248]
OuYang, B.; Xie, S.; Berardi, M.J.; Zhao, X.; Dev, J.; Yu, W.; Sun, B.; Chou, J.J. Unusual architecture of the p7 channel from hepatitis C virus. Nature, 2013, 498(7455), 521-525.
[] [PMID: 23739335]
Wang, J.; Pielak, R.M.; McClintock, M.A.; Chou, J.J. Solution structure and functional analysis of the influenza B proton channel. Nat. Struct. Mol. Biol., 2009, 16(12), 1267-1271.
[] [PMID: 19898475]
Fu, Q.; Fu, T-M.; Cruz, A.C.; Sengupta, P.; Thomas, S.K.; Wang, S.; Siegel, R.M.; Wu, H.; Chou, J.J. Structural basis and functional role of intramembrane trimerization of the Fas/CD95 death receptor. Mol. Cell, 2016, 61(4), 602-613.
[] [PMID: 26853147]
Call, M.E.; Wucherpfennig, K.W.; Chou, J.J. The structural basis for intramembrane assembly of an activating immunoreceptor complex. Nat. Immunol., 2010, 11(11), 1023-1029.
[] [PMID: 20890284]
Brüschweiler, S.; Yang, Q.; Run, C.; Chou, J.J. Substrate-modulated ADP/ATP-transporter dynamics revealed by NMR relaxation dispersion. Nat. Struct. Mol. Biol., 2015, 22(8), 636-641.
[] [PMID: 26167881]
Cao, C.; Wang, S.; Cui, T.; Su, X-C.; Chou, J.J. Ion and inhibitor binding of the double-ring ion selectivity filter of the mitochondrial calcium uniporter. Proc. Natl. Acad. Sci. USA, 2017, 114(14), E2846-E2851.
[] [PMID: 28325874]
Piai, A.; Dev, J.; Fu, Q.; Chou, J.J. Stability and Water Accessibility of the Trimeric Membrane Anchors of the HIV-1 Envelope Spikes. J. Am. Chem. Soc., 2017, 139(51), 18432-18435.
[] [PMID: 29193965]
Pan, L.; Fu, T-M.; Zhao, W.; Zhao, L.; Chen, W.; Qiu, C.; Liu, W.; Liu, Z.; Piai, A.; Fu, Q.; Chen, S.; Wu, H.; Chou, J.J. Higher-order clustering of the transmembrane anchor of DR5 drives signaling. Cell, 2019, 176(6), 1477-1489.
[] [PMID: 30827683]
Schnell, J.R.; Zhou, G.P.; Zweckstetter, M.; Rigby, A.C.; Chou, J.J. Rapid and accurate structure determination of coiled-coil domains using NMR dipolar couplings: application to cGMP-dependent protein kinase Ialpha. Protein Sci., 2005, 14(9), 2421-2428.
[] [PMID: 16131665]
Chou, K.C. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J. Proteome Res., 2005, 4(5), 1681-1686.
[] [PMID: 16212421]
Chou, K.C.; Howe, W.J. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem. Biophys. Res. Commun., 2002, 292(3), 702-708.
[] [PMID: 11922623]
Huang, R-B.; Cheng, D.; Liao, S-M.; Lu, B.; Wang, Q-Y.; Xie, N-Z.; Troy Ii, F.A.; Zhou, G-P. The intrinsic relationship between structure and function of the sialyltransferase ST8Sia family members. Curr. Top. Med. Chem., 2017, 17(21), 2359-2369.
[] [PMID: 28413949]
Zhou, G-P.; Huang, R-B.; Troy, F.A. II 3D structural conformation and functional domains of polysialyltransferase ST8Sia IV required for polysialylation of neural cell adhesion molecules. Protein Pept. Lett., 2015, 22(2), 137-148.
[] [PMID: 25329332]
Chou, K.C. Modeling the tertiary structure of human cathepsin-E. Biochem. Biophys. Res. Commun., 2005, 331(1), 56-60.
[] [PMID: 15845357]
Chou, K.C. Insights from modeling the 3D structure of DNA-CBF3b complex. J. Proteome Res., 2005, 4(5), 1657-1660.
[] [PMID: 16212418]
Wang, S-Q.; Du, Q-S.; Chou, K-C. Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem. Biophys. Res. Commun., 2007, 354(3), 634-640.
[] [PMID: 17266937]
Wang, S-Q.; Du, Q-S.; Huang, R-B.; Zhang, D-W.; Chou, K-C. Insights from investigating the interaction of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus. Biochem. Biophys. Res. Commun., 2009, 386(3), 432-436.
[] [PMID: 19523442]
Li, X-B.; Wang, S-Q.; Xu, W-R.; Wang, R-L.; Chou, K-C. Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method. PLoS One, 2011, 6(11)e28111
[] [PMID: 22140516]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol., 2016, 394, 223-230.
[] [PMID: 26807806]
Chou, K-C. Impacts of bioinformatics to medicinal chemistry. Med. Chem., 2015, 11(3), 218-234.
[] [PMID: 25548930]
Xie, H-L.; Fu, L.; Nie, X-D. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou’s PseAAC. Protein Eng. Des. Sel., 2013, 26(11), 735-742.
[] [PMID: 24048266]
Jia, C.; Lin, X.; Wang, Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou’s pseudo amino acid composition. Int. J. Mol. Sci., 2014, 15(6), 10410-10423.
[] [PMID: 24918295]
Xu, Y.; Wen, X.; Shao, X-J.; Deng, N-Y.; Chou, K-C. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int. J. Mol. Sci., 2014, 15(5), 7594-7610.
[] [PMID: 24857907]
Qiu, W-R.; Xiao, X.; Lin, W-Z.; Chou, K-C. iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. J. Biomol. Struct. Dyn., 2015, 33(8), 1731-1742.
[] [PMID: 25248923]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget, 2016, 7(23), 34558-34570.
[] [PMID: 27153555]
Ju, Z.; Cao, J-Z.; Gu, H. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. J. Theor. Biol., 2016, 397, 145-150.
[] [PMID: 26908349]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Chou, K-C. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget, 2016, 7(28), 44310-44321.
[] [PMID: 27322424]
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K-C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids, 2017, 7, 155-163.
[] [PMID: 28624191]
Liu, B.; Yang, F.; Chou, K.C. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol. Ther. Nucleic Acids, 2017, 7, 267-277.
[] [PMID: 28624202]
Qiu, W-R.; Jiang, S-Y.; Sun, B-Q.; Xiao, X.; Cheng, X.; Chou, K-C. iRNA-2methyl: Identify RNA 2′-O-methylation Sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med. Chem., 2017, 13(8), 734-743.
[] [PMID: 28641529]
Kumar, V.S.; Vellaichamy, A. Sequence and structure-based characterization of ubiquitination sites in human and yeast proteins using Chou’s sample formulation. Proteins, 2019, 87(8), 646-657.
[] [PMID: 30958587]
Wang, L.; Zhang, R.; Mu, Y. Fu-SulfPred: Identification of protein S-sulfenylation sites by fusing forests via chou’s general PseAAC. J. Theor. Biol., 2019, 461, 51-58.
[] [PMID: 30365947]
Li, F.; Zhang, Y.; Purcell, A.W.; Webb, G.I.; Chou, K-C.; Lithgow, T.; Li, C.; Song, J. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics, 2019, 20(1), 112.
[] [PMID: 30841845]
Sabooh, M.F.; Iqbal, N.; Khan, M.; Khan, M.; Maqbool, H.F. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J. Theor. Biol., 2018, 452, 1-9.
[] [PMID: 29727634]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol. Biol. Rep., 2018, 45(6), 2501-2509.
[] [PMID: 30311130]
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K-C. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal. Biochem., 2018, 550, 109-116.
[] [PMID: 29704476]
Liu, D.; Li, G.; Zuo, Y. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes. Brief. Bioinform., 2018. [Epub Ahead of Print
[] [PMID: 29947743]
Tan, J-X.; Li, S-H.; Zhang, Z-M.; Chen, C-X.; Chen, W.; Tang, H.; Lin, H. Identification of hormone binding proteins based on machine learning methods. Math. Biosci. Eng., 2019, 16(4), 2466-2480.
[] [PMID: 31137222]
Tang, H.; Zhao, Y-W.; Zou, P.; Zhang, C-M.; Chen, R.; Huang, P.; Lin, H. HBPred: a tool to identify growth hormone-binding proteins. Int. J. Biol. Sci., 2018, 14(8), 957-964.
[] [PMID: 29989085]
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K-C. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theor. Biol., 2015, 377, 47-56.
[] [PMID: 25908206]
Chou, K.C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins, 2001, 43(3), 246-255.
[] [PMID: 11288174]
Zuo, Y.; Li, Y.; Chen, Y.; Li, G.; Yan, Z.; Yang, L. PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition. Bioinformatics, 2017, 33(1), 122-124.
[] [PMID: 27565583]
Chen, W.; Lei, T-Y.; Jin, D-C.; Lin, H.; Chou, K-C. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem., 2014, 456, 53-60.
[] [PMID: 24732113]
Zhou, W.; Yan, H.; Fan, X.; Hao, Q. Prediction of protein-protein interactions based on molecular interface features and the support vector machine. Curr. Bioinform., 2013, 8(1), 3-8.
Li, B-Q.; Zhang, Y-H.; Jin, M-L.; Huang, T.; Cai, Y-D. Prediction of protein-peptide interactions with a nearest neighbor algorithm. Curr. Bioinform., 2018, 13(1), 14-24.
Nemade, P.A.; Pardasani, K.R. Fuzzy support vector machine model to predict human death domain protein–protein interactions. Netw. Model. Anal. Health Inform. Bioinform., 2015, 4(1), 1-12.
Bolon-Canedo, V.; Sanchez-Marono, N.; Alonso-Betanzos, A.; Benitez, J.M.; Herrera, F. A review of microarray datasets and applied feature selection methods. Inf. Sci., 2014, 282, 111-135.
Guyon, I.; Weston, J.; Barnhill, S.; Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn., 2002, 46(1-3), 389-422.
Blum, A.L.; Langley, P. Selection of relevant features and examples in machine learning. Artif. Intell., 1997, 97(1-2), 245-271.
Zhang, R.; Nie, F.P.; Li, X.L.; Wei, X. Feature selection with multi-view data: A survey. Inf. Fusion, 2019, 50, 158-167.
Chandrashekar, G.; Sahin, F. A survey on feature selection methods. Comput. Electr. Eng., 2014, 40(1), 16-28.
Liu, B.; Wang, S.; Long, R.; Chou, K-C. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics, 2017, 33(1), 35-41.
[] [PMID: 27531102]
Gao, W.F.; Hu, L.; Zhang, P. Class-specific mutual information variation for feature selection. Pattern Recognit., 2018, 79, 328-339.
Yan, H.; Xin, S.; Ma, J.; Wang, H.; Zhang, H.; Liu, J. A three microRNA-based prognostic signature for small cell lung cancer overall survival. J. Cell. Biochem., 2018. [Epub ahead of print
[ ] [PMID: 30536412]
Chou, K-C.; Shen, H-B. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem. Biophys. Res. Commun., 2007, 357(3), 633-640.
[] [PMID: 17434148]
Wang, M.; Yang, J.; Chou, K.C. Using string kernel to predict signal peptide cleavage site based on subsite coupling model. Amino Acids, 2005, 28(4), 395-402.
[] [PMID: 15838592]
Chou, K.C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[] [PMID: 11786179]
Xu, Y.; Shao, X-J.; Wu, L-Y.; Deng, N-Y.; Chou, K-C. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ, 2013, 1e171
[] [PMID: 24109555]
Liu, B.; Yang, F.; Huang, D-S.; Chou, K-C. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34(1), 33-40.
[] [PMID: 28968797]
Zhang, C-J.; Tang, H.; Li, W-C.; Lin, H.; Chen, W.; Chou, K-C. iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget, 2016, 7(43), 69783-69793.
[] [PMID: 27626500]
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K-C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7(13), 16895-16909.
[] [PMID: 26942877]
Song, J.; Li, C.; Zheng, C.; Revote, J.; Zhang, Z.; Webb, G.I. MetalExplorer, a bioinformatics tool for the improved prediction of eight types of metal-binding sites using a random forest algorithm with two-step feature selection. Curr. Bioinform., 2017, 12(6), 480-489.
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
Strobl, C.; Malley, J.; Tutz, G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods, 2009, 14(4), 323-348.
[] [PMID: 19968396]
Cai, Z.; Xu, D.; Zhang, Q.; Zhang, J.; Ngai, S.M.; Shao, J. Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol. Biosyst., 2015, 11(3), 791-800.
[] [PMID: 25512221]
Mehan, M.R.; Nunez-Iglesias, J.; Dai, C.; Waterman, M.S.; Zhou, X.J. An integrative modular approach to systematically predict gene-phenotype associations. BMC Bioinformatics, 2010, 11(Suppl. 1), S62.
[] [PMID: 20122238]
Pang, H.; George, S.L.; Hui, K.; Tong, T. Gene selection using iterative feature elimination random forests for survival outcomes. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2012, 9(5), 1422-1431.
[] [PMID: 22547432]
Svetlichnyy, D.; Imrichova, H.; Fiers, M.; Kalender Atak, Z.; Aerts, S. Identification of high-impact cis-regulatory mutations using transcription factor specific random forest models. PLOS Comput. Biol., 2015, 11(11)e1004590
[] [PMID: 26562774]
Xiaoyan, W.; Zhenyu, W.; Kang, L. Classification and identification of differential gene expression for microarray data: improvement of the random forest method. 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE ’08), 2008, pp. 763-766.
Hsi-Che, L.; Pei-Chen, P.; Tzung-Chien, H.; Ting-Chi, Y.; Chih-Jen, L.; Chien-Yu, C.; Jen-Yin, H.; Lee-Yung, S.; Der-Cherng, L. Comparison of feature selection methods for cross-laboratory microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinforma., 2013, 10(3), 593-604.
Spinella, J.F.; Mehanna, P.; Vidal, R.; Saillour, V.; Cassart, P.; Richer, C.; Ouimet, M.; Healy, J.; Sinnett, D. SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing. BMC Genomics, 2016, 17(1), 912.
[] [PMID: 27842494]
Elfwing, S.; Uchibe, E.; Doya, K. Scaled free-energy based reinforcement learning for robust and efficient learning in high-dimensional state spaces. Front. Neurorobot., 2013, 7, 3.
[] [PMID: 23450126]
Bennet, J.; Ganaprakasam, C.A.; Arputharaj, K. A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis. Scien. W. J., 2014, 2014195470
[] [PMID: 25162043]
Fu, C.; Deng, S.; Song, Q.; Jing, L. Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma. IET Syst. Biol., 2013, 7(5), 165-169.
[] [PMID: 24067416]
Yang, D.; Parrish, R.S.; Brock, G.N. Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data. Comput. Biol. Med., 2014, 46, 1-10.
[] [PMID: 24529200]
Hongyi, P.; Yinlian, F.; Jinshan, L.; Xiang, F.; Chunfu, J. Optimal gene subset selection using the modified SFFS algorithm for tumor classification. Neural Comput. Appl., 2013, 23(6), 1531-1538.
Chakraborty, S. Bayesian binary kernel probit model for microarray based cancer classification and gene selection. Comput. Stat. Data Anal., 2009, 53(12), 4198-4209.
Saunders, C.T.; Wong, W.S.W.; Swamy, S.; Becq, J.; Murray, L.J.; Cheetham, R.K. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics, 2012, 28(14), 1811-1817.
[] [PMID: 22581179]
Liu, B.; Long, R.; Chou, K.C. iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics, 2016, 32(16), 2411-2418.
[] [PMID: 27153623]
Chou, K.C.; Shen, H.B. MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun., 2007, 360(2), 339-345.
[] [PMID: 17586467]
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D.; Chou, K.C. iPhos-PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform., 2016, 36(5-6)
[] [PMID: 28488814]
Qiu, W-R.; Sun, B-Q.; Xiao, X.; Xu, Z-C.; Chou, K-C. iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics, 2016, 32(20), 3116-3123.
[] [PMID: 27334473]
Qiu, W-R.; Xiao, X.; Xu, Z-C.; Chou, K-C. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget, 2016, 7(32), 51270-51283.
[] [PMID: 27323404]
Shen, H.B.; Chou, K.C. Using ensemble classifier to identify membrane protein types. Amino Acids, 2007, 32(4), 483-488.
[] [PMID: 17031474]
Shen, H-B.; Chou, K-C. QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information. J. Proteome Res., 2009, 8(3), 1577-1584.
[] [PMID: 19226167]
Shen, H-B.; Chou, K-C. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal. Biochem., 2009, 394(2), 269-274.
[] [PMID: 19651102]
Yang, P.; Yang, Y.H.; Zhou, B.B.; Zomaya, A.Y. A review of ensemble methods in bioinformatics. Curr. Bioinform., 2010, 5(4), 296-308.
Wang, Y.Y.; Wang, D.J.; Geng, N.; Wang, Y.Z.; Yin, Y.Q.; Jin, Y.C. Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection. Appl. Soft Comput., 2019, 77, 188-204.
Babalyan, K.; Sultanov, R.; Generozov, E.; Sharova, E.; Kostryukova, E.; Larin, A.; Kanygina, A.; Govorun, V.; Arapidi, G. LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer. PLoS One, 2018, 13(11)e0204371
[] [PMID: 30388122]
Liu, Z.P.; Liu, J.M. A integrated cancer classification method based on CIJEP. J. Comput. Theor. Nanosci., 2015, 12(9), 2041-2047.
Dudoit, S.; Fridlyand, J.; Speed, T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J. Am. Stat. Assoc., 2002, 97(457), 77-87.
Qiao, M.; Hu, Y.; Guo, Y.; Wang, Y.; Yu, J. Breast tumor classification based on a computerized breast imaging reporting and data system feature system. J. Ultrasound Med., 2018, 37(2), 403-415.
[] [PMID: 28804937]
Chou, K.C.; Forsén, S. Graphical rules for enzyme-catalysed rate laws. Biochem. J., 1980, 187(3), 829-835.
[] [PMID: 7188428]
Zhou, G.P.; Deng, M.H. An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J., 1984, 222(1), 169-176.
[] [PMID: 6477507]
Chou, K.C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem., 1989, 264(20), 12074-12079.
[PMID: 2745429]
Chou, K.C. Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys. Chem., 1990, 35(1), 1-24.
[] [PMID: 2183882]
Chou, K.C.; Forsén, S. Diffusion-controlled effects in reversible enzymatic fast reaction systems--critical spherical shell and proximity rate constant. Biophys. Chem., 1980, 12(3-4), 255-263.
[] [PMID: 7225518]
Chou, K.C.; Li, T.T.; Forsén, S. The critical spherical shell in enzymatic fast reaction systems. Biophys. Chem., 1980, 12(3-4), 265-269.
[] [PMID: 7225519]
Chou, K.C. The biological functions of low-frequency vibrations (phonons). VI. A possible dynamic mechanism of allosteric transition in antibody molecules. Biopolymers, 1987, 26(2), 285-295.
[] [PMID: 3828475]
Chou, K.C. Low-frequency collective motion in biomacromolecules and its biological functions. Biophys. Chem., 1988, 30(1), 3-48.
[] [PMID: 3046672]
Huang, J.; Liu, Z.; Ma, Q.; He, Z.; Niu, Z.; Zhang, M.; Pan, L.; Qu, X.; Yu, J.; Niu, B. Studies on the Interaction between Three Small Flavonoid Molecules and Bovine Lactoferrin. BioMed Res. Int., 2018, 20187523165
[] [PMID: 30356365]
Zhang, D.J.; Zou, L.; Zhou, X.H.; He, F.Z.; Zhang, D.J.; Zou, L.; Zhou, X.H.; He, F.Z. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access, 2018, 6, 28936-28944.
Turki, T. An empirical study of machine learning algorithms for cancer identification. Proceedings of 2018 Ieee 15th International Conference on Networking, Sensing And Control; IEEE: New York, . 2018.
Zhang, B.; He, X.; Ouyang, F.; Gu, D.; Dong, Y.; Zhang, L.; Mo, X.; Huang, W.; Tian, J.; Zhang, S. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett., 2017, 403, 21-27.
[] [PMID: 28610955]
Patel, S.; Tripathi, R.; Kumari, V.; Varadwaj, P. DeepInteract: Deep Neural Network Based Protein-Protein Interaction Prediction Tool. Curr. Bioinform., 2017, 12(6), 551-557.
Ravi, C.; Khare, N. An adaboost optimized ccfis based classification model for breast cancer detection. J. Eng. Sci. Technol., 2017, 12(6), 1446-1459.
Jaffar, M.A. Hybrid Texture based Classification of Breast Mammograms using Adaboost Classifier. Int. J. Adv. Comput. Sci. Appl., 2017, 8(5), 321-327.
Tsuji, K.; Lu, H.M.; Tan, J.K.; Kim, H.; Yoneda, K.; Tanaka, F. Automatic identification of circulating tumor cells in fluorescence microscopy images based on adaBoost. Proceedings of 2017 17th International Conference on Control, Automation And Systems; IEEE: New York, . 2017, pp. 1449-1454.
Breiman, L. Bagging predictors. Mach. Learn., 1996, 24(2), 123-140.
Markus, M.T.; Groenen, P.J.F. An introduction to the bootstrap. Psychometrika, 1998, 63(1), 97-101.
Bashir, S.; Qamar, U.; Khan, F.H. WebMAC: A web based clinical expert system. Inf. Syst. Front., 2018, 20(5), 1135-1151.
Askarzadeh, A.; Rezazadeh, A. Artificial neural network training using a new efficient optimization algorithm. Appl. Soft Comput., 2013, 13(2), 1206-1213.
Luo, S.T.; Cheng, B.W. Diagnosing breast masses in digital mammography using feature selection and ensemble methods. J. Med. Syst., 2012, 36(2), 569-577.
[] [PMID: 20703679]
Shawky, D.M.; Seddik, A.F. On the temporal effects of features on the prediction of breast cancer survivability. Curr. Bioinform., 2017, 12(4), 378-384.
Cherkassky, V. The nature of statistical learning theory EEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 1997, 87(6), 1564-1564.
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
Vapnik, V.N. An overview of statistical learning theory. IEEE Trans. Neural Netw., 1999, 10(5), 988-999.
[] [PMID: 18252602]
Du, X.; Li, X.; Li, W.; Yan, Y.; Zhang, Y. Identification and analysis of cancer diagnosis using probabilistic classification vector machines with feature selection. Curr. Bioinform., 2018, 13(6), 625-632.
Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov., 1998, 2(2), 121-167.
Bu, H.; Hao, J.; Guan, J.; Zhou, S. Predicting enhancers from multiple cell lines and tissues across different developmental stages based on svm method. Curr. Bioinform., 2018, 13(6), 655-660.
Das, S.; Meher, P.K.; Rai, A.; Bhar, L.M.; Mandal, B.N. Statistical approaches for gene selection, hub gene identification and module interaction in gene co-expression network analysis: an application to aluminum stress in soybean (Glycine max L.). PLoS One, 2017, 12(1)e0169605
[] [PMID: 28056073]
Su, W.X.; Li, Q.Z.; Zhang, L.Q.; Fan, G.L.; Wu, C.Y.; Yan, Z.H.; Zuo, Y.C. Gene expression classification using epigenetic features and DNA sequence composition in the human embryonic stem cell line H1. Gene, 2016, 592(1), 227-234.
[] [PMID: 27468948]
Zhang, S.; Han, J.; Zhong, D.; Liu, R.; Zheng, J. Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome. Comput. Biol. Chem., 2014, 52, 1-8.
[] [PMID: 25086506]
Jaison, B.; Chilambuchelvan, A.; Junaid, K. A. M. 2015.
Lv, Y.D.; Wang, Y.; Tan, Y.F.; Du, W.; Liu, K.K.; Wang, H. Pancreatic cancer biomarker detection using recursive feature elimination based on support vector machine and large margin distribution machine Proceedings of 2017 4th International Conference on Systems and Informatics, 2017, , pp. 1450-1455.
Chen, H.L.; Yang, B.; Liu, J.; Liu, D.Y. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl., 2011, 38(7), 9014-9022.
Zarzar, M.; Razak, E.; Htike, Z.Z.; Yusof, F. DNA microarray gene expression analysis for diagnosis of oral dysplasia and squamous-cell carcinoma. Adv. Sci. Lett., 2015, 21(11), 3468-3471.
Onken, M.D.; Winkler, A.E.; Kanchi, K.L.; Chalivendra, V.; Law, J.H.; Rickert, C.G.; Kallogjeri, D.; Judd, N.P.; Dunn, G.P.; Piccirillo, J.F.; Lewis, J.S., Jr; Mardis, E.R.; Uppaluri, R. A surprising cross-species conservation in the genomic landscape of mouse and human oral cancer identifies a transcriptional signature predicting metastatic disease. Clin. Cancer Res., 2014, 20(11), 2873-2884.
[] [PMID: 24668645]
Chen, Y.; Sun, J.; Huang, L-C.; Xu, H.; Zhao, Z. Classification of cancer primary sites using machine learning and somatic mutations. BioMed Res. Int., 2015, •••2015491502
[] [PMID: 26539502]
Jiang, H.; Zhao, D.; Zheng, R.; Ma, X. Construction of pancreatic cancer classifier based on SVM optimized by improved FOA. BioMed Res. Int., 2015, 2015781023
[] [PMID: 26543867]
Yang, W.; Yoshigoe, K.; Qin, X.; Liu, J.S.; Yang, J.Y.; Niemierko, A.; Deng, Y.; Liu, Y.; Dunker, A.; Chen, Z.; Wang, L.; Xu, D.; Arabnia, H.R.; Tong, W.; Yang, M. Identification of genes and pathways involved in kidney renal clear cell carcinoma. BMC Bioinformatics, 2014, 15(Suppl. 17), S2.
[] [PMID: 25559354]
Wang, Y.; Li, Y.; Wang, Q.; Lv, Y.; Wang, S.; Chen, X.; Yu, X.; Jiang, W.; Li, X. Computational identification of human long intergenic non-coding RNAs using a GA-SVM algorithm. Gene, 2014, 533(1), 94-99.
[] [PMID: 24120395]
Rezaeian, I.; Tavakoli, A.; Cavallo-Medved, D.; Porter, L.A.; Rueda, L. A novel model used to detect differential splice junctions as biomarkers in prostate cancer from RNA-Seq data. J. Biomed. Inform., 2016, 60, 422-430.
[] [PMID: 26992567]
Gálvez, J.M.; Castillo, D.; Herrera, L.J.; San Román, B.; Valenzuela, O.; Ortuño, F.M.; Rojas, I. Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series. PLoS One, 2018, 13(5)e0196836
[] [PMID: 29750795]
Wang, H.F.; Zheng, B.C.; Yoon, S.W.; Ko, H.S. A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur. J. Oper. Res., 2018, 267(2), 687-699.
Hopfield, J.J. Artificial neural networks. IEEE Circuits Devices Mag. (USA), 1988, 4(5), 3-10.
Long, H.; Wang, M.; Fu, H. Deep convolutional neural networks for predicting hydroxyproline in proteins. Curr. Bioinform., 2017, 12(3), 233-238.
Manning, T.; Sleator, R.D.; Walsh, P. Biologically inspired intelligent decision making: A commentary on the use of artificial neural networks in bioinformatics. Bioengineered, 2014, 5(2), 80-95.
[] [PMID: 24335433]
Acharya, U.R.; Vinitha Sree, S.; Mookiah, M.R.K.; Yantri, R.; Molinari, F.; Zieleźnik, W.; Małyszek-Tumidajewicz, J.; Stępień, B.; Bardales, R.H.; Witkowska, A.; Suri, J.S. Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification. Proc. Inst. Mech. Eng. H, 2013, 227(7), 788-798.
[] [PMID: 23636761]
Mariani, S.; Grassi, A.; Mendez, M.O.; Milioli, G.; Parrino, L.; Terzano, M.G.; Bianchi, A.M. EEG segmentation for improving automatic CAP detection. Clin. Neurophysiol., 2013, 124(9), 1815-1823.
[] [PMID: 23643311]
Sachdeva, J.; Kumar, V.; Gupta, I.; Khandelwal, N.; Ahuja, C.K. Segmentation, feature extraction, and multiclass brain tumor classification. J. Digit. Imaging, 2013, 26(6), 1141-1150.
[] [PMID: 23645344]
Zhao, Y.; Chen, D.; Luo, Y.; Li, H.; Deng, B.; Huang, S-B.; Chiu, T-K.; Wu, M-H.; Long, R.; Hu, H.; Zhao, X.; Yue, W.; Wang, J.; Chen, J. A microfluidic system for cell type classification based on cellular size-independent electrical properties. Lab Chip, 2013, 13(12), 2272-2277.
[] [PMID: 23640025]
Firoozpour, L.; Sadatnezhad, K.; Dehghani, S.; Pourbasheer, E.; Foroumadi, A.; Shafiee, A.; Amanlou, M. An efficient piecewise linear model for predicting activity of caspase-3 inhibitors. Daru, 2012, 20(1), 31.
[] [PMID: 23351435]
Leite, D.; Costa, P.; Gomide, F. Evolving granular neural networks from fuzzy data streams. Neural Netw., 2013, 38, 1-16.
[] [PMID: 23201554]
Nie, L.; Deng, L.; Fan, C.; Zhan, W.; Tang, Y. Prediction of protein s-sulfenylation sites using a deep belief network. Curr. Bioinform., 2018, 13(5), 461-467.
Yu, L.; Sun, X.; Tian, S.; Shi, X.; Yan, Y. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr. Bioinform., 2018, 13(3), 253-259.
Peng, L.; Peng, M.; Liao, B.; Huang, G.; Li, W.; Xie, D. The advances and challenges of deep learning application in biological big data processing. Curr. Bioinform., 2018, 13(4), 352-359.
Hou, C.S. YE integrated use of statistical-based approaches and computational intelligence techniques for tumors classification using microarray. Discrete Dyn. Nat. Soc., 2015, 2015, 1-8.
Chu, C.M.; Yao, C.T.; Chang, Y.T.; Chou, H.L.; Chou, Y.C.; Chen, K.H.; Terng, H.J.; Huang, C.S.; Lee, C.C.; Su, S.L.; Liu, Y.C.; Lin, F.G.; Wetter, T.; Chang, C.W. Gene expression profiling of colorectal tumors and normal mucosa by microarrays meta-analysis using prediction analysis of microarray, artificial neural network, classification, and regression trees. Dis. Markers, 2014, 2014634123
[] [PMID: 24959000]
Lancashire, L.J.; Rees, R.C.; Ball, G.R. Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach. Artif. Intell. Med., 2008, 43(2), 99-111.
[] [PMID: 18420392]
Hanai, T.; Hamada, H.; Okamoto, M. Application of bioinformatics for DNA microarray data to bioscience, bioengineering and medical fields. J. Biosci. Bioeng., 2006, 101(5), 377-384.
[] [PMID: 16781465]
Wang, S.; Shi, J.; Ye, Z.; Dong, D.; Yu, D.; Zhou, M.; Liu, Y.; Gevaert, O.; Wang, K.; Zhu, Y.; Zhou, H.; Liu, Z.; Tian, J. Predicting EGFR mutation status in lung adenocarcinoma on ct image using deep learning. Eur. Respir. J., 2019, 53(3)1800986
Hu, L.; Bell, D.; Antani, S.; Xue, Z.; Yu, K.; Horning, M.P.; Gachuhi, N.; Wilson, B.; Jaiswal, M.S.; Befano, B.; Long, L.R.; Herrero, R.; Einstein, M.H.; Burk, R.D.; Demarco, M.; Gage, J.C.; Rodriguez, A.C.; Wentzensen, N.; Schiffman, M. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J. Natl. Cancer Inst., 2019, 111(9), 923-932.
[] [PMID: 30629194]
Bonet, I. Machine learning for prediction of HIV drug resistance: A Review. Curr. Bioinform., 2015, 10(5), 579-585.
Wong, K.K.; Rostomily, R.; Wong, S.T.C. Prognostic gene discovery in glioblastoma patients using deep learning. Cancers (Basel), 2019, 11(1)E53
[] [PMID: 30626092]
Jeyaraj, P.R.; Samuel Nadar, E.R. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol., 2019, 145(4), 829-837.
[] [PMID: 30603908]
Bulik-Sullivan, B.; Busby, J.; Palmer, C.D.; Davis, M.J.; Murphy, T.; Clark, A.; Busby, M.; Duke, F.; Yang, A.; Young, L.; Ojo, N.C.; Caldwell, K.; Abhyankar, J.; Boucher, T.; Hart, M.G.; Makarov, V.; Montpreville, V.T.; Mercier, O.; Chan, T.A.; Scagliotti, G.; Bironzo, P.; Novello, S.; Karachaliou, N.; Rosell, R.; Anderson, I.; Gabrail, N.; Hrom, J.; Limvarapuss, C.; Choquette, K.; Spira, A.; Rousseau, R.; Voong, C.; Rizvi, N.A.; Fadel, E.; Frattini, M.; Jooss, K.; Skoberne, M.; Francis, J.; Yelensky, R. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol., 2018, 37(1), 55.
[] [PMID: 30556813]
Xia, F.; Shukla, M.; Brettin, T.; Garcia-Cardona, C.; Cohn, J.; Allen, J.E.; Maslov, S.; Holbeck, S.L.; Doroshow, J.H.; Evrard, Y.A.; Stahlberg, E.A.; Stevens, R.L. Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinformatics, 2018, 19(Suppl. 18), 486.
[] [PMID: 30577754]
Ainscough, B.J.; Barnell, E.K.; Ronning, P.; Campbell, K.M.; Wagner, A.H.; Fehniger, T.A.; Dunn, G.P.; Uppaluri, R.; Govindan, R.; Rohan, T.E.; Griffith, M.; Mardis, E.R.; Swamidass, S.J.; Griffith, O.L. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat. Genet., 2018, 50(12), 1735-1743.
[] [PMID: 30397337]
Xiao, X.; Lin, W.Z.; Chou, K.C. Recent advances in predicting protein classification and their applications to drug development. Curr. Top. Med. Chem., 2013, 13(14), 1622-1635.
[] [PMID: 23889055]
Qiu, W-R.; Jiang, S-Y.; Xu, Z-C.; Xiao, X.; Chou, K-C. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget, 2017, 8(25), 41178-41188.
[] [PMID: 28476023]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K-C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[] [PMID: 29348418]
Cheng, X.; Lin, W-Z.; Xiao, X.; Chou, K-C. pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics, 2019, 35(3), 398-406.
[] [PMID: 30010789]
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 17(21), 2337-2358.
[] [PMID: 28413951]

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