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Search life-sciences literature (45,104,206 articles, preprints and more)

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Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases.
Samman N, Mohabatkar H, Rabiei P
Mol Biol Res Commun, 12(3):117-126, 01 Jan 2023
Cited by: 1 article | PMID: 37525666 | PMCID: PMC10387176
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Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.
Asim MN, Ibrahim MA, Imran Malik M, Dengel A, Ahmed S
Int J Mol Sci, 23(15):8221, 26 Jul 2022
Cited by: 1 article | PMID: 35897818 | PMCID: PMC9329987
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Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model.
Li Y, Sun R, Yin K, Xu Y, Chai B, Xiao L
Sci Rep, 9(1):19853, 27 Dec 2019
Cited by: 2 articles | PMID: 31882832 | PMCID: PMC6934798
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Some illuminating remarks on molecular genetics and genomics as well as drug development.
Chou KC
Mol Genet Genomics, 295(2):261-274, 01 Jan 2020
Cited by: 1 article | PMID: 31894399
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Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.
Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, de la Riva GA, Antunes A
Biomolecules, 10(1):E26, 23 Dec 2019
Cited by: 6 articles | PMID: 31878100 | PMCID: PMC7022958
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Set of approaches based on 3D structure and position specific-scoring matrix for predicting DNA-binding proteins.
Nanni L, Brahnam S
Bioinformatics, 35(11):1844-1851, 01 Jun 2019
Cited by: 5 articles | PMID: 30395157
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Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection.
Saikia S, Bordoloi M, Sarmah R
Curr Drug Targets, 20(5):522-539, 01 Jan 2019
Cited by: 11 articles | PMID: 30394207
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A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods.
Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H
Curr Drug Targets, 20(5):540-550, 01 Jan 2019
Cited by: 13 articles | PMID: 30277150
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Representation Learning for Class C G Protein-Coupled Receptors Classification.
Cruz-Barbosa R, Ramos-Pérez EG, Giraldo J
Molecules, 23(3):E690, 19 Mar 2018
Cited by: 0 articles | PMID: 29562690 | PMCID: PMC6017523
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A Method for the Annotation of Functional Similarities of Coding DNA Sequences: the Case of a Populated Cluster of Transmembrane Proteins.
Fuertes MA, Rodrigo JR, Alonso C
J Mol Evol, 84(1):29-38, 03 Nov 2016
Cited by: 3 articles | PMID: 27812751
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Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.
Chen W, Lin H, Chou KC
Mol Biosyst, 11(10):2620-2634, 01 Oct 2015
Cited by: 116 articles | PMID: 26099739
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iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.
Kabir M, Hayat M
Mol Genet Genomics, 291(1):285-296, 30 Aug 2015
Cited by: 39 articles | PMID: 26319782
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Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition.
Ali F, Hayat M
J Theor Biol, 384:78-83, 20 Aug 2015
Cited by: 34 articles | PMID: 26297889
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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
Mol Inform, 34(1):8-17, 26 Sep 2014
Cited by: 56 articles | PMID: 27490858
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iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.
Xiao X, Zou HL, Lin WZ
J Membr Biol, 248(4):745-752, 22 Mar 2015
Cited by: 6 articles | PMID: 25796484
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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 KC
J Theor Biol, 377:47-56, 20 Apr 2015
Cited by: 105 articles | PMID: 25908206
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PECM: prediction of extracellular matrix proteins using the concept of Chou's pseudo amino acid composition.
Zhang J, Sun P, Zhao X, Ma Z
J Theor Biol, 363:412-418, 11 Aug 2014
Cited by: 12 articles | PMID: 25123433
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iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition.
Xu Y, Wen X, Shao XJ, Deng NY, Chou KC
Int J Mol Sci, 15(5):7594-7610, 05 May 2014
Cited by: 89 articles | PMID: 24857907 | PMCID: PMC4057693
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iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.
Chen W, Chen W, Feng PM, Lin H, Chou KC
Biomed Res Int, 2014:623149, 21 May 2014
Cited by: 73 articles | PMID: 24967386 | PMCID: PMC4055483
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Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins.
Arango-Argoty GA, Jaramillo-Garzón JA, Castellanos-Domínguez G
J Theor Biol, 364:121-130, 16 Sep 2014
Cited by: 2 articles | PMID: 25219623
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Classifying G-protein-coupled receptors to the finest subtype level.
Gao QB, Ye XF, He J
Biochem Biophys Res Commun, 439(2):303-308, 20 Aug 2013
Cited by: 6 articles | PMID: 23973783
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Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou's pseudo amino acid composition.
Zhang L, Zhao X, Kong L
J Theor Biol, 355:105-110, 13 Apr 2014
Cited by: 27 articles | PMID: 24735902
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iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.
Qiu WR, Xiao X, Chou KC
Int J Mol Sci, 15(2):1746-1766, 24 Jan 2014
Cited by: 120 articles | PMID: 24469313 | PMCID: PMC3958819
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PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition.
Chen W, Lei TY, Jin DC, Lin H, Chou KC
Anal Biochem, 456:53-60, 13 Apr 2014
Cited by: 178 articles | PMID: 24732113
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Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model.
Li X, Wu X, Wu G
J Theor Biol, 347:84-94, 12 Jan 2014
Cited by: 2 articles | PMID: 24423409
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