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[1]
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The Development and Progress in Machine Learning for Protein Subcellular Localization Prediction
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The Open Bioinformatics Journal,
2022 |
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[2]
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Protein Subcellular Localization Based on Evolutionary Information and Segmented Distribution
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Mathematical Problems in Engineering,
2021 |
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[3]
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The significant and profound impacts of Gordon life science institute
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2021 |
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[4]
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4mC-RF: Improving the prediction of 4mC sites using composition and position relative features and statistical moment
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Analytical Biochemistry,
2021 |
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[5]
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The Remarkable impacts of Gordon life science institute
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2021 |
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[6]
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NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule
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2021 |
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[7]
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IAmideV-deep: Valine amidation site prediction in proteins using deep learning and pseudo amino acid compositions
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2021 |
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[8]
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iPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC
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2021 |
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[9]
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iPhosS (Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions
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IEEE/ACM Transactions …,
2020 |
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[10]
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The divination of things by things
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Extended Abstracts of the 2020 CHI Conference on …,
2020 |
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[11]
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RF-MaloSite and DL-MaloSite: Two independent computational methods based on Random Forest (RF) and Deep Learning (DL) to predict malonylation sites
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2020 |
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[12]
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Distorted key theory and its implication for drug development
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2020 |
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[13]
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Showcase to Illustrate How the Web-Server pLoc_Deep-mGneg Is Working
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2020 |
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[14]
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pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning
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2020 |
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[15]
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Predicting gram-positive bacterial protein subcellular location by using combined features
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2020 |
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[16]
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Analysis of domain feature of gram-positive bacterial protein.
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2020 |
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[17]
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ML-RBF: Predict protein subcellular locations in a multi-label system using evolutionary features
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2020 |
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[18]
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Recent Progresses for Computationally Identifying N 6-methyladenosine Sites in Saccharomyces cerevisiae
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2020 |
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[19]
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GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting
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2020 |
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[20]
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The End of Our Earth Is Certainly to Come:“When”? and “Why”?
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2020 |
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[21]
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RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
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2020 |
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[22]
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Enhancing Segmentation Approaches from Oaam to Fuzzy KC-Means
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2020 |
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[23]
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Use Chou's 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis
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2020 |
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[24]
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Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development
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2020 |
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[25]
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Showcase to Illustrate How the Web-server iPTM-mLys is working
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2020 |
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[26]
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How the artificial intelligence tool iRNA-2methyl is working for RNA 2'-O-methylation sites
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2020 |
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[27]
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An insightful 20-year recollection since the birth of pseudo amino acid components
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2020 |
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[28]
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Showcase to illustrate how the web-server iKcr-PseEns is working
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2020 |
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[29]
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Predicting lncRNA subcellular localization using unbalanced pseudo-k nucleotide compositions
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2020 |
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[30]
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Sequence-based identification of arginine amidation sites in proteins using deep representations of proteins and PseAAC
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2020 |
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[31]
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An Effective Multi-Label Protein Sub-Chloroplast Localization Prediction by Skipped-Grams of Evolutionary Profiles Using Deep Neural Network
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IEEE/ACM Transactions on Computational …,
2020 |
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[32]
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cACP: Classifying anticancer peptides using discriminative intelligent model via Chou's 5-step rules and general pseudo components
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2020 |
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[33]
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Feature selection and classification for gene expression data using novel correlation based overlapping score method via Chou's 5-steps rule
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2020 |
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[34]
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Some illuminating remarks on molecular genetics and genomics as well as drug development
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2020 |
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[35]
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Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America. Showcase To Illustrate How the Web-Server Initro-Tyr Is Working
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Journal of Clinical Cancer Research,
2019 |
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[36]
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The impact of statin therapy on the survival of patients with gastrointestinal cancer
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2019 |
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[37]
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A Study for Therapeutic Treatment against Parkinson's Disease via Chou's 5-steps Rule
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2019 |
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[38]
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Inhibition of α-amylase Activity by Zn2+: Insights from Spectroscopy and Molecular Dynamics Simulations
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2019 |
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[39]
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A Possible Modulation Mechanism of Intramolecular and Intermolecular Interactions for NCAM Polysialylation and Cell Migration
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2019 |
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[40]
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RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule
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2019 |
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[41]
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Bayesian Polytrees with Learned Deep Features for Multi-Class Cell Segmentation
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2019 |
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[42]
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Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach
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2019 |
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[43]
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Identifying N6-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer
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2019 |
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[44]
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LAIPT: Lysine Acetylation Site Identification with Polynomial Tree
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2019 |
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[45]
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MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
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2019 |
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[46]
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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
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2019 |
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[47]
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pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
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2019 |
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[48]
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SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
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2019 |
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[49]
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MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule
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2019 |
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[50]
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iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components
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2019 |
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[51]
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In Silico Design and Synthesis of Targeted Curcumin Derivatives as Xanthine Oxidase Inhibitors
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2019 |
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[52]
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Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection
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2019 |
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[53]
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iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and …
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2019 |
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[54]
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The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses
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2019 |
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[55]
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MsDBP: Exploring DNA-binding Proteins by Integrating Multi-scale Sequence Information via Chou's 5-steps Rule
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Journal of Proteome Research,
2019 |
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[56]
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iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via …
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2019 |
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[57]
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Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule
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2019 |
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[58]
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Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Betalyase Plays in Hyperoxaluria
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2019 |
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[59]
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iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General …
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2019 |
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[60]
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Identification and characterization of WD40 superfamily genes in peach
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2019 |
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[61]
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Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks
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2019 |
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[62]
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Application of machine learning approaches for the design and study of anticancer drugs
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2019 |
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[63]
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Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
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2019 |
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[64]
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Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
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2019 |
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[65]
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Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression
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2019 |
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[66]
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Advance in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
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2019 |
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[67]
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Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
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2019 |
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[68]
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Progresses in predicting post-translational modification
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2019 |
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[69]
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iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and …
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2019 |
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[70]
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Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
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2019 |
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[71]
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pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by Chou's general PseAAC and IHTS treatment to balance training dataset
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2019 |
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[72]
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pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset
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2019 |
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[73]
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Critical evaluation of web-based prediction tools for human protein subcellular localization
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2019 |
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[74]
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Showcase to Illustrate How the Web-Server Idna6ma-Pseknc is Working
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Journal of Pathology Research Reviews & Reports,
2019 |
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[75]
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How the artificial intelligence tool iSNO-PseAAC is working in predicting the cysteine S-nitrosylation sites in proteins
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Journal of Stem Cell Research and Medicine,
2019 |
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[76]
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Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications
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2019 |
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[77]
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An insightful recollection for predicting protein subcellular locations in multi-label systems
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2019 |
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[78]
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An insightful 10-year recollection since the emergence of the 5-steps rule.
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2019 |
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[79]
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iHyd-PseAAC (EPSV): identifying hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via chou's 5-step rule and general …
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2019 |
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[80]
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Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5-steps rule
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2019 |
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[81]
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Identify Lysine Neddylation Sites Using Bi-Profile Bayes Feature Extraction via the Chou's 5-Steps Rule and General Pseudo Components
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2019 |
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[82]
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Biological Production of (S)-acetoin: A State-of-the-Art Review
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2019 |
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[83]
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Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis
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2019 |
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[84]
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The multiple applications and possible mechanisms of the hyperbaric oxygenation therapy
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2019 |
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[85]
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Gordon life science institute: its philosophy, achievements, and perspective
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2019 |
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[86]
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An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago
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2019 |
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[87]
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Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5‐steps rule
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2019 |
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[88]
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19F-NMR in Target-based Drug Discovery
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2019 |
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[89]
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Intriguing Story about the Birth of Gordon Life Science Institute and its Development and Driving Force
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2019 |
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[90]
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Efficient computational model for classification of protein localization images using extended threshold adjacency statistics and support vector machines
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Computer methods and …,
2018 |
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[91]
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iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
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2018 |
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[92]
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pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
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Genomics,
2018 |
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[93]
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iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
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Genomics,
2018 |
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[94]
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iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
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Genomics,
2018 |
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[95]
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pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC
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Genomics,
2018 |
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[96]
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iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
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Briefings in Bioinformatics,
2018 |
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[97]
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iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites
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Molecular Therapy - Nucleic Acids,
2018 |
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[98]
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pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC
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Bioinformatics,
2018 |
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[99]
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Implications of newly identified brain eQTL genes and their interactors in Schizophrenia
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Molecular Therapy - Nucleic Acids,
2018 |
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[100]
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Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains
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Journal of Theoretical Biology,
2018 |
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[101]
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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 …
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Journal of Theoretical Biology,
2018 |
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[102]
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BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
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Journal of Theoretical Biology,
2018 |
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[103]
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iRO-3wPseKNC: Identify DNA replication origins by three-window-based PseKNC.
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Bioinformatics,
2018 |
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[104]
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EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
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Journal of Theoretical Biology,
2018 |
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[105]
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iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier
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Genomics,
2018 |
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[106]
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iRNA (m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition
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Analytical Biochemistry,
2018 |
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[107]
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Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
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Journal of Theoretical Biology,
2018 |
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[108]
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pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
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Journal of Theoretical Biology,
2018 |
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[109]
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Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC
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Genomics,
2018 |
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[110]
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Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition
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Gene,
2018 |
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[111]
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pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
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Genomics,
2018 |
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[112]
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Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC
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Journal of Theoretical Biology,
2018 |
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[113]
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Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC
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2018 |
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[114]
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iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components
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Journal of Theoretical Biology,
2018 |
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[115]
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Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition
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Journal of Theoretical Biology,
2018 |
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[116]
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Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence
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Journal of Theoretical Biology,
2018 |
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[117]
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A proteome-wide systems toxicological approach deciphers the interaction network of chemotherapeutic drugs in the cardiovascular milieu
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RSC Advances,
2018 |
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[118]
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Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC
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Journal of Theoretical Biology,
2018 |
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[119]
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Characterization of proteins in different subcellular localizations for Escherichia coli K12
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Genomics,
2018 |
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[120]
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Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile …
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PLOS ONE,
2018 |
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[121]
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Large-scale frequent stem pattern mining in RNA families
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Journal of Theoretical Biology,
2018 |
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[122]
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Characterize the difference between TMPRSS2-ERG and non-TMPRSS2-ERG fusion patients by clinical and biological characteristics in prostate cancer
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Gene,
2018 |
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[123]
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mLysPTMpred: Multiple Lysine PTM Site Prediction Using Combination of SVM with Resolving Data Imbalance Issue
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2018 |
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[124]
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iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components
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Genomics,
2018 |
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[125]
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Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC
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Journal of Theoretical Biology,
2018 |
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[126]
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iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide …
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Molecular Genetics and Genomics,
2018 |
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[127]
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Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC
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Molecular Biology Reports,
2018 |
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[128]
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Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features
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Journal of Theoretical Biology,
2018 |
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[129]
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Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods
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Computational Materials Science,
2018 |
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[130]
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iEnhancer-EL: Identifying enhancers and their strength with ensemble learning approach
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Bioinformatics,
2018 |
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[131]
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NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
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Journal of Theoretical Biology,
2018 |
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[132]
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LQTA-R: A new 3D-QSAR methodology applied to a set of DGAT1 inhibitors
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Computational Biology and Chemistry,
2018 |
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[133]
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iPPI-PseAAC (CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
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Journal of Theoretical Biology,
2018 |
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[134]
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iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC
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Bioinformatics,
2018 |
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[135]
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Heterodimer binding scaffolds recognition via the analysis of kinetically hot residues
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Pharmaceuticals,
2018 |
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[136]
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Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
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Journal of Theoretical Biology,
2018 |
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[137]
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UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components
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Chemometrics and Intelligent Laboratory Systems,
2018 |
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[138]
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Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC
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Journal of Theoretical Biology,
2018 |
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[139]
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iRO-3wPseKNC: Identify DNA replication origins by three-window-based PseKNC
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|
2018 |
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[140]
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pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC
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2018 |
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[141]
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Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans
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2018 |
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[142]
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pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset
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2018 |
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[143]
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iPSW (2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
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2018 |
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[144]
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pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information
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Bioinformatics,
2017 |
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[145]
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Intriguing Story about the Birth of Gordon Life Science Institute and its Development and Driving Force. J Retro Virol Anti Retro Virol 2019, 1 (1): 180002 …
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