Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction
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Hiroyuki Kurata | Watshara Shoombuatong | Md Mehedi Hasan | Mst Shamima Khatun | H. Kurata | M. Hasan | W. Shoombuatong | M. S. Khatun | Watshara Shoombuatong
[1] Virapong Prachayasittikul,et al. PAAP: a web server for predicting antihypertensive activity of peptides. , 2018, Future medicinal chemistry.
[2] Hiroyuki Kurata,et al. PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features , 2019, Front. Genet..
[3] Balachandran Manavalan,et al. mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides , 2019, International journal of molecular sciences.
[4] Erli Pang,et al. Yeast protein-protein interaction binding sites: prediction from the motif-motif, motif-domain and domain-domain levels. , 2010, Molecular bioSystems.
[5] Yu Xia,et al. Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing , 2017, PLoS Comput. Biol..
[6] Renata Guerra-Sá,et al. In silico Prediction of Protein–Protein Interaction Network Induced by Manganese II in Meyerozyma guilliermondii , 2020, Frontiers in Microbiology.
[7] S. Teichmann,et al. Structure, dynamics, assembly, and evolution of protein complexes. , 2015, Annual review of biochemistry.
[8] Hiroyuki Kurata,et al. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation , 2020, Plant Molecular Biology.
[9] Baldomero Oliva,et al. iLoops: a protein-protein interaction prediction server based on structural features , 2013, Bioinform..
[10] Crhisllane Rafaele dos Santos Vasconcelos,et al. Building protein-protein interaction networks for Leishmania species through protein structural information , 2018, BMC Bioinformatics.
[11] Shinn-Ying Ho,et al. SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs , 2013, PloS one.
[12] Sandra Romero-Molina,et al. PPI‐Detect: A support vector machine model for sequence‐based prediction of protein–protein interactions , 2019, J. Comput. Chem..
[13] Hiroyuki Kurata,et al. Prediction of S-nitrosylation sites by integrating support vector machines and random forest. , 2019, Molecular omics.
[14] Xing-Ming Zhao,et al. PPIM : A protein-protein interaction database for Maize 11 12 , 2015 .
[15] Yang Wang,et al. Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[16] Mohammad Ganjtabesh,et al. Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources , 2017, BMC Bioinformatics.
[17] Yuh-Jyh Hu,et al. Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme , 2019, BMC Bioinformatics.
[18] Myeong Ok Kim,et al. PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions , 2018, Front. Immunol..
[19] Nazar Zaki,et al. Improving the Detection of Protein Complexes by Predicting Novel Missing Interactome Links in the Protein-Protein Interaction Network , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[20] Thomas L. Madden,et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.
[21] J. Reifman,et al. Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection , 2009, PloS one.
[22] Lan Huang,et al. Profiling of Protein Interaction Networks of Protein Complexes Using Affinity Purification and Quantitative Mass Spectrometry* , 2010, Molecular & Cellular Proteomics.
[23] Hiroyuki Kurata,et al. Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information , 2019, Scientific Reports.
[24] Hiroyuki Kurata,et al. A Comprehensive Review of In silico Analysis for Protein S-sulfenylation Sites. , 2018, Protein and peptide letters.
[25] Virapong Prachayasittikul,et al. Navigating the chemical space of dipeptidyl peptidase-4 inhibitors , 2015, Drug design, development and therapy.
[26] Yang Guo,et al. Protein–protein interaction network‐based detection of functionally similar proteins within species , 2012, Proteins.
[27] Bogdan Istrate,et al. Algorithmic approaches to protein-protein interaction site prediction , 2015, Algorithms for Molecular Biology.
[28] Lu Wang,et al. Protein–protein interaction networks and different clustering analysis in Burkitt’s lymphoma , 2018, Hematology.
[29] Bindu Nanduri,et al. HPIDB 2.0: a curated database for host–pathogen interactions , 2016, Database J. Biol. Databases Curation.
[30] Hiroyuki Kurata,et al. Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information. , 2017, Molecular bioSystems.
[31] Wei Chen,et al. FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification , 2020, Frontiers in Genetics.
[32] Leyi Wei,et al. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation , 2019, Molecular therapy. Nucleic acids.
[33] Hiroyuki Kurata,et al. Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites , 2019, Cells.
[34] Hongjie Wu,et al. DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions. , 2019, Journal of molecular biology.
[35] Yuan Zhou,et al. Critical assessment and performance improvement of plant‐pathogen protein‐protein interaction prediction methods , 2019, Briefings Bioinform..
[36] Md. Nurul Haque Mollah,et al. NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features , 2018, Molecules.
[37] Mudita Singhal,et al. A domain-based approach to predict protein-protein interactions , 2007, BMC Bioinformatics.
[38] Zhilong Xiu,et al. Protein–protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis , 2014, Journal of Industrial Microbiology & Biotechnology.
[39] Xiaopan Zhang,et al. Prediction of Protein-Protein Interactions Based on Domain , 2019, Comput. Math. Methods Medicine.
[40] Pei-Yu Wu,et al. Detection of membrane protein–protein interaction in planta based on dual‐intein‐coupled tripartite split‐GFP association , 2018, The Plant journal : for cell and molecular biology.
[41] Jinyan Li,et al. Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs , 2015, PloS one.
[42] Yong Zhou,et al. Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer–Based Relevance Vector Machine , 2019, Evolutionary bioinformatics online.
[43] Nalini Schaduangrat,et al. THPep: A machine learning-based approach for predicting tumor homing peptides , 2019, Comput. Biol. Chem..
[44] Reza Ebrahimpour,et al. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information. , 2013, Genomics.
[45] Luh Tung,et al. Detection of Protein-Protein Interaction Within an RNA-Protein Complex Via Unnatural-Amino-Acid-Mediated Photochemical Crosslinking. , 2016, Methods in molecular biology.
[46] Mathieu Blanchette,et al. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks , 2009, RECOMB.
[47] En-Shiun Annie Lee,et al. Prediction of Protein-Protein Interaction via co-occurring Aligned Pattern Clusters. , 2016, Methods.
[48] Md. Nurul Haque Mollah,et al. SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties. , 2016, Molecular bioSystems.
[49] Abdollah Dehzangi,et al. iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features. , 2017, Journal of theoretical biology.
[50] Lucian Ilie,et al. SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome , 2017, BMC Bioinformatics.
[51] Virapong Prachayasittikul,et al. osFP: a web server for predicting the oligomeric states of fluorescent proteins , 2016, Journal of Cheminformatics.
[52] Virapong Prachayasittikul,et al. Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation , 2019, International journal of molecular sciences.
[53] Zhengwei Zhu,et al. CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..
[54] Behnam Neyshabur,et al. Predicting protein‐protein interactions through sequence‐based deep learning , 2018, Bioinform..
[55] Reza Ebrahimpour,et al. LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information. , 2014, Genomics.
[56] Xing Chen,et al. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction , 2018, Cell Death & Disease.
[57] Nazar Zaki,et al. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters , 2015, PloS one.
[58] Luhua Lai,et al. Sequence-based prediction of protein protein interaction using a deep-learning algorithm , 2017, BMC Bioinformatics.
[59] Leyi Wei,et al. mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation , 2018, Bioinform..
[60] Yasuo Tabei,et al. Scalable Prediction of Compound‐protein Interaction on Compressed Molecular Fingerprints , 2020, Molecular informatics.
[61] C. Sander,et al. Correlated mutations and residue contacts in proteins , 1994, Proteins.
[62] Martin H. Schaefer,et al. HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks , 2016, Nucleic Acids Res..
[63] T. M. Murali,et al. Computational prediction of host-pathogen protein-protein interactions , 2007, ISMB/ECCB.
[64] Menglong Li,et al. PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment , 2010, BMC Research Notes.
[65] Balachandran Manavalan,et al. Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening , 2020, Medicinal research reviews.
[66] Erich Bornberg-Bauer,et al. The Evolution of Protein Interaction Networks in Regulatory Proteins , 2004, Comparative and functional genomics.
[67] Balachandran Manavalan,et al. Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides. , 2020, Current protein & peptide science.
[68] Hiroyuki Kurata,et al. GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple sequence features , 2018, PloS one.
[69] Kara Dolinski,et al. The BioGRID interaction database: 2019 update , 2018, Nucleic Acids Res..
[70] M. Gerstein,et al. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.
[71] Minoru Kanehisa,et al. AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..
[72] Chen Cao,et al. Using discriminative vector machine model with 2DPCA to predict interactions among proteins , 2019, BMC Bioinformatics.
[73] S. Orrù,et al. Protein-protein interaction networks as a new perspective to evaluate distinct functional roles of voltage-dependent anion channel isoforms. , 2017, Molecular bioSystems.
[74] Abdollah Dehzangi,et al. iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features , 2017, Scientific Reports.
[75] Nalini Schaduangrat,et al. HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation , 2020, Bioinform..
[76] Gwang Lee,et al. AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest , 2018, Front. Pharmacol..
[77] Seiki Kuramitsu,et al. DNA Binding and Protein-Protein Interaction Sites in MutS, a Mismatched DNA Recognition Protein from Thermus thermophilus HB8* , 2000, The Journal of Biological Chemistry.
[78] Yuri Matsuzaki,et al. MEGADOCK: An All-to-All Protein-Protein Interaction Prediction System Using Tertiary Structure Data , 2013, Protein and peptide letters.
[79] Hongfei Lin,et al. Detection of protein complexes from multiple protein interaction networks using graph embedding , 2019, Artif. Intell. Medicine.
[80] Ji-Yong An,et al. Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC. , 2017, Journal of theoretical biology.
[81] Kenji Mizuguchi,et al. Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators , 2014, BMC Bioinformatics.
[82] Jiangning Song,et al. Conditional random field approach to prediction of protein-protein interactions using domain information , 2011, BMC Systems Biology.
[83] Kyubong Jo,et al. FRET-based analysis of protein-nucleic acid interactions by genetically incorporating a fluorescent amino acid , 2014, Amino Acids.
[84] MengChu Zhou,et al. Highly Efficient Framework for Predicting Interactions Between Proteins , 2017, IEEE Transactions on Cybernetics.
[85] Balachandran Manavalan,et al. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome. , 2019, International journal of biological macromolecules.
[86] A. Pandey,et al. Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. , 2012, Molecular bioSystems.
[87] Xing Chen,et al. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding , 2016, BMC Bioinformatics.
[88] Hiroyuki Kurata,et al. iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Features , 2018, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).
[89] Kara Dolinski,et al. The BioGRID interaction database: 2017 update , 2016, Nucleic Acids Res..
[90] Douglas R Storts,et al. Protein-protein interaction studies on protein arrays: effect of detection strategies on signal-to-background ratios. , 2009, Analytical Biochemistry.
[91] Dianjing Guo,et al. A systematic identification of species-specific protein succinylation sites using joint element features information , 2017, International journal of nanomedicine.
[92] Dmitry Korkin,et al. DISPOT: a simple knowledge-based protein domain interaction statistical potential , 2019, Bioinform..
[93] Chitra Subramanian,et al. In vivo detection of protein-protein interaction in plant cells using BRET. , 2004, Methods in molecular biology.
[94] Cesim Erten,et al. SiPAN: simultaneous prediction and alignment of protein-protein interaction networks , 2015, Bioinform..
[95] Burkhard Rost,et al. Evolutionary profiles improve protein-protein interaction prediction from sequence , 2015, Bioinform..
[96] Rebecca L Poole. The TAIR database. , 2007, Methods in molecular biology.
[97] Robert B. Russell,et al. InterPreTS: protein Interaction Prediction through Tertiary Structure , 2003, Bioinform..
[98] Jun Wang,et al. Predicting protein-protein interactions using high-quality non-interacting pairs , 2018, BMC Bioinformatics.
[99] Hiroyuki Kurata,et al. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties , 2019, FEBS letters.
[100] Alfonso Valencia,et al. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions. , 2013, Molecular bioSystems.
[101] Geoffrey I. Webb,et al. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites , 2019, Bioinform..
[102] Lenwood S. Heath,et al. DeNovo: virus-host sequence-based protein-protein interaction prediction , 2016, Bioinform..
[103] Mohammad Ali Moni,et al. Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana , 2020, Comput. Biol. Chem..
[104] J. Zhuang,et al. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes. , 2016, Genetics and molecular research : GMR.
[105] Kyungsook Han,et al. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. , 2010, Protein and peptide letters.
[106] Xianyi Lian,et al. Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework , 2019, mSystems.
[107] Leyi Wei,et al. AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees , 2019, Computational and structural biotechnology journal.
[108] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[109] Virapong Prachayasittikul,et al. HemoPred: a web server for predicting the hemolytic activity of peptides. , 2017, Future medicinal chemistry.
[110] Ujjwal Maulik,et al. Computational Prediction of HCV-Human Protein-Protein Interaction via Topological Analysis of HCV Infected PPI Modules , 2018, IEEE Transactions on NanoBioscience.
[111] R. Raz,et al. ProMate: a structure based prediction program to identify the location of protein-protein binding sites. , 2004, Journal of molecular biology.
[112] D. Liu,et al. Biochemical and functional characterization of Epstein–Barr virus-encoded BARF1 protein: interaction with human hTid1 protein facilitates its maturation and secretion , 2006, Oncogene.
[113] Hiroyuki Kurata,et al. SIPMA: A Systematic Identification of Protein-Protein Interactions in Zea mays Using Autocorrelation Features in a Machine-Learning Framework , 2018, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).
[114] Hao Lv,et al. iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features , 2019, Molecular therapy. Nucleic acids.
[115] Wan Kyu Kim,et al. Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair. , 2002, Genome informatics. International Conference on Genome Informatics.
[116] Lin Lu,et al. Protein-protein interaction analysis to identify biomarker networks for endometriosis , 2017, Experimental and therapeutic medicine.
[117] Balachandran Manavalan,et al. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. , 2018, Journal of proteome research.
[118] Hiroyuki Ogata,et al. AAindex: Amino Acid Index Database , 1999, Nucleic Acids Res..
[119] Javier De Las Rivas,et al. APID database: redefining protein–protein interaction experimental evidences and binary interactomes , 2019, Database J. Biol. Databases Curation.
[120] Jie Hu,et al. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools , 2019, Briefings Bioinform..
[121] Md. Mehedi Hasan,et al. Opinion Prediction of protein Post-Translational Modification sites: An overview , 2017 .
[122] Richard M. Jackson,et al. Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces , 2006, Bioinform..
[123] Byeungwoo Jeon,et al. A Network Hierarchy-Based method for functional module detection in protein-protein interaction networks. , 2018, Journal of theoretical biology.
[124] Richard Wade-Martins,et al. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease , 2015, Scientific Reports.
[125] Md. Nurul Haque Mollah,et al. Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework. , 2020, Protein and peptide letters.
[126] Jinyan Li,et al. Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network , 2019, Neurocomputing.
[127] Piyali Chatterjee,et al. Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features , 2018, J. Bioinform. Comput. Biol..
[128] Xing Chen,et al. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics , 2016, International journal of molecular sciences.
[129] Javier De Las Rivas,et al. Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks , 2010, PLoS Comput. Biol..
[130] Ioannis Xenarios,et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions , 2002, Nucleic Acids Res..
[131] C. Pham,et al. Detection of protein-protein interaction using bimolecular fluorescence complementation assay. , 2015, Methods in molecular biology.
[132] Chen Fu,et al. Machine-Learning-Based Predictor of Human-Bacteria Protein-Protein Interactions by Incorporating Comprehensive Host-Network Properties. , 2019, Journal of proteome research.
[133] Hans-Werner Mewes,et al. CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..