iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE
暂无分享,去创建一个
Yongsheng Liu | Saeed Ahmed | Wenyu Chen | Hosney Jahan | S. M. Hasan Mahmud | Nasir Islam Sujan | Wenyu Chen | Saeed Ahmed | Hosney Jahan | S. Mahmud | Yongsheng Liu | Nasir Islam Sujan
[1] Qianzhong Li,et al. Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components , 2007, J. Comput. Chem..
[2] David S. Wishart,et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..
[3] Khurshid Ahmad,et al. Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix , 2016, Neurocomputing.
[4] Guangya Zhang,et al. Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. , 2008, Journal of theoretical biology.
[5] Stuart L. Schreiber,et al. Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays , 2002, Nature.
[6] Abdollah Dehzangi,et al. iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting , 2017, Scientific Reports.
[7] Antje Chang,et al. BRENDA , the enzyme database : updates and major new developments , 2003 .
[8] Zhu-Hong You,et al. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information. , 2016, Current protein & peptide science.
[9] David S. Wishart,et al. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..
[10] Ali Masoudi-Nejad,et al. Drug–target interaction prediction via chemogenomic space: learning-based methods , 2014, Expert opinion on drug metabolism & toxicology.
[11] Jianyu Shi,et al. Predicting existing targets for new drugs base on strategies for missing interactions , 2016, BMC Bioinformatics.
[12] S. Khan,et al. Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC. , 2017, Journal of theoretical biology.
[13] Dingfang Li,et al. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information , 2017, Molecules.
[14] X. Chen,et al. TTD: Therapeutic Target Database , 2002, Nucleic Acids Res..
[15] Michael J. Keiser,et al. Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.
[16] Jie Li,et al. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning , 2016, Briefings Bioinform..
[17] Yoshihiro Yamanishi,et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.
[18] Maqsood Hayat,et al. Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. , 2019, Genomics.
[19] Jian-Yu Shi,et al. A unified solution for different scenarios of predicting drug-target interactions via triple matrix factorization , 2018, BMC Systems Biology.
[20] P. Bork,et al. Drug Target Identification Using Side-Effect Similarity , 2008, Science.
[21] Minzhu Xie,et al. XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction , 2018, IEEE Transactions on NanoBioscience.
[22] Lu Huang,et al. Update of TTD: Therapeutic Target Database , 2009, Nucleic Acids Res..
[23] Keith C. C. Chan,et al. Large-scale prediction of drug-target interactions from deep representations , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[24] Jie Li,et al. Prediction of Polypharmacological Profiles of Drugs by the Integration of Chemical, Side Effect, and Therapeutic Space , 2013, J. Chem. Inf. Model..
[25] Andrew L. Hopkins,et al. Predicting promiscuity , 2009 .
[26] K. Chou,et al. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. , 2013, Journal of theoretical biology.
[27] Bin Chen,et al. PubChem as a Source of Polypharmacology , 2009, J. Chem. Inf. Model..
[28] H. van de Waterbeemd,et al. ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.
[29] Xing Chen,et al. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences , 2017, Scientific Reports.
[30] James G. Lyons,et al. A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition. , 2013, Journal of theoretical biology.
[31] Yong Wang,et al. Computationally Probing Drug-Protein Interactions Via Support Vector Machine , 2010 .
[32] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[33] Zengrui Wu,et al. Network-Based Methods for Prediction of Drug-Target Interactions , 2018, Front. Pharmacol..
[34] Feng Xu,et al. Therapeutic target database update 2014: a resource for targeted therapeutics , 2013, Nucleic Acids Res..
[35] Philip E. Bourne,et al. Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir , 2011, PLoS Comput. Biol..
[36] Miriam Seoane Santos,et al. Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier] , 2018, IEEE Computational Intelligence Magazine.
[37] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[38] Riccardo Bellazzi,et al. PaPI: pseudo amino acid composition to score human protein-coding variants , 2015, BMC Bioinformatics.
[39] Robert B. Russell,et al. SuperTarget and Matador: resources for exploring drug-target relationships , 2007, Nucleic Acids Res..
[40] Sahand Khakabimamaghani,et al. Drug-target interaction prediction from PSSM based evolutionary information. , 2016, Journal of pharmacological and toxicological methods.
[41] S. Haggarty,et al. Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. , 2003, Chemistry & biology.
[42] Xing Chen,et al. A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences. , 2018, Current protein & peptide science.
[43] Hui Yu,et al. Predicting Drug-Target Interactions via Within-Score and Between-Score , 2015, BioMed research international.
[44] Dong-Sheng Cao,et al. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures. , 2012, Analytica chimica acta.
[45] T. Tsunoda,et al. PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction. , 2017, Journal of theoretical biology.
[46] Susumu Goto,et al. KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..
[47] Dong-Sheng Cao,et al. PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions , 2018, Journal of Cheminformatics.
[48] Yoshihiro Yamanishi,et al. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework , 2010, Bioinform..
[49] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[50] Chuang Liu,et al. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..
[51] Mehmet Gönen,et al. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization , 2012, Bioinform..
[52] Chee Keong Kwoh,et al. Drug-target interaction prediction via class imbalance-aware ensemble learning , 2016, BMC Bioinformatics.
[53] B. Efron,et al. A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .
[54] Natalia Novac,et al. Challenges and opportunities of drug repositioning. , 2013, Trends in pharmacological sciences.
[55] Michael J. Keiser,et al. Predicting new molecular targets for known drugs , 2009, Nature.
[56] Kuo-Chen Chou,et al. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes , 2005, Bioinform..
[57] K. Chou,et al. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. , 2008, Analytical biochemistry.
[58] Hua Yu,et al. A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data , 2012, PloS one.
[59] Saeed Ahmad,et al. Improving prediction of extracellular matrix proteins using evolutionary information via a grey system model and asymmetric under-sampling technique , 2018 .
[60] Kuldip K. Paliwal,et al. A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition , 2014, IEEE Transactions on NanoBioscience.
[61] Yong-Yeol Ahn,et al. Optimizing drug–target interaction prediction based on random walk on heterogeneous networks , 2015, Journal of Cheminformatics.
[62] Faisal Saeed,et al. Bioactive Molecule Prediction Using Extreme Gradient Boosting , 2016, Molecules.
[63] Stephen H. Bryant,et al. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique. , 2016, Analytica chimica acta.
[64] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[65] 中尾 光輝,et al. KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .
[66] Bin Yu,et al. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. , 2019, Genomics.
[67] Lin He,et al. Exploring Off-Targets and Off-Systems for Adverse Drug Reactions via Chemical-Protein Interactome — Clozapine-Induced Agranulocytosis as a Case Study , 2011, PLoS Comput. Biol..
[68] Xue-wen Chen,et al. On Position-Specific Scoring Matrix for Protein Function Prediction , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[69] George Papadatos,et al. The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..
[70] Xiaomin Luo,et al. TarFisDock: a web server for identifying drug targets with docking approach , 2006, Nucleic Acids Res..
[71] Jian-Yu Shi,et al. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. , 2015, Methods.
[72] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[73] Hailin Chen,et al. A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks , 2013, PloS one.
[74] J. S. Cramer. The Origins of Logistic Regression , 2002 .
[75] Chunyan Miao,et al. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction , 2016, PLoS Comput. Biol..
[76] Tapio Pahikkala,et al. Toward more realistic drug^target interaction predictions , 2014 .
[77] Alan Wee-Chung Liew,et al. Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines , 2016, J. Chem. Inf. Model..
[78] Nairanjana Dasgupta,et al. An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach , 2018, PloS one.
[79] Yoshihiro Yamanishi,et al. KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..
[80] John B. O. Mitchell. The Relationship between the Sequence Identities of Alpha Helical Proteins in the PDB and the Molecular Similarities of Their Ligands , 2001, J. Chem. Inf. Comput. Sci..
[81] Damian Szklarczyk,et al. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data , 2015, Nucleic Acids Res..
[82] Geoffrey I. Webb,et al. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles , 2017, Bioinform..
[83] K. Chou,et al. Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features , 2010, PloS one.