Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction
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Xianfang Wang | Peng Gao | Fan Lu | Hongfei Li | Yifeng Liu | Dongqing Wei | Dongqing Wei | Hongfei Li | Xianfang Wang | Peng Gao | Yifeng Liu | Fan Lu
[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Hojung Nam,et al. SELF-BLM: Prediction of drug-target interactions via self-training SVM , 2017, PloS one.
[3] James L. McClelland,et al. Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension , 2019, Philosophical Transactions of the Royal Society B.
[4] P. Cohen. Protein kinases — the major drug targets of the twenty-first century? , 2002, Nature reviews. Drug discovery.
[5] Tudor I. Oprea,et al. Drug Repurposing: Far Beyond New Targets for Old Drugs , 2012, The AAPS Journal.
[6] Tudor I. Oprea,et al. A comprehensive map of molecular drug targets , 2016, Nature Reviews Drug Discovery.
[7] Regina Z. Cer,et al. IC50-to-Ki: a web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding , 2009, Nucleic Acids Res..
[8] Raghvendra Mall,et al. PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine , 2018, Bioinform..
[9] Tao Xu,et al. Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis , 2014, J. Chem. Inf. Model..
[10] L. Johnson,et al. Protein Kinase Inhibitors: Insights into Drug Design from Structure , 2004, Science.
[11] Achim Zeileis,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[12] Allen D. Roses,et al. Pharmacogenetics in drug discovery and development: a translational perspective , 2008, Nature Reviews Drug Discovery.
[13] Mary G. George,et al. Assessing potential population impact of statin treatment for primary prevention of atherosclerotic cardiovascular diseases in the USA: population-based modelling study , 2017, BMJ Open.
[14] Raghvendra Mall,et al. DeepSol: a deep learning framework for sequence‐based protein solubility prediction , 2018, Bioinform..
[15] Xiao-Ying Yan,et al. Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods , 2019, Comput. Biol. Chem..
[16] Jun Sun,et al. Development of deep learning method for lead content prediction of lettuce leaf using hyperspectral images , 2019, International Journal of Remote Sensing.
[17] Jatinderkumar R. Saini,et al. Designing punjabi poetry classifiers using machine learning and different textual features , 2019, Int. Arab J. Inf. Technol..
[18] Yu-Yen Ou,et al. DeepETC: A deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes , 2020, Neurocomputing.
[19] Weida Tong,et al. In silico drug repositioning: what we need to know. , 2013, Drug discovery today.
[20] Loris Nanni,et al. iProStruct2D: Identifying protein structural classes by deep learning via 2D representations , 2019, Expert Syst. Appl..
[21] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[22] Tapani Raiko,et al. International Conference on Learning Representations (ICLR) , 2016 .
[23] Yutaka Akiyama,et al. NRLMFβ: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug–target interaction prediction , 2019, Biochemistry and biophysics reports.
[24] Artem Cherkasov,et al. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines , 2017, Journal of Cheminformatics.
[25] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[26] 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.
[27] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[28] M. Köppen,et al. The Curse of Dimensionality , 2010 .
[29] Vladimir B. Bajic,et al. DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches , 2017, Bioinform..
[30] Yi Xiong,et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features , 2019, Briefings Bioinform..
[31] Chee-Keong Kwoh,et al. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey , 2019, Briefings Bioinform..
[32] Arzucan Özgür,et al. DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..
[33] Mindy I. Davis,et al. Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.