Chu Y - Search Results

1

NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.

Jiang M, Zhao B, Luo S, Wang Q, Chu Y, Chen T, Mao X, Liu Y, Wang Y, Jiang X, Wei DQ, Xiong Y. Jiang M, et al. Among authors: chu y. Brief Bioinform. 2021 Nov 5;22(6):bbab310. doi: 10.1093/bib/bbab310. Brief Bioinform. 2021. PMID: 34396388

2

Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method.

Shan X, Wang X, Li CD, Chu Y, Zhang Y, Xiong Y, Wei DQ. Shan X, et al. Among authors: chu y. J Chem Inf Model. 2019 Nov 25;59(11):4577-4586. doi: 10.1021/acs.jcim.9b00749. Epub 2019 Oct 22. J Chem Inf Model. 2019. PMID: 31603319

3

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. Chu Y, et al. Brief Bioinform. 2021 Jan 18;22(1):451-462. doi: 10.1093/bib/bbz152. Brief Bioinform. 2021. PMID: 31885041

4

SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.

Zhang YF, Wang X, Kaushik AC, Chu Y, Shan X, Zhao MZ, Xu Q, Wei DQ. Zhang YF, et al. Among authors: chu y. Front Chem. 2020 Jan 10;7:895. doi: 10.3389/fchem.2019.00895. eCollection 2019. Front Chem. 2020. PMID: 31998687 Free PMC article.

5

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Chu Y, Shan X, Chen T, Jiang M, Wang Y, Wang Q, Salahub DR, Xiong Y, Wei DQ. Chu Y, et al. Brief Bioinform. 2021 May 20;22(3):bbaa205. doi: 10.1093/bib/bbaa205. Brief Bioinform. 2021. PMID: 32964234

6

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.

Chen T, Wang X, Chu Y, Wang Y, Jiang M, Wei DQ, Xiong Y. Chen T, et al. Among authors: chu y. Front Microbiol. 2020 Sep 24;11:580382. doi: 10.3389/fmicb.2020.580382. eCollection 2020. Front Microbiol. 2020. PMID: 33072049 Free PMC article.

7

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.

Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. Chu Y, et al. Brief Bioinform. 2021 Nov 5;22(6):bbab165. doi: 10.1093/bib/bbab165. Brief Bioinform. 2021. PMID: 34009265

8

MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information.

Dai Q, Chu Y, Li Z, Zhao Y, Mao X, Wang Y, Xiong Y, Wei DQ. Dai Q, et al. Among authors: chu y. Comput Biol Med. 2021 Sep;136:104706. doi: 10.1016/j.compbiomed.2021.104706. Epub 2021 Jul 28. Comput Biol Med. 2021. PMID: 34371319

9

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

Lin S, Wang Y, Zhang L, Chu Y, Liu Y, Fang Y, Jiang M, Wang Q, Zhao B, Xiong Y, Wei DQ. Lin S, et al. Among authors: chu y. Brief Bioinform. 2022 Jan 17;23(1):bbab421. doi: 10.1093/bib/bbab421. Brief Bioinform. 2022. PMID: 34671814

10

De novo molecular design with deep molecular generative models for PPI inhibitors.

Wang J, Chu Y, Mao J, Jeon HN, Jin H, Zeb A, Jang Y, Cho KH, Song T, No KT. Wang J, et al. Among authors: chu y. Brief Bioinform. 2022 Jul 18;23(4):bbac285. doi: 10.1093/bib/bbac285. Brief Bioinform. 2022. PMID: 35830870

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