Jianzhu Ma

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Jian PengHelixonPatvirtintas el. paštas helixon.com
Jinbo XuProfessor, Toyota Technological Institute at ChicagoPatvirtintas el. paštas ttic.edu
Sheng WangZelixir BiotechPatvirtintas el. paštas zelixir.com
Trey IdekerUniversity of California San DiegoPatvirtintas el. paštas ucsd.edu
Sheng WangAssistant Professor at University of WashingtonPatvirtintas el. paštas uw.edu
Roded SharanTel Aviv UniversityPatvirtintas el. paštas post.tau.ac.il
Keiichiro OnoUniversity of California, San DiegoPatvirtintas el. paštas ucsd.edu
Le SongGenBio AI, Mohamed bin Zayed University of Artificial IntelligencePatvirtintas el. paštas mbzuai.ac.ae
Joseph R. EckerHoward Hughes Medical Institute and The Salk Institute for Biological StudiesPatvirtintas el. paštas Salk.edu
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoPatvirtintas el. paštas salk.edu
Jesse DixonAssistant Professor, Salk Institute for Biological SciencesPatvirtintas el. paštas salk.edu
Muhan ZhangPeking UniversityPatvirtintas el. paštas pku.edu.cn
Ben RaphaelProfessor of Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisPatvirtintas el. paštas cs.uiuc.edu
Mingfu ShaoDepartment of Computer Science and Engineerings, The Pennsylvania State UniversityPatvirtintas el. paštas psu.edu
Stefano ErmonStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Xuegong ZhangTsinghua UniversityPatvirtintas el. paštas tsinghua.edu.cn
Jian MaRay and Stephanie Lane Professor of Computational Biology, Carnegie Mellon UniversityPatvirtintas el. paštas cs.cmu.edu
Gene Yeo PhD MBAProfessor of Cellular and Molecular Medicine at UC San DiegoPatvirtintas el. paštas ucsd.edu
Wei-Ying MaTsinghua UniversityPatvirtintas el. paštas air.tsinghua.edu.cn

Stebėti

Jianzhu Ma

Tsinghua University

Patvirtintas el. paštas tsinghua.edu.cn - Pagrindinis puslapis

Machine Learning Computational Biology Bioinformatics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Protein secondary structure prediction using deep convolutional neural fields S Wang, J Peng, J Ma, J Xu Scientific reports 6 (1), 18962, 2016	707	2016
Using deep learning to model the hierarchical structure and function of a cell J Ma, MK Yu, S Fong, K Ono, E Sage, B Demchak, R Sharan, T Ideker Nature methods 15 (4), 290-298, 2018	423	2018
High-resolution de novo structure prediction from primary sequence R Wu, F Ding, R Wang, R Shen, X Zhang, S Luo, C Su, Z Wu, Q Xie, ... BioRxiv, 2022.07. 21.500999, 2022	389	2022
Predicting drug response and synergy using a deep learning model of human cancer cells BM Kuenzi, J Park, SH Fong, KS Sanchez, J Lee, JF Kreisberg, J Ma, ... Cancer cell 38 (5), 672-684. e6, 2020	387	2020
RaptorX server: a resource for template-based protein structure modeling M Källberg, G Margaryan, S Wang, J Ma, J Xu Protein structure prediction, 17-27, 2014	313	2014
A 3D generative model for structure-based drug design S Luo, J Guan, J Ma, J Peng Advances in Neural Information Processing Systems 34, 6229-6239, 2021	232	2021
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures S Luo, Y Su, X Peng, S Wang, J Peng, J Ma Advances in Neural Information Processing Systems 35, 9754-9767, 2022	226	2022
Protein structure alignment beyond spatial proximity S Wang, J Ma, J Peng, J Xu Scientific reports 3 (1), 1448, 2013	222	2013
Pocket2mol: Efficient molecular sampling based on 3d protein pockets X Peng, S Luo, J Guan, Q Xie, J Peng, J Ma International Conference on Machine Learning, 17644-17655, 2022	209	2022
Protein threading using context-specific alignment potential J Ma, S Wang, F Zhao, J Xu Bioinformatics 29 (13), i257-i265, 2013	181	2013
Instaflow: One step is enough for high-quality diffusion-based text-to-image generation X Liu, X Zhang, J Ma, J Peng The Twelfth International Conference on Learning Representations, 2023	175	2023
3d equivariant diffusion for target-aware molecule generation and affinity prediction J Guan, WW Qian, X Peng, Y Su, J Peng, J Ma arXiv preprint arXiv:2303.03543, 2023	172	2023
Large-scale foundation model on single-cell transcriptomics M Hao, J Gong, X Zeng, C Liu, Y Guo, X Cheng, T Wang, J Ma, X Zhang, ... Nature methods 21 (8), 1481-1491, 2024	167	2024
Visible machine learning for biomedicine KY Michael, J Ma, J Fisher, JF Kreisberg, BJ Raphael, T Ideker Cell 173 (7), 1562-1565, 2018	166	2018
SARS-CoV-2 exacerbates proinflammatory responses in myeloid cells through C-type lectin receptors and Tweety family member 2 Q Lu, J Liu, S Zhao, MFG Castro, M Laurent-Rolle, J Dong, X Ran, ... Immunity 54 (6), 1304-1319. e9, 2021	152	2021
Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation J Zhou, J Ma, Y Chen, C Cheng, B Bao, J Peng, TJ Sejnowski, JR Dixon, ... Proceedings of the National Academy of Sciences 116 (28), 14011-14018, 2019	152	2019
Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns S Suresh, V Budde, J Neville, P Li, J Ma Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021	151	2021
Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients J Ma, SH Fong, Y Luo, CJ Bakkenist, JP Shen, S Mourragui, LFA Wessels, ... Nature Cancer 2 (2), 233-244, 2021	148	2021
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields S Wang, J Ma, J Xu Bioinformatics 32 (17), i672-i679, 2016	143	2016
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning J Ma, S Wang, Z Wang, J Xu Bioinformatics 31 (21), 3506-3513, 2015	142	2015

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