| Deep learning in bioinformatics: Introduction, application, and perspective in the big data era Y Li, C Huang, L Ding, Z Li, Y Pan, X Gao Methods 166, 4-21, 2019 | 395 | 2019 |
| DEEPre: sequence-based enzyme EC number prediction by deep learning Y Li, S Wang, R Umarov, B Xie, M Fan, L Li, X Gao Bioinformatics, btx680, 2017 | 292 | 2017 |
| Towards artificial general intelligence via a multimodal foundation model N Fei, Z Lu, Y Gao, G Yang, Y Huo, J Wen, H Lu, R Song, X Gao, T Xiang, ... Nature Communications 13 (1), 1-13, 2022 | 212 | 2022 |
| Bioinformatics clouds for big data manipulation L Dai, X Gao, Y Guo, J Xiao, Z Zhang Biology direct 7 (1), 43, 2012 | 210 | 2012 |
| A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis L Zhou, Z Li, J Zhou, H Li, Y Chen, Y Huang, D Xie, L Zhao, M Fan, ... IEEE Transactions on Medical Imaging 39 (8), 2638-2652, 2020 | 209 | 2020 |
| Semantic similarity and machine learning with ontologies M Kulmanov, FZ Smaili, X Gao, R Hoehndorf Briefings in Bioinformatics, 2020 | 192 | 2020 |
| Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine AH Emwas, E Saccenti, X Gao, RT McKay, VAPM Dos Santos, R Roy, ... Metabolomics 14, 1-23, 2018 | 175 | 2018 |
| Opa2vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction FZ Smaili, X Gao, R Hoehndorf Bioinformatics 35 (12), 2133-2140, 2019 | 169 | 2019 |
| Recommendations and standardization of biomarker quantification using NMR-based metabolomics with particular focus on urinary analysis AH Emwas, R Roy, RT McKay, D Ryan, L Brennan, L Tenori, C Luchinat, ... Journal of proteome research 15 (2), 360-373, 2016 | 168 | 2016 |
| Opportunities and challenges for ChatGPT and large language models in biomedicine and health S Tian, Q Jin, L Yeganova, PT Lai, Q Zhu, X Chen, Y Yang, Q Chen, ... Briefings in Bioinformatics 25 (1), bbad493, 2024 | 167 | 2024 |
| GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization P Han, P Yang, P Zhao, S Shang, Y Liu, J Zhou, X Gao, P Kalnis Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 161 | 2019 |
| Non-negative matrix factorization by maximizing correntropy for cancer clustering JJY Wang, X Wang, X Gao BMC bioinformatics 14, 1-11, 2013 | 152 | 2013 |
| Onto2vec: joint vector-based representation of biological entities and their ontology-based annotations FZ Smaili, X Gao, R Hoehndorf Bioinformatics 34 (13), i52-i60, 2018 | 146 | 2018 |
| Learning from weak and noisy labels for semantic segmentation Z Lu, Z Fu, T Xiang, P Han, L Wang, X Gao IEEE transactions on pattern analysis and machine intelligence 39 (3), 486-500, 2017 | 138 | 2017 |
| Multiple graph regularized nonnegative matrix factorization JJY Wang, H Bensmail, X Gao Pattern Recognition 46 (10), 2840-2847, 2013 | 132 | 2013 |
| DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques MA Thafar, RS Olayan, H Ashoor, S Albaradei, VB Bajic, X Gao, ... Journal of Cheminformatics 12, 1-17, 2020 | 122 | 2020 |
| RNA Secondary Structure Prediction By Learning Unrolled Algorithms X Chen, Y Li, R Umarov, X Gao, L Song arXiv preprint arXiv:2002.05810, 2020 | 120 | 2020 |
| Transfer-medium-free nanofiber-reinforced graphene film and applications in wearable transparent pressure sensors H Ren, L Zheng, G Wang, X Gao, Z Tan, J Shan, L Cui, K Li, M Jian, L Zhu, ... Acs Nano 13 (5), 5541-5548, 2019 | 114 | 2019 |
| DeepSimulator: a deep simulator for Nanopore sequencing Y Li, R Han, C Bi, M Li, S Wang, X Gao Bioinformatics 34 (17), 2899-2908, 2018 | 113 | 2018 |
| Machine learning and deep learning methods that use omics data for metastasis prediction S Albaradei, M Thafar, A Alsaedi, C Van Neste, T Gojobori, M Essack, ... Computational and Structural Biotechnology Journal 19, 5008-5018, 2021 | 112 | 2021 |
Juexiao ZhouPhD Candidate, King Abdullah University of Science and Technology在 kaust.edu.sa 的电子邮件经过验证
