Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique FY Dao, H Lv, F Wang, CQ Feng, H Ding, W Chen, H Lin Bioinformatics 35 (12), 2075-2083, 2019 | 192 | 2019 |
i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome W Chen, H Lv, F Nie, H Lin Bioinformatics 35 (16), 2796-2800, 2019 | 191 | 2019 |
Evaluation of different computational methods on 5-methylcytosine sites identification H Lv, ZM Zhang, SH Li, JX Tan, W Chen, H Lin Briefings in bioinformatics 21 (3), 982-995, 2020 | 137 | 2020 |
iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites in Homo sapiens H Yang, H Lv, H Ding, W Chen, H Lin Journal of computational biology 25 (11), 1266-1277, 2018 | 131 | 2018 |
Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method H Lv, FY Dao, ZX Guan, H Yang, YW Li, H Lin Briefings in bioinformatics 22 (4), bbaa255, 2021 | 109 | 2021 |
iDNA-MS: an integrated computational tool for detecting DNA modification sites in multiple genomes H Lv, FY Dao, D Zhang, ZX Guan, H Yang, W Su, ML Liu, H Ding, W Chen, ... Iscience 23 (4), 2020 | 87 | 2020 |
A computational platform to identify origins of replication sites in eukaryotes FY Dao, H Lv, H Zulfiqar, H Yang, W Su, H Gao, H Ding, H Lin Briefings in bioinformatics 22 (2), 1940-1950, 2021 | 86 | 2021 |
A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae H Yang, W Yang, FY Dao, H Lv, H Ding, W Chen, H Lin Briefings in Bioinformatics 21 (5), 1568-1580, 2020 | 83 | 2020 |
iRNA-m7G: identifying N7-methylguanosine sites by fusing multiple features W Chen, P Feng, X Song, H Lv, H Lin Molecular Therapy-Nucleic Acids 18, 269-274, 2019 | 82 | 2019 |
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design H Lv, L Shi, JW Berkenpas, FY Dao, H Zulfiqar, H Ding, Y Zhang, L Yang, ... Briefings in bioinformatics 22 (6), bbab320, 2021 | 79 | 2021 |
Computational identification of N6-methyladenosine sites in multiple tissues of mammals FY Dao, H Lv, YH Yang, H Zulfiqar, H Gao, H Lin Computational and structural biotechnology journal 18, 1084-1091, 2020 | 76 | 2020 |
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops FY Dao, H Lv, D Zhang, ZM Zhang, L Liu, H Lin Briefings in bioinformatics 22 (4), bbaa356, 2021 | 73 | 2021 |
iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features D Zhang, ZC Xu, W Su, YH Yang, H Lv, H Yang, H Lin Bioinformatics 37 (2), 171-177, 2021 | 72 | 2021 |
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach H Lv, FY Dao, H Zulfiqar, H Lin Briefings in Bioinformatics 22 (6), bbab244, 2021 | 68 | 2021 |
iDNA6mA-Rice: a computational tool for detecting N6-methyladenine sites in rice H Lv, FY Dao, ZX Guan, D Zhang, JX Tan, Y Zhang, W Chen, H Lin Frontiers in genetics 10, 793, 2019 | 63 | 2019 |
PPD: a manually curated database for experimentally verified prokaryotic promoters W Su, ML Liu, YH Yang, JS Wang, SH Li, H Lv, FY Dao, H Yang, H Lin Journal of Molecular Biology 433 (11), 166860, 2021 | 45 | 2021 |
A survey for predicting enzyme family classes using machine learning methods JX Tan, H Lv, F Wang, FY Dao, W Chen, H Ding Current drug targets 20 (5), 540-550, 2019 | 44 | 2019 |
Identifying phage virion proteins by using two-step feature selection methods JX Tan, FY Dao, H Lv, PM Feng, H Ding Molecules 23 (8), 2000, 2018 | 43 | 2018 |
Advances in mapping the epigenetic modifications of 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC) H Lv, FY Dao, D Zhang, H Yang, H Lin Biotechnology and Bioengineering 118 (11), 4204-4216, 2021 | 42 | 2021 |
A sequence-based deep learning approach to predict CTCF-mediated chromatin loop H Lv, FY Dao, H Zulfiqar, W Su, H Ding, L Liu, H Lin Briefings in bioinformatics 22 (5), bbab031, 2021 | 42 | 2021 |