Lantian Yao

1.9k total citations
30 papers, 370 citations indexed

About

Lantian Yao is a scholar working on Molecular Biology, Microbiology and Computational Theory and Mathematics. According to data from OpenAlex, Lantian Yao has authored 30 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 12 papers in Microbiology and 4 papers in Computational Theory and Mathematics. Recurrent topics in Lantian Yao's work include Machine Learning in Bioinformatics (15 papers), Antimicrobial Peptides and Activities (12 papers) and vaccines and immunoinformatics approaches (9 papers). Lantian Yao is often cited by papers focused on Machine Learning in Bioinformatics (15 papers), Antimicrobial Peptides and Activities (12 papers) and vaccines and immunoinformatics approaches (9 papers). Lantian Yao collaborates with scholars based in China, Taiwan and Hong Kong. Lantian Yao's co-authors include Tzong-Yi Lee, Yuxuan Pang, Ying‐Chih Chiang, Zhuo Wang, Chia‐Ru Chung, Jiahui Guan, Jhih-Hua Jhong, Wenshuo Li, Hsien‐Da Huang and Yixian Huang and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Lantian Yao

25 papers receiving 367 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Lantian Yao 322 113 57 53 32 30 370
Yuxuan Pang 213 0.7× 90 0.8× 33 0.6× 7 0.1× 36 1.1× 17 257
Ya-Wei Zhao 621 1.9× 40 0.4× 70 1.2× 123 2.3× 6 0.2× 8 667
Weiliang Zhu 733 2.3× 25 0.2× 216 3.8× 39 0.7× 20 0.6× 8 776
Bi‐Qian Sun 834 2.6× 22 0.2× 72 1.3× 28 0.5× 9 0.3× 8 872
Yihe Pang 355 1.1× 29 0.3× 60 1.1× 39 0.7× 10 0.3× 11 401
Lesong Wei 338 1.0× 89 0.8× 129 2.3× 6 0.1× 7 0.2× 13 383
Tadakazu Takakura 152 0.5× 74 0.7× 108 1.9× 12 0.2× 21 0.7× 9 332
Javier Klett 192 0.6× 32 0.3× 34 0.6× 11 0.2× 32 1.0× 18 319
Runyu Jing 247 0.8× 23 0.2× 57 1.0× 27 0.5× 10 0.3× 46 314
Wenjia He 271 0.8× 47 0.4× 36 0.6× 23 0.4× 3 0.1× 14 320

Countries citing papers authored by Lantian Yao

Since Specialization
Citations

This map shows the geographic impact of Lantian Yao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lantian Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lantian Yao more than expected).

Fields of papers citing papers by Lantian Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lantian Yao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lantian Yao. The network helps show where Lantian Yao may publish in the future.

Co-authorship network of co-authors of Lantian Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Lantian Yao. A scholar is included among the top collaborators of Lantian Yao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lantian Yao. Lantian Yao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lin, Xiukun, Feng Wang, Yiyang Zhao, et al.. (2025). AFPDeepPred: A Deep Learning Framework for Accurate Identification of Antifreeze Proteins. Journal of Chemical Information and Modeling. 65(22). 12256–12267.
2.
Yao, Lantian, et al.. (2025). ToxiPep: Peptide toxicity prediction via fusion of context-aware representation and atomic-level graph. Computational and Structural Biotechnology Journal. 27. 2347–2358. 3 indexed citations
3.
Yao, Lantian, et al.. (2025). StackDILI: Enhancing Drug-Induced Liver Injury Prediction through Stacking Strategy with Effective Molecular Representations. Journal of Chemical Information and Modeling. 65(2). 1027–1039. 2 indexed citations
4.
He, Xi, Feng Wang, Zhihao Zhao, et al.. (2025). PhaseNet: A computational framework for identifying phase-separating proteins based on protein language model. International Journal of Biological Macromolecules. 334(Pt 1). 149044–149044.
5.
He, Xi, et al.. (2025). CAP-m7G: A capsule network-based framework for specific RNA N7-methylguanosine site identification using image encoding and reconstruction layers. Computational and Structural Biotechnology Journal. 27. 804–812. 1 indexed citations
6.
Yao, Lantian, et al.. (2025). Caps-ac4C: An effective computational framework for identifying N4-acetylcytidine sites in human mRNA based on deep learning. Journal of Molecular Biology. 437(6). 168961–168961. 1 indexed citations
7.
Zhao, Zhihao, Yulan Liu, Cheng Zhang, et al.. (2025). Toward high-efficiency, low-resource, and explainable neuropeptide prediction with MSKDNP. Briefings in Bioinformatics. 26(5).
8.
Zhao, Zhihao, et al.. (2024). DeepKlapred: A deep learning framework for identifying protein lysine lactylation sites via multi-view feature fusion. International Journal of Biological Macromolecules. 283(Pt 3). 137668–137668. 3 indexed citations
9.
Yao, Lantian, Chia‐Ru Chung, Yixian Huang, et al.. (2024). KinPred-RNA—kinase activity inference and cancer type classification using machine learning on RNA-seq data. iScience. 27(4). 109333–109333. 2 indexed citations
10.
Yao, Lantian, Jiahui Guan, Chia‐Ru Chung, et al.. (2024). ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network. Briefings in Bioinformatics. 25(5). 10 indexed citations
11.
Yao, Lantian, Chia‐Ru Chung, Wenyang Zhang, et al.. (2024). dbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era. Nucleic Acids Research. 53(D1). D364–D376. 19 indexed citations
12.
Yao, Lantian, et al.. (2024). AMPActiPred: A three‐stage framework for predicting antibacterial peptides and activity levels with deep forest. Protein Science. 33(6). e5006–e5006. 17 indexed citations
13.
Chung, Chia‐Ru, Yun Tang, Shangfu Li, et al.. (2024). dbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research. Nucleic Acids Research. 53(D1). D377–D386. 5 indexed citations
14.
15.
Huang, Yixian, Hsi‐Yuan Huang, Hsi‐Yuan Huang, et al.. (2023). A Robust Drug–Target Interaction Prediction Framework with Capsule Network and Transfer Learning. International Journal of Molecular Sciences. 24(18). 14061–14061. 14 indexed citations
16.
Yao, Lantian, Wenshuo Li, Yuntian Zhang, et al.. (2023). Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation. International Journal of Molecular Sciences. 24(5). 4328–4328. 21 indexed citations
17.
Guan, Jiahui, Lantian Yao, Chia‐Ru Chung, Ying‐Chih Chiang, & Tzong-Yi Lee. (2023). StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture. International Journal of Molecular Sciences. 24(12). 10348–10348. 12 indexed citations
18.
Yao, Lantian, Wenshuo Li, Chia‐Ru Chung, et al.. (2023). DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning. Protein Science. 32(10). e4758–e4758. 24 indexed citations
19.
Luo, Mengqi, Shangfu Li, Yuxuan Pang, et al.. (2022). Extraction of microRNA–target interaction sentences from biomedical literature by deep learning approach. Briefings in Bioinformatics. 24(1). 5 indexed citations
20.
Ma, Renfei, Shangfu Li, Wenshuo Li, et al.. (2022). KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-Specific Phosphorylation Sites. Genomics Proteomics & Bioinformatics. 21(1). 228–241. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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