Weida Tong

30.1k total citations · 2 hit papers
344 papers, 13.3k citations indexed

About

Weida Tong is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Weida Tong has authored 344 papers receiving a total of 13.3k indexed citations (citations by other indexed papers that have themselves been cited), including 190 papers in Molecular Biology, 103 papers in Computational Theory and Mathematics and 58 papers in Genetics. Recurrent topics in Weida Tong's work include Computational Drug Discovery Methods (103 papers), Gene expression and cancer classification (77 papers) and Bioinformatics and Genomic Networks (54 papers). Weida Tong is often cited by papers focused on Computational Drug Discovery Methods (103 papers), Gene expression and cancer classification (77 papers) and Bioinformatics and Genomic Networks (54 papers). Weida Tong collaborates with scholars based in United States, China and United Kingdom. Weida Tong's co-authors include Roger Perkins, Hong Fang, Huixiao Hong, Minjun Chen, Leming Shi, Daniel M. Sheehan, Jürgen Borlak, Ruth Roberts, William J. Welsh and Qian Xie and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Weida Tong

333 papers receiving 12.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

FDA-approved drug labeling for the study of drug-induced liver injury

2011 296 citations Minjun Chen, Zhichao Liu et al. Drug Discovery Today profile →
DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans

2016 267 citations Minjun Chen, Shraddha Thakkar et al. Drug Discovery Today profile →

Author Peers

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

Author						Papers	Cites
Weida Tong	5.5k	3.6k	2.0k	1.6k	1.5k	344	13.3k
Christopher P. Austin	5.7k 1.0×	2.5k 0.7×	693 0.3×	881 0.5×	866 0.6×	172	11.9k
Ruili Huang	3.3k 0.6×	2.6k 0.7×	793 0.4×	602 0.4×	1.3k 0.8×	219	8.0k
Evan Bolton	7.3k 1.3×	5.1k 1.4×	1.3k 0.6×	414 0.3×	631 0.4×	71	14.6k
Kamil Kuča	6.5k 1.2×	2.7k 0.8×	884 0.4×	511 0.3×	3.1k 2.0×	1.1k	31.6k
Huixiao Hong	3.0k 0.6×	2.2k 0.6×	649 0.3×	830 0.5×	2.4k 1.6×	227	9.0k
Benjamin A. Shoemaker	8.3k 1.5×	4.9k 1.4×	1.2k 0.6×	495 0.3×	379 0.3×	38	14.7k
Menghang Xia	3.2k 0.6×	1.9k 0.5×	666 0.3×	590 0.4×	1.5k 1.0×	231	7.6k
Paul Thiessen	6.0k 1.1×	4.3k 1.2×	1.1k 0.6×	315 0.2×	625 0.4×	38	12.8k
Siqian He	6.1k 1.1×	4.3k 1.2×	1.1k 0.6×	403 0.2×	379 0.3×	14	12.4k

Countries citing papers authored by Weida Tong

Since Specialization

Citations

This map shows the geographic impact of Weida Tong'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 Weida Tong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weida Tong more than expected).

Fields of papers citing papers by Weida Tong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Weida Tong. 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 Weida Tong. The network helps show where Weida Tong may publish in the future.

Co-authorship network of co-authors of Weida Tong

This figure shows the co-authorship network connecting the top 25 collaborators of Weida Tong. A scholar is included among the top collaborators of Weida Tong 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 Weida Tong. Weida Tong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Wu, Leihong, et al.. (2025). Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel. Drug Safety. 48(6). 655–665.

2.

Chen, Minjun, et al.. (2025). New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now?. Drug Discovery Today. 30(9). 104452–104452. 2 indexed citations

3.

Meier, Matthew J., Joshua Harrill, Kamin J. Johnson, et al.. (2024). Progress in toxicogenomics to protect human health. Nature Reviews Genetics. 26(2). 105–122. 21 indexed citations

4.

Yu, Ying, Naixin Zhang, Luyao Ren, et al.. (2023). Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method. Genome biology. 24(1). 201–201. 27 indexed citations

5.

Wu, Jun, Jian Ouyang, Jiajia Zhou, et al.. (2023). PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model. Bioinformatics. 39(11). 18 indexed citations

6.

Deveson, Ira W., Bindu Swapna Madala, Ted Wong, et al.. (2022). Using synthetic chromosome controls to evaluate the sequencing of difficult regions within the human genome. Genome biology. 23(1). 19–19. 5 indexed citations

7.

Li, Ting, Weida Tong, Ruth Roberts, Zhichao Liu, & Shraddha Thakkar. (2021). DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation. Frontiers in Artificial Intelligence. 4. 757780–757780. 39 indexed citations

8.

Meehan, Joe, et al.. (2020). DLI-IT: a deep learning approach to drug label identification through image and text embedding. BMC Medical Informatics and Decision Making. 20(1). 14 indexed citations

9.

Rathman, James F., Chihae Yang, Aleksandra Mostrąg, et al.. (2020). Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment. Chemical Research in Toxicology. 34(2). 601–615. 10 indexed citations

10.

Li, Dongying, Bridgett Knox, Si Chen, et al.. (2019). MicroRNAs hsa-miR-495-3p and hsa-miR-486-5p suppress basal and rifampicin-induced expression of human sulfotransferase 2A1 (SULT2A1) by facilitating mRNA degradation. Biochemical Pharmacology. 169. 113617–113617. 16 indexed citations

11.

Li, Dongying, William H. Tolleson, Dianke Yu, et al.. (2019). Regulation of cytochrome P450 expression by microRNAs and long noncoding RNAs: Epigenetic mechanisms in environmental toxicology and carcinogenesis. Journal of Environmental Science and Health Part C. 37(3). 180–214. 55 indexed citations

12.

Li, Dongying, William H. Tolleson, Dianke Yu, et al.. (2019). Regulation of cytochrome P450 expression by microRNAs and long noncoding RNAs: Epigenetic mechanisms in environmental toxicology and carcinogenesis. Journal of Environmental Science and Health Part A Environmental Science and Engineering and Toxicology. 4 indexed citations

13.

Fang, Hong, Stephen Harris, Minjun Chen, et al.. (2017). ArrayTrack: An FDA and Public Genomic Tool. Methods in molecular biology. 333–353. 10 indexed citations

14.

Wu, Leihong, Zhichao Liu, Joshua Xu, et al.. (2015). NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis. Biomarkers in Medicine. 9(11). 1053–1065. 7 indexed citations

15.

Miller, Margaret A., et al.. (2012). 2012 Global Summit on Regulatory Science (GSRS-2012)—Modernizing Toxicology. Toxicological Sciences. 131(1). 9–12. 4 indexed citations

16.

Rocca‐Serra, Philippe, Marco Brandizi, Eamonn Maguire, et al.. (2010). ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics. 26(18). 2354–2356. 171 indexed citations

17.

Fang, Hong, Weida Tong, Leming Shi, Robert L. Jakab, & John F. Bowyer. (2004). Classification of cDNA Array Genes That Have a Highly Significant Discriminative Power Due to Their Unique Distribution in Four Brain Regions. DNA and Cell Biology. 23(10). 661–674. 3 indexed citations

18.

Hong, Huixiao, Weida Tong, Roger Perkins, et al.. (2004). Multiclass Decision Forest—A Novel Pattern Recognition Method for Multiclass Classification in Microarray Data Analysis. DNA and Cell Biology. 23(10). 685–694. 29 indexed citations

19.

Tong, Weida, Roger Perkins, Xing Li, William J. Welsh, & Daniel M. Sheehan. (1997). QSAR Models for Binding of Estrogenic Compounds to Estrogen Receptor α and β Subtypes. Endocrinology. 138(9). 4022–4025. 117 indexed citations

20.

Tong, Weida. (1997). QSAR Models for Binding of Estrogenic Compounds to Estrogen Receptor and Subtypes. Endocrinology. 138(9). 4022–4025. 42 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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