Daifeng Wang

14.2k total citations
58 papers, 945 citations indexed

About

Daifeng Wang is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Daifeng Wang has authored 58 papers receiving a total of 945 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 10 papers in Genetics and 6 papers in Cancer Research. Recurrent topics in Daifeng Wang's work include Bioinformatics and Genomic Networks (19 papers), Single-cell and spatial transcriptomics (15 papers) and Gene expression and cancer classification (11 papers). Daifeng Wang is often cited by papers focused on Bioinformatics and Genomic Networks (19 papers), Single-cell and spatial transcriptomics (15 papers) and Gene expression and cancer classification (11 papers). Daifeng Wang collaborates with scholars based in United States, China and Singapore. Daifeng Wang's co-authors include Nam D. Nguyen, Mark Gerstein, Joel Rozowsky, Ting Jin, Paul Muir, Jing Zhang, Shaoke Lou, Farren J. Isaacs, George M. Weinstock and Shantao Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Bioinformatics.

In The Last Decade

Daifeng Wang

56 papers receiving 939 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daifeng Wang United States 18 585 173 80 78 63 58 945
Toby Dylan Hocking United States 13 782 1.3× 323 1.9× 68 0.8× 96 1.2× 79 1.3× 33 1.2k
Nicole Rusk United States 14 610 1.0× 94 0.5× 99 1.2× 88 1.1× 63 1.0× 76 1.1k
Amir H. Assadi United States 13 517 0.9× 105 0.6× 49 0.6× 138 1.8× 27 0.4× 53 930
George C. Tseng United States 10 887 1.5× 237 1.4× 124 1.6× 67 0.9× 29 0.5× 12 1.2k
Xue Jiang China 18 513 0.9× 78 0.5× 112 1.4× 83 1.1× 49 0.8× 55 1.1k
Sara Ballouz United States 16 788 1.3× 186 1.1× 79 1.0× 80 1.0× 22 0.3× 28 984
Florian Hahne United States 10 1.1k 1.9× 201 1.2× 153 1.9× 112 1.4× 57 0.9× 17 1.6k
Qingsong Tang China 14 1.0k 1.7× 129 0.7× 163 2.0× 113 1.4× 30 0.5× 44 1.4k
James Brown United Kingdom 24 884 1.5× 132 0.8× 167 2.1× 113 1.4× 40 0.6× 60 1.6k
Wen Wang United States 25 771 1.3× 212 1.2× 129 1.6× 103 1.3× 82 1.3× 72 1.7k

Countries citing papers authored by Daifeng Wang

Since Specialization
Citations

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

Fields of papers citing papers by Daifeng Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daifeng Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Daifeng Wang. A scholar is included among the top collaborators of Daifeng Wang 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 Daifeng Wang. Daifeng Wang 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.
2.
Knaack, Sara, Stuart MacGregor, Masoumeh Hosseini, et al.. (2025). Single‐nucleus analysis reveals oxidative stress in Down syndrome basal forebrain neurons at birth. Alzheimer s & Dementia. 21(7). e70445–e70445. 1 indexed citations
3.
Gupta, Chirag, Declan Clarke, Saniya Khullar, et al.. (2025). Network-based drug repurposing for psychiatric disorders using single-cell genomics. Cell Genomics. 5(11). 101003–101003. 1 indexed citations
4.
Khullar, Saniya, et al.. (2024). NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation. Bioinformatics Advances. 5(1). vbae206–vbae206.
5.
Wang, Daifeng, et al.. (2023). MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology. Patterns. 4(11). 100847–100847. 1 indexed citations
6.
Khullar, Saniya & Daifeng Wang. (2023). Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer’s disease phenotypes and Covid-19 severity. Human Molecular Genetics. 32(11). 1797–1813. 7 indexed citations
7.
Shen, Minjie, Qiping Dong, Carissa L. Sirois, et al.. (2023). Elevated levels of FMRP-target MAP1B impair human and mouse neuronal development and mouse social behaviors via autophagy pathway. Nature Communications. 14(1). 3801–3801. 16 indexed citations
8.
Wang, Daifeng, et al.. (2023). CMOT: Cross-Modality Optimal Transport for multimodal inference. Genome biology. 24(1). 163–163. 11 indexed citations
9.
Gupta, Chirag, Jielin Xu, Ting Jin, et al.. (2022). Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease. PLoS Computational Biology. 18(7). e1010287–e1010287. 16 indexed citations
10.
Nguyen, Nam D., Jiawei Huang, & Daifeng Wang. (2022). A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data. Nature Computational Science. 2(1). 38–46. 29 indexed citations
11.
Wu, Yuchang, Junha Shin, Ye Zheng, et al.. (2021). Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLoS Genetics. 17(2). e1009309–e1009309. 11 indexed citations
12.
Fathi, Ali, Sakthikumar Mathivanan, Linghai Kong, et al.. (2021). Chemically induced senescence in human stem cell‐derived neurons promotes phenotypic presentation of neurodegeneration. Aging Cell. 21(1). e13541–e13541. 17 indexed citations
13.
Shen, Minjie, Qiping Dong, Yu Gao, et al.. (2021). FXR1 regulation of parvalbumin interneurons in the prefrontal cortex is critical for schizophrenia-like behaviors. Molecular Psychiatry. 26(11). 6845–6867. 23 indexed citations
14.
Huang, Jiawei, Jie Sheng, & Daifeng Wang. (2021). Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics. Communications Biology. 4(1). 1308–1308. 8 indexed citations
15.
Jin, Ting, Nam D. Nguyen, Flaminia Talos, & Daifeng Wang. (2020). ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages. Bioinformatics. 37(8). 1115–1124. 22 indexed citations
16.
Nguyen, Nam D., Ting Jin, & Daifeng Wang. (2020). Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes. Bioinformatics. 37(12). 1772–1775. 14 indexed citations
17.
Gao, Yu, Minjie Shen, Jose Gonzalez, et al.. (2020). RGS6 Mediates Effects of Voluntary Running on Adult Hippocampal Neurogenesis. Cell Reports. 32(5). 107997–107997. 24 indexed citations
18.
Yan, Koon‐Kiu, Daifeng Wang, Kun Xiong, & Mark Gerstein. (2020). Comparing Technological Development and Biological Evolution from a Network Perspective. Cell Systems. 10(3). 219–222. 1 indexed citations
19.
Wang, Daifeng, Fei He, Sergei Maslov, & Mark Gerstein. (2016). DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks. PLoS Computational Biology. 12(10). e1005146–e1005146. 5 indexed citations
20.
Muir, Paul, Shantao Li, Shaoke Lou, et al.. (2016). The real cost of sequencing: scaling computation to keep pace with data generation. Genome biology. 17(1). 53–53. 211 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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