Cen Wan

1.4k total citations
23 papers, 345 citations indexed

About

Cen Wan is a scholar working on Molecular Biology, Artificial Intelligence and Aging. According to data from OpenAlex, Cen Wan has authored 23 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 4 papers in Artificial Intelligence and 4 papers in Aging. Recurrent topics in Cen Wan's work include Bioinformatics and Genomic Networks (11 papers), Machine Learning in Bioinformatics (6 papers) and Gene expression and cancer classification (6 papers). Cen Wan is often cited by papers focused on Bioinformatics and Genomic Networks (11 papers), Machine Learning in Bioinformatics (6 papers) and Gene expression and cancer classification (6 papers). Cen Wan collaborates with scholars based in United Kingdom, China and Germany. Cen Wan's co-authors include David T. Jones, Alex A. Freitas, Rui Fa, Domenico Cozzetto, João Pedro de Magalhães, Chenhao Yang, Ashish Rajput, Jingwei Wang, Robi Tăcutu and Diogo Barardo and has published in prestigious journals such as Nature Communications, Blood and Bioinformatics.

In The Last Decade

Cen Wan

22 papers receiving 342 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cen Wan United Kingdom 10 218 77 52 49 46 23 345
А. В. Селиверстов Russia 11 209 1.0× 69 0.9× 27 0.5× 16 0.3× 75 1.6× 57 357
Ferhat Alkan Denmark 11 494 2.3× 65 0.8× 29 0.6× 28 0.6× 13 0.3× 18 573
Costas Bouyioukos France 7 629 2.9× 33 0.4× 59 1.1× 11 0.2× 41 0.9× 12 800
Chaoyang Zhang United States 14 496 2.3× 73 0.9× 46 0.9× 5 0.1× 68 1.5× 43 651
Domenica D’Elia Italy 15 334 1.5× 51 0.7× 35 0.7× 4 0.1× 53 1.2× 38 470
Simon Brent United Kingdom 8 328 1.5× 174 2.3× 70 1.3× 10 0.2× 13 0.3× 9 541
Michael E. Gruidl United States 8 340 1.6× 145 1.9× 21 0.4× 105 2.1× 41 0.9× 9 511
Daniela Butano United Kingdom 3 253 1.2× 50 0.6× 46 0.9× 30 0.6× 9 0.2× 5 331
Isaac Ho United States 5 344 1.6× 97 1.3× 93 1.8× 5 0.1× 25 0.5× 6 500
Kahn Rhrissorrakrai United States 11 226 1.0× 66 0.9× 17 0.3× 16 0.3× 17 0.4× 28 428

Countries citing papers authored by Cen Wan

Since Specialization
Citations

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

Fields of papers citing papers by Cen Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cen Wan

This figure shows the co-authorship network connecting the top 25 collaborators of Cen Wan. A scholar is included among the top collaborators of Cen Wan 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 Cen Wan. Cen Wan 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.
Bhatti, Gaurav, Herdiantri Sufriyana, Roberto Romero, et al.. (2025). Placental epigenetic clocks derived from crowdsourcing: Implications for the study of accelerated aging in obstetrics. iScience. 28(8). 113181–113181. 1 indexed citations
2.
3.
Buchan, Daniel, et al.. (2023). Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning. Briefings in Functional Genomics. 23(4). 441–451. 2 indexed citations
4.
Wan, Cen. (2022). Positive Feature Values Prioritized Hierarchical Dependency Constrained Averaged One-dependence Estimators for Gene Ontology Feature Spaces. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 8. 826–829. 2 indexed citations
5.
Wan, Cen. (2022). Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naïve Bayes Classifier for Hierarchical Feature Spaces. 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 12. 106–110. 3 indexed citations
6.
Huang, Zhen, Ivanete de Oliveira Furo, Jing Liu, et al.. (2022). Recurrent chromosome reshuffling and the evolution of neo-sex chromosomes in parrots. Nature Communications. 13(1). 944–944. 45 indexed citations
7.
8.
Wan, Cen & Alex A. Freitas. (2020). Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces.. BIROn (Birkbeck, University of London). 557–568. 2 indexed citations
9.
Xue, Ting, Duo Chen, Limin Liang, et al.. (2020). A high-quality genome provides insights into the new taxonomic status and genomic characteristics of Cladopus chinensis (Podostemaceae). Horticulture Research. 7(1). 46–46. 13 indexed citations
10.
Wan, Cen & David T. Jones. (2020). Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nature Machine Intelligence. 2(9). 540–550. 54 indexed citations
11.
Xiao, Shijun, Gang Lin, Duo Chen, et al.. (2019). Chromosome genome assembly and annotation of the yellowbelly pufferfish with PacBio and Hi-C sequencing data. Scientific Data. 6(1). 267–267. 18 indexed citations
12.
Wan, Cen, Domenico Cozzetto, Rui Fa, & David T. Jones. (2019). Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks. PLoS ONE. 14(7). e0209958–e0209958. 13 indexed citations
13.
Fa, Rui, Domenico Cozzetto, Cen Wan, & David T. Jones. (2018). Predicting human protein function with multi-task deep neural networks. PLoS ONE. 13(6). e0198216–e0198216. 49 indexed citations
14.
Wan, Cen. (2018). Hierarchical Feature Selection for Knowledge Discovery: Application of Data Mining to the Biology of Ageing. CERN Document Server (European Organization for Nuclear Research).
15.
Wan, Cen. (2018). Hierarchical Feature Selection for Knowledge Discovery. 3 indexed citations
16.
Wan, Cen & Alex A. Freitas. (2017). An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features. Artificial Intelligence Review. 50(2). 201–240. 24 indexed citations
17.
Wan, Cen, Jonathan Lees, Federico Minneci, Christine Orengo, & David T. Jones. (2017). Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster. PLoS Computational Biology. 13(10). e1005791–e1005791. 8 indexed citations
18.
Wan, Cen, Robi Tăcutu, Diogo Barardo, et al.. (2016). Systematic analysis of the gerontome reveals links between aging and age-related diseases. Human Molecular Genetics. 25(21). ddw307–ddw307. 64 indexed citations
19.
Wan, Cen & Alex A. Freitas. (2015). Two methods for constructing a gene ontology-based feature network for a Bayesian network classifier and applications to datasets of aging-related genes. Kent Academic Repository (University of Kent). 27–36. 5 indexed citations
20.
Wan, Cen & Alex A. Freitas. (2013). Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods. Kent Academic Repository (University of Kent). 373–380. 9 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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