Feng‐Biao Guo

3.0k total citations
94 papers, 2.3k citations indexed

About

Feng‐Biao Guo is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Feng‐Biao Guo has authored 94 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Molecular Biology, 14 papers in Genetics and 10 papers in Ecology. Recurrent topics in Feng‐Biao Guo's work include Genomics and Phylogenetic Studies (53 papers), RNA and protein synthesis mechanisms (53 papers) and Machine Learning in Bioinformatics (31 papers). Feng‐Biao Guo is often cited by papers focused on Genomics and Phylogenetic Studies (53 papers), RNA and protein synthesis mechanisms (53 papers) and Machine Learning in Bioinformatics (31 papers). Feng‐Biao Guo collaborates with scholars based in China, Hong Kong and United States. Feng‐Biao Guo's co-authors include Jian Huang, Hao Lin, Hui Ding, Wen Wei, Yuan‐Nong Ye, Nini Rao, Chuan Dong, Chun‐Ting Zhang, Wei Chen and Anying Zhang and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Feng‐Biao Guo

90 papers receiving 2.3k citations

Peers

Feng‐Biao Guo

ECOLO

RNMI

GENET

CTM

Jean-François Gibrat France

Andreas Grote Germany

Carl Schmitz Australia

Andrey Shelenkov Russia

Dzmitry Padhorny United States

Maurice Scheer Germany

Robin Pearce United States

Kunchur Guruprasad India

Lars Kiemer Denmark

Petra Langendijk-Genevaux Switzerland

Jean-François Gibrat France

Feng‐Biao Guo

1.9k ×1.0

170 ×0.6

ECOLO

80 ×0.4

RNMI

220 ×1.2

GENET

144 ×0.8

CTM

Citations per year, relative to Feng‐Biao Guo Feng‐Biao Guo (= 1×) peers Jean-François Gibrat

Countries citing papers authored by Feng‐Biao Guo

Since Specialization

Citations

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

Fields of papers citing papers by Feng‐Biao Guo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng‐Biao Guo. 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 Feng‐Biao Guo. The network helps show where Feng‐Biao Guo may publish in the future.

Co-authorship network of co-authors of Feng‐Biao Guo

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

All Works

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20 of 20 papers shown

Guo, Feng‐Biao, et al.. (2025). A computer‐aided drug repurposing: the antibacterial agents targeting GroEL. British Journal of Pharmacology. 183(7). 1390–1403.

He, Yao, Shishi Liu, Long Chen, et al.. (2024). Versatile plant genome engineering using anti-CRISPR-Cas12a systems. Science China Life Sciences. 67(12). 2730–2745. 4 indexed citations

Ye, Yuan‐Nong, et al.. (2021). Quantitative elucidation of associations between nucleotide identity and physicochemical properties of amino acids and the functional insight. Computational and Structural Biotechnology Journal. 19. 4042–4048. 3 indexed citations

Wang, Beibei, et al.. (2021). BioMaster: An Integrated Database and Analytic Platform to Provide Comprehensive Information About BioBrick Parts. Frontiers in Microbiology. 12. 593979–593979. 3 indexed citations

Liu, Shuo, et al.. (2019). Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes. Frontiers in Microbiology. 10. 1236–1236. 33 indexed citations

Yang, Hui, Wang‐Ren Qiu, Feng‐Biao Guo, et al.. (2018). iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. International Journal of Biological Sciences. 14(8). 883–891. 132 indexed citations

Du, Meng‐Ze, et al.. (2018). The GC Content as a Main Factor Shaping the Amino Acid Usage During Bacterial Evolution Process. Frontiers in Microbiology. 9. 2948–2948. 32 indexed citations

Guo, Feng‐Biao, Chuan Dong, Hong‐Li Hua, et al.. (2017). Accurate prediction of human essential genes using only nucleotide composition and association information. Bioinformatics. 33(12). 1758–1764. 46 indexed citations

Ye, Yuan‐Nong, et al.. (2017). SSER: Species specific essential reactions database. BMC Systems Biology. 11(1). 50–50. 3 indexed citations

10.

Dong, Chuan, Hong‐Li Hua, Yuan‐Nong Ye, et al.. (2016). Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots. Molecular BioSystems. 12(9). 2893–2900. 18 indexed citations

11.

Lin, Yan, et al.. (2015). ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes. Nucleic Acids Research. 43(W1). W85–W90. 21 indexed citations

12.

Li, Yi, Nini Rao, Feng Yang, et al.. (2014). Biocomputional construction of a gene network under acid stress in Synechocystis sp. PCC 6803. Research in Microbiology. 165(6). 420–428. 8 indexed citations

13.

Guo, Feng‐Biao, Zhongkui Xia, Wen Wei, & Hailong Zhao. (2014). Statistical analyses of conserved features of genomic islands in bacteria. Genetics and Molecular Research. 13(1). 1782–1793. 5 indexed citations

14.

Ding, Hui, Hao Lin, Wei Chen, et al.. (2014). Prediction of protein structural classes based on feature selection technique. Interdisciplinary Sciences Computational Life Sciences. 6(3). 235–240. 28 indexed citations

15.

Ning, Luwen, et al.. (2014). Analysis of the Relationship between Genomic GC Content and Patterns of Base Usage, Codon Usage and Amino Acid Usage in Prokaryotes: Similar GC Content Adopts Similar Compositional Frequencies Regardless of the Phylogenetic Lineages. PLoS ONE. 9(9). e107319–e107319. 28 indexed citations

16.

Wei, Wen, Luwen Ning, Yuan‐Nong Ye, & Feng‐Biao Guo. (2013). Geptop: A Gene Essentiality Prediction Tool for Sequenced Bacterial Genomes Based on Orthology and Phylogeny. PLoS ONE. 8(8). e72343–e72343. 61 indexed citations

17.

Guo, Feng‐Biao, Yuan‐Nong Ye, Hailong Zhao, Dan Lin, & Wei Wang. (2012). Universal Pattern and Diverse Strengths of Successive Synonymous Codon Bias in Three Domains of Life, Particularly Among Prokaryotic Genomes. DNA Research. 19(6). 477–485. 13 indexed citations

18.

Du, Meng‐Ze, Feng‐Biao Guo, & Yueyun Chen. (2011). Gene Re-annotation in Genome of the ExtremophilePyrobaculum Aerophilumby Using Bioinformatics Methods. Journal of Biomolecular Structure and Dynamics. 29(2). 391–401. 5 indexed citations

19.

Guo, Feng‐Biao & Yan Lin. (2009). Identify Protein-coding Genes in the Genomes ofAeropyrum pernixK1 andChlorobium tepidumTLS. Journal of Biomolecular Structure and Dynamics. 26(4). 413–420. 14 indexed citations

20.

Guo, Feng‐Biao, Hao Lin, & Jian Huang. (2009). A plot of G + C content against sequence length of 640 bacterial chromosomes shows the points are widely scattered in the upper triangular area. Chromosome Research. 17(3). 359–364. 12 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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