Maxat Kulmanov

2.7k total citations
22 papers, 1.0k citations indexed

About

Maxat Kulmanov is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Maxat Kulmanov has authored 22 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 5 papers in Genetics and 2 papers in Plant Science. Recurrent topics in Maxat Kulmanov's work include Bioinformatics and Genomic Networks (15 papers), Biomedical Text Mining and Ontologies (12 papers) and Machine Learning in Bioinformatics (11 papers). Maxat Kulmanov is often cited by papers focused on Bioinformatics and Genomic Networks (15 papers), Biomedical Text Mining and Ontologies (12 papers) and Machine Learning in Bioinformatics (11 papers). Maxat Kulmanov collaborates with scholars based in Saudi Arabia, United Kingdom and United States. Maxat Kulmanov's co-authors include Robert Hoehndorf, Fatima Zohra Smaili, Xin Gao, Paul N. Schofield, Georgios V. Gkoutos, Imane Boudellioua, Stefan T. Arold, Francisco J. Guzmán‐Vega, Lydie Lane and Paula Duek and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Scientific Reports.

In The Last Decade

Maxat Kulmanov

21 papers receiving 987 citations

Peers

Maxat Kulmanov

CTM

GENET

Vladimir Gligorijević United States

Öznur Taştan Türkiye

Thomas Thorne United Kingdom

Joshua Meier United States

Gaston K. Mazandu South Africa

Ehsaneddin Asgari United States

Dan Ofer Israel

Ezekiel Adebiyi Nigeria

Mehdi Mirzaie Iran

Hiroaki Iwata Japan

Vladimir Gligorijević United States

Maxat Kulmanov

937 ×1.1

246 ×1.3

CTM

111 ×1.0

64 ×0.7

GENET

31 ×0.9

Citations per year, relative to Maxat Kulmanov Maxat Kulmanov (= 1×) peers Vladimir Gligorijević

Countries citing papers authored by Maxat Kulmanov

Since Specialization

Citations

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

Fields of papers citing papers by Maxat Kulmanov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxat Kulmanov

This figure shows the co-authorship network connecting the top 25 collaborators of Maxat Kulmanov. A scholar is included among the top collaborators of Maxat Kulmanov 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 Maxat Kulmanov. Maxat Kulmanov 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

Kulmanov, Maxat, Masao Nagasaki, Yasuyuki Ohkawa, et al.. (2025). Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia. Scientific Data. 12(1). 1316–1316.

Hoehndorf, Robert, et al.. (2024). DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction. Scientific Reports. 14(1). 31813–31813. 1 indexed citations

Kulmanov, Maxat, Francisco J. Guzmán‐Vega, Paula Duek, et al.. (2024). Protein function prediction as approximate semantic entailment. Nature Machine Intelligence. 6(2). 220–228. 45 indexed citations

Kulmanov, Maxat, Mohammed Alarawi, Dana Alhattab, et al.. (2024). A reference quality, fully annotated diploid genome from a Saudi individual. Scientific Data. 11(1). 1278–1278. 2 indexed citations

Kulmanov, Maxat, et al.. (2024). Predicting protein functions using positive-unlabeled ranking with ontology-based priors. Bioinformatics. 40(Supplement_1). i401–i409. 3 indexed citations

Kulmanov, Maxat, et al.. (2022). mOWL: Python library for machine learning with biomedical ontologies. Bioinformatics. 39(1). 6 indexed citations

Kulmanov, Maxat & Robert Hoehndorf. (2022). DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms. Bioinformatics. 38(Supplement_1). i238–i245. 40 indexed citations

Álam, Intikhab, Allan Kamau, Maxat Kulmanov, et al.. (2020). Functional Pangenome Analysis Shows Key Features of E Protein Are Preserved in SARS and SARS-CoV-2. Frontiers in Cellular and Infection Microbiology. 10. 405–405. 39 indexed citations

Kulmanov, Maxat & Robert Hoehndorf. (2020). DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Computational Biology. 16(11). e1008453–e1008453. 17 indexed citations

10.

Kulmanov, Maxat, Fatima Zohra Smaili, Xin Gao, & Robert Hoehndorf. (2020). Semantic similarity and machine learning with ontologies. Briefings in Bioinformatics. 22(4). 90 indexed citations

11.

Kulmanov, Maxat & Robert Hoehndorf. (2019). DeepGOPlus: improved protein function prediction from sequence. Bioinformatics. 36(2). 422–429. 265 indexed citations

12.

Boudellioua, Imane, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, & Robert Hoehndorf. (2019). DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics. 20(1). 65–65. 52 indexed citations

13.

Kafkas, Şenay, et al.. (2019). PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research. Scientific Data. 6(1). 79–79. 9 indexed citations

14.

Weiland, Claus, Maxat Kulmanov, Marco Schmidt, & Robert Hoehndorf. (2019). A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs. Biodiversity Information Science and Standards. 3. 1 indexed citations

15.

Kulmanov, Maxat, Paul N. Schofield, Georgios V. Gkoutos, & Robert Hoehndorf. (2018). Ontology-based validation and identification of regulatory phenotypes. Bioinformatics. 34(17). i857–i865. 4 indexed citations

16.

Boudellioua, Imane, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, & Robert Hoehndorf. (2018). OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants. Scientific Reports. 8(1). 14681–14681. 14 indexed citations

17.

Kulmanov, Maxat & Robert Hoehndorf. (2017). Evaluating the effect of annotation size on measures of semantic similarity. Journal of Biomedical Semantics. 8(1). 7–7. 19 indexed citations

18.

Salhi, Adil, Sónia Negrão, Magbubah Essack, et al.. (2017). DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species. Scientific Reports. 7(1). 5968–5968. 9 indexed citations

19.

Boudellioua, Imane, Rozaimi Razali, Maxat Kulmanov, et al.. (2017). Semantic prioritization of novel causative genomic variants. PLoS Computational Biology. 13(4). e1005500–e1005500. 25 indexed citations

20.

Kulmanov, Maxat, et al.. (2017). DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics. 34(4). 660–668. 329 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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