Nawar Malhis

1.3k total citations
18 papers, 521 citations indexed

About

Nawar Malhis is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Nawar Malhis has authored 18 papers receiving a total of 521 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 4 papers in Genetics and 3 papers in Artificial Intelligence. Recurrent topics in Nawar Malhis's work include Protein Structure and Dynamics (8 papers), Machine Learning in Bioinformatics (6 papers) and RNA and protein synthesis mechanisms (6 papers). Nawar Malhis is often cited by papers focused on Protein Structure and Dynamics (8 papers), Machine Learning in Bioinformatics (6 papers) and RNA and protein synthesis mechanisms (6 papers). Nawar Malhis collaborates with scholars based in Canada, United States and South Korea. Nawar Malhis's co-authors include Jörg Gsponer, Matthew Jacobson, Steven J.M. Jones, Yaron S.N. Butterfield, Martin Ester, Jennifer M. Bui, Bi Zhao, Lukasz Kurgan, Andrzej Kloczkowski and Johannes Söding and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Nawar Malhis

15 papers receiving 514 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nawar Malhis Canada	12	446	104	69	43	26	18	521
Marcel Turcotte Canada	12	596 1.3×	38 0.4×	42 0.6×	36 0.8×	33 1.3×	33	681
Edward C. Thayer United States	8	491 1.1×	67 0.6×	56 0.8×	72 1.7×	23 0.9×	16	568
Ioannis Filippis United Kingdom	10	313 0.7×	68 0.7×	59 0.9×	44 1.0×	5 0.2×	12	381
Juan Antonio García-Martín Spain	13	340 0.8×	77 0.7×	17 0.2×	79 1.8×	9 0.3×	20	418
Ariel Erijman Israel	9	392 0.9×	35 0.3×	36 0.5×	30 0.7×	24 0.9×	11	465
Jens Hanke Germany	12	529 1.2×	105 1.0×	12 0.2×	53 1.2×	39 1.5×	17	650
C. Gautier France	9	311 0.7×	43 0.4×	29 0.4×	50 1.2×	5 0.2×	14	358
Júlia Domingo Spain	6	368 0.8×	201 1.9×	25 0.4×	16 0.4×	24 0.9×	7	461
Nathan Rollins United States	7	276 0.6×	70 0.7×	40 0.6×	10 0.2×	10 0.4×	11	345
Hans Lehrach Germany	9	280 0.6×	76 0.7×	38 0.6×	32 0.7×	6 0.2×	9	404

Countries citing papers authored by Nawar Malhis

Since Specialization

Citations

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

Fields of papers citing papers by Nawar Malhis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nawar Malhis

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

All Works

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18 of 18 papers shown

Na, Dokyun, et al.. (2025). Challenging AlphaFold in predicting proteins with large-scale allosteric transitions. Communications Chemistry. 8(1). 378–378.

Malhis, Nawar, et al.. (2024). AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions. Proceedings of the National Academy of Sciences. 121(44). e2406407121–e2406407121. 26 indexed citations

Kurgan, Lukasz, Gang Hu, Kui Wang, et al.. (2023). Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins. Nature Protocols. 18(11). 3157–3172. 21 indexed citations

Basu, Sushmita, Bi Zhao, Eshel Faraggi, et al.. (2023). DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Research. 52(D1). D426–D433. 7 indexed citations

Malhis, Nawar, Matthew Jacobson, Steven J.M. Jones, & Jörg Gsponer. (2020). LIST-S2: taxonomy based sorting of deleterious missense mutations across species. Nucleic Acids Research. 48(W1). W154–W161. 61 indexed citations

Zhao, Bi, Akila Katuwawala, Christopher J. Oldfield, et al.. (2020). DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Research. 49(D1). D298–D308. 51 indexed citations

Kuechler, Erich R., et al.. (2020). Protein–Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations. Biomolecules. 10(8). 1097–1097. 21 indexed citations

Malhis, Nawar, Steven J.M. Jones, & Jörg Gsponer. (2019). Improved measures for evolutionary conservation that exploit taxonomy distances. Nature Communications. 10(1). 1556–1556. 24 indexed citations

Sun, Xiaolin, Nawar Malhis, Bi Zhao, et al.. (2019). Computational Disorder Analysis in Ethylene Response Factors Uncovers Binding Motifs Critical to Their Diverse Functions. International Journal of Molecular Sciences. 21(1). 74–74. 8 indexed citations

10.

Malhis, Nawar, Matthew Jacobson, & Jörg Gsponer. (2016). MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences. Nucleic Acids Research. 44(W1). W488–W493. 114 indexed citations

11.

Malhis, Nawar, et al.. (2015). Computational Identification of MoRFs in Protein Sequences Using Hierarchical Application of Bayes Rule. PLoS ONE. 10(10). e0141603–e0141603. 36 indexed citations

12.

Malhis, Nawar & Jörg Gsponer. (2015). Computational identification of MoRFs in protein sequences. Bioinformatics. 31(11). 1738–1744. 64 indexed citations

13.

Rose, Ann M., Nigel J. O’Neil, Mikhail Bilenky, et al.. (2010). Genomic sequence of a mutant strain of Caenorhabditis elegans with an altered recombination pattern. BMC Genomics. 11(1). 131–131. 12 indexed citations

14.

Malhis, Nawar & Steven J.M. Jones. (2010). High quality SNP calling using Illumina data at shallow coverage. Bioinformatics. 26(8). 1029–1035. 37 indexed citations

15.

Malhis, Nawar, Yaron S.N. Butterfield, Martin Ester, & Steven J.M. Jones. (2008). Slider—maximum use of probability information for alignment of short sequence reads and SNP detection. Bioinformatics. 25(1). 6–13. 36 indexed citations

16.

Malhis, Nawar, et al.. (2006). Detecting Gene Regulation Relations from Microarray Time Series Data.. 122–127.

17.

Malhis, Nawar, et al.. (2005). An efficient method for fuzzy identification of regulatory events in gene expression time series data. 3 indexed citations

18.

Malhis, Nawar, et al.. (2005). An Efficient Approach for Candidate Set Generation. Journal of Information & Knowledge Management. 4(4). 287–291.

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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