Michal Twik

8.5k total citations · 3 hit papers
8 papers, 4.8k citations indexed

About

Michal Twik is a scholar working on Molecular Biology, Genetics and Sensory Systems. According to data from OpenAlex, Michal Twik has authored 8 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Genetics and 1 paper in Sensory Systems. Recurrent topics in Michal Twik's work include Bioinformatics and Genomic Networks (5 papers), Biomedical Text Mining and Ontologies (5 papers) and Genomics and Rare Diseases (3 papers). Michal Twik is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), Biomedical Text Mining and Ontologies (5 papers) and Genomics and Rare Diseases (3 papers). Michal Twik collaborates with scholars based in Israel, United States and China. Michal Twik's co-authors include Doron Lancet, Marilyn Safran, Tsippi Iny Stein, Noa Rappaport, Ron Nudel, Inbar Plaschkes, Simon Fishilevich, Naomi Rosen, Asher Kohn and Gil Stelzer and has published in prestigious journals such as Nucleic Acids Research, BioMedical Engineering OnLine and Database.

In The Last Decade

Michal Twik

8 papers receiving 4.8k citations

Hit Papers

The GeneCards Suite: From Gene Data Mining to Disease Gen... 2016 2026 2019 2022 2016 2017 2016 1000 2.0k 3.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michal Twik Israel 8 2.8k 706 682 561 460 8 4.8k
Inbar Plaschkes Israel 17 3.1k 1.1× 705 1.0× 670 1.0× 550 1.0× 422 0.9× 30 5.2k
Simon Fishilevich Israel 10 2.5k 0.9× 625 0.9× 622 0.9× 537 1.0× 373 0.8× 12 4.5k
Noa Rappaport United States 20 3.4k 1.2× 809 1.1× 743 1.1× 572 1.0× 468 1.0× 34 5.8k
Gil Stelzer Israel 15 2.6k 0.9× 575 0.8× 620 0.9× 517 0.9× 414 0.9× 23 4.5k
Asher Kohn Israel 4 2.5k 0.9× 614 0.9× 612 0.9× 518 0.9× 364 0.8× 5 4.3k
Ron Nudel Denmark 16 2.7k 1.0× 912 1.3× 647 0.9× 549 1.0× 419 0.9× 37 5.1k
Dvir Dahary Israel 15 2.9k 1.0× 657 0.9× 744 1.1× 473 0.8× 333 0.7× 21 4.7k
Yaron Guan‐Golan Israel 4 2.1k 0.7× 465 0.7× 502 0.7× 489 0.9× 369 0.8× 6 3.7k
Tieliu Shi China 32 3.4k 1.2× 594 0.8× 718 1.1× 389 0.7× 356 0.8× 150 4.9k
Yaron Mazor Israel 7 2.1k 0.7× 475 0.7× 488 0.7× 478 0.9× 340 0.7× 7 3.7k

Countries citing papers authored by Michal Twik

Since Specialization
Citations

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

Fields of papers citing papers by Michal Twik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michal Twik

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

All Works

8 of 8 papers shown
1.
Dahary, Dvir, Yaron Mazor, Ruth Barshir, et al.. (2019). Genome analysis and knowledge-driven variant interpretation with TGex. BMC Medical Genomics. 12(1). 200–200. 33 indexed citations
2.
Rappaport, Noa, Simon Fishilevich, Ron Nudel, et al.. (2017). Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect. BioMedical Engineering OnLine. 16(S1). 72–72. 68 indexed citations
3.
Fishilevich, Simon, Ron Nudel, Noa Rappaport, et al.. (2017). GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017. 746 indexed citations breakdown →
4.
Stelzer, Gil, Naomi Rosen, Inbar Plaschkes, et al.. (2016). The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current Protocols in Bioinformatics. 54(1). 1.30.1–1.30.33. 3231 indexed citations breakdown →
5.
Marenco, Luis, Rixin Wang, Robert A. McDougal, et al.. (2016). ORDB, HORDE, ODORactor and other on-line knowledge resources of olfactory receptor-odorant interactions. Database. 2016. baw132–baw132. 15 indexed citations
6.
Rappaport, Noa, Michal Twik, Inbar Plaschkes, et al.. (2016). MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Research. 45(D1). D877–D887. 432 indexed citations breakdown →
7.
Rappaport, Noa, Michal Twik, Noam Nativ, et al.. (2014). MalaCards: A Comprehensive Automatically‐Mined Database of Human Diseases. Current Protocols in Bioinformatics. 47(1). 1.24.1–19. 75 indexed citations
8.
Rappaport, Noa, Noam Nativ, Gil Stelzer, et al.. (2013). MalaCards: an integrated compendium for diseases and their annotation. Database. 2013. bat018–bat018. 214 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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