Farrokh Mehryary

9.6k total citations · 2 hit papers
15 papers, 3.8k citations indexed

About

Farrokh Mehryary is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Farrokh Mehryary has authored 15 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 8 papers in Artificial Intelligence and 3 papers in Computational Theory and Mathematics. Recurrent topics in Farrokh Mehryary's work include Biomedical Text Mining and Ontologies (9 papers), Bioinformatics and Genomic Networks (7 papers) and Topic Modeling (6 papers). Farrokh Mehryary is often cited by papers focused on Biomedical Text Mining and Ontologies (9 papers), Bioinformatics and Genomic Networks (7 papers) and Topic Modeling (6 papers). Farrokh Mehryary collaborates with scholars based in Finland, Denmark and United Kingdom. Farrokh Mehryary's co-authors include Sampo Pyysalo, Lars Juhl Jensen, Katerina Nastou, Nadezhda T. Doncheva, Christian von Mering, Mikaela Koutrouli, Tao Fang, Peer Bork, Rebecca Kirsch and Damian Szklarczyk and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Journal of the American Medical Informatics Association.

In The Last Decade

Farrokh Mehryary

12 papers receiving 3.8k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

2022 3.6k citations Damian Szklarczyk, Rebecca Kirsch et al. Nucleic Acids Research profile →
The STRING database in 2025: protein networks with directionality of regulation

2024 93 citations Damian Szklarczyk, Katerina Nastou et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Farrokh Mehryary Finland	8	2.1k	460	370	347	342	15	3.8k
Mikaela Koutrouli Denmark	5	2.2k 1.0×	463 1.0×	373 1.0×	355 1.0×	343 1.0×	12	3.9k
Yaron Guan‐Golan Israel	4	2.1k 1.0×	502 1.1×	340 0.9×	465 1.3×	329 1.0×	6	3.7k
Yaron Mazor Israel	7	2.1k 1.0×	488 1.1×	347 0.9×	475 1.4×	337 1.0×	7	3.7k
Hatem Zayed Qatar	32	2.0k 0.9×	383 0.8×	265 0.7×	577 1.7×	270 0.8×	175	3.3k
Sergey Kaplan Israel	2	1.9k 0.9×	451 1.0×	330 0.9×	428 1.2×	316 0.9×	3	3.4k
Xiaoli Jiao United States	10	2.5k 1.2×	748 1.6×	546 1.5×	489 1.4×	400 1.2×	21	4.3k
Shahar Zimmerman Israel	4	2.0k 0.9×	514 1.1×	344 0.9×	442 1.3×	354 1.0×	4	3.6k
Simon Fishilevich Israel	10	2.5k 1.2×	622 1.4×	425 1.1×	625 1.8×	382 1.1×	12	4.5k
Yunping Zhu China	28	2.5k 1.2×	464 1.0×	402 1.1×	235 0.7×	250 0.7×	112	4.1k
Asher Kohn Israel	4	2.5k 1.2×	612 1.3×	421 1.1×	614 1.8×	368 1.1×	5	4.3k

Countries citing papers authored by Farrokh Mehryary

Since Specialization

Citations

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

Fields of papers citing papers by Farrokh Mehryary

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farrokh Mehryary

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

All Works

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15 of 15 papers shown

Mehryary, Farrokh, et al.. (2025). Question Answering models for information extraction from perovskite materials science literature. Communications Materials. 6(1).

Mehryary, Farrokh, et al.. (2025). Annotated textual dataset PV600 of perovskite bandgaps for information extraction from literature. Scientific Data. 12(1). 1401–1401. 1 indexed citations

Mehryary, Farrokh, Katerina Nastou, Tomoko Ohta, Lars Juhl Jensen, & Sampo Pyysalo. (2024). STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature. Bioinformatics. 40(9). 7 indexed citations

Nastou, Katerina, et al.. (2024). RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature. Database. 2024. 2 indexed citations

Szklarczyk, Damian, Katerina Nastou, Mikaela Koutrouli, et al.. (2024). The STRING database in 2025: protein networks with directionality of regulation. Nucleic Acids Research. 53(D1). D730–D737. 93 indexed citations breakdown →

Mehryary, Farrokh, et al.. (2023). Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations. Database. 2023. 10 indexed citations

Szklarczyk, Damian, Rebecca Kirsch, Mikaela Koutrouli, et al.. (2022). The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research. 51(D1). D638–D646. 3589 indexed citations breakdown →

Mehryary, Farrokh, Hans Moen, Tapio Salakoski, & Filip Ginter. (2020). Entity-Pair Embeddings for Improving Relation Extraction in the Biomedical Domain.. The European Symposium on Artificial Neural Networks. 613–618. 1 indexed citations

Hakala, Kai, Suwisa Kaewphan, Jari Björne, et al.. (2020). Neural Network and Random Forest Models in Protein Function Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(3). 1772–1781. 17 indexed citations

10.

Sarker, Abeed, Maksim Belousov, Kai Hakala, et al.. (2018). Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task. Journal of the American Medical Informatics Association. 25(10). 1274–1283. 61 indexed citations

11.

Mehryary, Farrokh, Jari Björne, Tapio Salakoski, & Filip Ginter. (2018). Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction. Database. 2018. 11 indexed citations

12.

Mehryary, Farrokh, Kai Hakala, Suwisa Kaewphan, et al.. (2017). End-to-End System for Bacteria Habitat Extraction. 80–90. 10 indexed citations

13.

Moen, Hans, Kai Hakala, Farrokh Mehryary, et al.. (2017). Detecting mentions of pain and acute confusion in Finnish clinical text. 365–372.

14.

Mehryary, Farrokh, Jari Björne, Sampo Pyysalo, Tapio Salakoski, & Filip Ginter. (2016). Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016. 25 indexed citations

15.

Mehryary, Farrokh, Suwisa Kaewphan, Kai Hakala, & Filip Ginter. (2016). Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification. Journal of Biomedical Semantics. 7(1). 27–27.

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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