Ehsaneddin Asgari

2.2k total citations · 1 hit paper
25 papers, 917 citations indexed

About

Ehsaneddin Asgari is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ehsaneddin Asgari has authored 25 papers receiving a total of 917 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Molecular Biology and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ehsaneddin Asgari's work include Topic Modeling (11 papers), Natural Language Processing Techniques (6 papers) and Machine Learning in Bioinformatics (5 papers). Ehsaneddin Asgari is often cited by papers focused on Topic Modeling (11 papers), Natural Language Processing Techniques (6 papers) and Machine Learning in Bioinformatics (5 papers). Ehsaneddin Asgari collaborates with scholars based in United States, Germany and Iran. Ehsaneddin Asgari's co-authors include Mohammad R. K. Mofrad, Alice C. McHardy, Gabriel Cabot, Petra Gastmeier, Daniel E Jonas, Andreas Bremges, Antonio Oliver, Susanne Häußler, Monika Schniederjans and Michael Hogardt and has published in prestigious journals such as Bioinformatics, PLoS ONE and Scientific Reports.

In The Last Decade

Ehsaneddin Asgari

24 papers receiving 894 citations

Hit Papers

Continuous Distributed Representation of Biological Seque... 2015 2026 2018 2022 2015 100 200 300 400 500
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.

  1. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics
    2015 543 citations Ehsaneddin Asgari, Mohammad R. K. Mofrad PLoS ONE profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ehsaneddin Asgari United States 10 715 164 105 69 53 25 917
Jiawei Wang China 18 868 1.2× 67 0.4× 30 0.3× 114 1.7× 13 0.2× 42 1.2k
Vladimir Gligorijević United States 14 937 1.3× 246 1.5× 111 1.1× 11 0.2× 74 1.4× 24 1.3k
Andrew P. Norgan United States 14 161 0.2× 47 0.3× 61 0.6× 41 0.6× 15 0.3× 53 597
Fangping Wan United States 13 847 1.2× 523 3.2× 87 0.8× 25 0.4× 162 3.1× 18 1.1k
Julia Koehler Leman United States 16 869 1.2× 148 0.9× 32 0.3× 9 0.1× 168 3.2× 25 1.1k
Otto Ritter Germany 5 550 0.8× 106 0.6× 55 0.5× 17 0.2× 88 1.7× 7 747
Soma Barman India 16 412 0.6× 109 0.7× 22 0.2× 15 0.2× 28 0.5× 57 771
William Hayes United States 10 399 0.6× 30 0.2× 40 0.4× 67 1.0× 29 0.5× 22 776
Jeyakumar Natarajan India 15 569 0.8× 85 0.5× 185 1.8× 29 0.4× 20 0.4× 82 837
Hak Suk Chung South Korea 18 568 0.8× 117 0.7× 11 0.1× 140 2.0× 49 0.9× 52 1.1k

Countries citing papers authored by Ehsaneddin Asgari

Since Specialization
Citations

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

Fields of papers citing papers by Ehsaneddin Asgari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ehsaneddin Asgari

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

All Works

20 of 20 papers shown
1.
Asgari, Ehsaneddin, et al.. (2024). AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations. 1704–1710. 1 indexed citations
2.
Meyer, Fernando, et al.. (2024). Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes. Briefings in Bioinformatics. 25(3). 10 indexed citations
3.
Asgari, Ehsaneddin, et al.. (2023). GO Bench: shared hub for universal benchmarking of machine learning-based protein functional annotations. Bioinformatics. 39(2). 1 indexed citations
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Asgari, Ehsaneddin, et al.. (2023). SUT at SemEval-2023 Task 1: Prompt Generation for Visual Word Sense Disambiguation. 2160–2163. 1 indexed citations
8.
Hotop, Sven‐Kevin, Ehsaneddin Asgari, Ulrike Beutling, et al.. (2022). Peptide microarrays coupled to machine learning reveal individual epitopes from human antibody responses with neutralizing capabilities against SARS-CoV-2. Emerging Microbes & Infections. 11(1). 1037–1048. 15 indexed citations
9.
Asgari, Ehsaneddin, et al.. (2021). EpitopeVec: linear epitope prediction using deep protein sequence embeddings. Bioinformatics. 37(23). 4517–4525. 26 indexed citations
10.
Khaledi, Ariane, Aaron Weimann, Monika Schniederjans, et al.. (2020). Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics. EMBO Molecular Medicine. 12(3). e10264–e10264. 127 indexed citations
11.
Asgari, Ehsaneddin, et al.. (2020). EmbLexChange at SemEval-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes. 201–207. 3 indexed citations
12.
Asgari, Ehsaneddin, Alice C. McHardy, & Mohammad R. K. Mofrad. (2019). Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX). Scientific Reports. 9(1). 3577–3577. 49 indexed citations
13.
Asgari, Ehsaneddin. (2019). Life Language Processing: Deep Learning-based Language-agnostic Processing of Proteomics, Genomics/Metagenomics, and Human Languages. eScholarship (California Digital Library). 3 indexed citations
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Asgari, Ehsaneddin, Philipp C. Münch, Till Robin Lesker, Alice C. McHardy, & Mohammad R. K. Mofrad. (2018). DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection. Bioinformatics. 35(14). 2498–2500. 10 indexed citations
16.
Jahed, Zeinab, Darya Fadavi, Uyen T. Vu, et al.. (2018). Molecular Insights into the Mechanisms of SUN1 Oligomerization in the Nuclear Envelope. Biophysical Journal. 114(5). 1190–1203. 31 indexed citations
17.
Asgari, Ehsaneddin, et al.. (2017). Measuring Countriess Human Rights Positions in UN Universal Periodic Review. SSRN Electronic Journal. 1 indexed citations
18.
Asgari, Ehsaneddin & Mohammad R. K. Mofrad. (2015). Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics. PLoS ONE. 10(11). e0141287–e0141287. 543 indexed citations breakdown →
19.
Asgari, Ehsaneddin & Jean-Cédric Chappelier. (2013). Linguistic Resources and Topic Models for the Analysis of Persian Poems. North American Chapter of the Association for Computational Linguistics. 23–31. 8 indexed citations
20.
Neshati, Mahmood, Djoerd Hiemstra, Ehsaneddin Asgari, & Hamid Beigy. (2013). Integration of scientific and social networks. World Wide Web. 17(5). 1051–1079. 14 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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