Heval Ataş

965 total citations · 1 hit paper
10 papers, 556 citations indexed

About

Heval Ataş is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Heval Ataş has authored 10 papers receiving a total of 556 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 2 papers in Infectious Diseases. Recurrent topics in Heval Ataş's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Bioinformatics (4 papers) and Protein Structure and Dynamics (4 papers). Heval Ataş is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Bioinformatics (4 papers) and Protein Structure and Dynamics (4 papers). Heval Ataş collaborates with scholars based in Türkiye, United Kingdom and United States. Heval Ataş's co-authors include Tunca Doğan, María Martin, Rengül Çetin-Atalay, Volkan Atalay, Ahmet Süreyya Rifaioğlu, Kemal Turhan, Aybar C. Acar, A. Biber, Vishal Joshi and Andrew Nightingale and has published in prestigious journals such as Nucleic Acids Research, Blood and PLoS Computational Biology.

In The Last Decade

Heval Ataş

10 papers receiving 547 citations

Hit Papers

align trajectories

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.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

2018 363 citations Ahmet Süreyya Rifaioğlu, Heval Ataş et al. Briefings in Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Heval Ataş Türkiye	6	380	317	106	33	30	10	556
Tevfik Kizilören United Kingdom	3	264 0.7×	267 0.8×	81 0.8×	37 1.1×	29 1.0×	5	490
Yuemin Bian United States	11	298 0.8×	280 0.9×	106 1.0×	59 1.8×	28 0.9×	19	535
Sybilla Corbett United Kingdom	3	271 0.7×	261 0.8×	82 0.8×	36 1.1×	16 0.5×	5	486
Thamani Dahoun Switzerland	6	323 0.8×	256 0.8×	76 0.7×	17 0.5×	29 1.0×	6	565
Kristina Preuer Austria	2	258 0.7×	274 0.9×	74 0.7×	29 0.9×	39 1.3×	2	384
Hanbin Shan China	2	209 0.6×	234 0.7×	73 0.7×	16 0.5×	28 0.9×	3	426
Ahmet Süreyya Rifaioğlu Türkiye	8	672 1.8×	529 1.7×	174 1.6×	55 1.7×	39 1.3×	15	896
Emma J. Manners United Kingdom	4	305 0.8×	302 1.0×	84 0.8×	47 1.4×	16 0.5×	7	583
Shuting Jin China	14	507 1.3×	357 1.1×	139 1.3×	23 0.7×	88 2.9×	31	739
José Liñares-Blanco Spain	6	214 0.6×	199 0.6×	46 0.4×	24 0.7×	36 1.2×	10	419

Countries citing papers authored by Heval Ataş

Since Specialization

Citations

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

Fields of papers citing papers by Heval Ataş

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heval Ataş

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Ataş, Heval & Tunca Doğan. (2023). How to approach machine learning-based prediction of drug/compound–target interactions. Journal of Cheminformatics. 15(1). 16–16. 23 indexed citations

2.

Ataş, Heval, et al.. (2022). Learning functional properties of proteins with language models. Nature Machine Intelligence. 4(3). 227–245. 106 indexed citations

3.

Doğan, Tunca, Heval Ataş, Vishal Joshi, et al.. (2021). CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations. Nucleic Acids Research. 49(16). e96–e96. 26 indexed citations

4.

Çetin-Atalay, Rengül, Deniz Kahraman, Ahmet Süreyya Rifaioğlu, et al.. (2021). Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools. Journal of Gastrointestinal Cancer. 52(4). 1266–1276. 2 indexed citations

5.

Doğan, Tunca, Marcus Baumann, Heval Ataş, et al.. (2021). Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases. PLoS Computational Biology. 17(11). e1009171–e1009171. 14 indexed citations

6.

Ataş, Heval, et al.. (2020). Learning Functional Properties of Proteins with Language Models. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

7.

Ataş, Heval, Nurcan Tunçbağ, & Tunca Doğan. (2018). Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites. Methods in molecular biology. 1762. 51–69. 5 indexed citations

8.

Rifaioğlu, Ahmet Süreyya, Heval Ataş, María Martin, et al.. (2018). Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Briefings in Bioinformatics. 20(5). 1878–1912. 363 indexed citations breakdown →

9.

Ataş, Heval, et al.. (2017). Anti-cancer effect of clofazimine as a single agent and in combination with cisplatin on U266 multiple myeloma cell line. Leukemia Research. 55. 33–40. 14 indexed citations

10.

Ataş, Heval, et al.. (2016). Anti-Cancer Effects of Clofazimine As a Single Agent and in Combination with Cisplatin in Multiple Myeloma. Blood. 128(22). 5897–5897. 2 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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