Anil Korkut

44.8k total citations · 1 hit paper
28 papers, 1.1k citations indexed

About

Anil Korkut is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Anil Korkut has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Oncology and 7 papers in Cancer Research. Recurrent topics in Anil Korkut's work include Bioinformatics and Genomic Networks (8 papers), Computational Drug Discovery Methods (6 papers) and Gene Regulatory Network Analysis (3 papers). Anil Korkut is often cited by papers focused on Bioinformatics and Genomic Networks (8 papers), Computational Drug Discovery Methods (6 papers) and Gene Regulatory Network Analysis (3 papers). Anil Korkut collaborates with scholars based in United States, Türkiye and Sweden. Anil Korkut's co-authors include Chris Sander, Özgün Babur, Rehan Akbani, Thierry Soussi, Teng‐Kuei Hsu, Olivier Lichtarge, Maria Cardenas, Xubin Li, John N. Weinstein and Yuexin Liu and has published in prestigious journals such as PLoS ONE, Hepatology and Cancer Research.

In The Last Decade

Anil Korkut

26 papers receiving 1.1k 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.

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas

2019 421 citations Lawrence A. Donehower, Thierry Soussi et al. Cell Reports profile →

Peers

Anil Korkut

MB

ONCOL

CR

PRM

CTM

Alex J. Eustace Ireland

Darren R. Tyson United States

Aphrothiti J. Hanrahan United States

Sriganesh Srihari Australia

Bertram Klinger Germany

Jonathan R. Dry United States

Francisco Martínez-Jiménez Spain

Jiannong Li United States

Sam Thompson United Kingdom

Deena M.A. Gendoo Canada

Alex J. Eustace Ireland

Anil Korkut

688 ×1.0

MB

429 ×1.2

ONCOL

347 ×1.1

CR

163 ×0.9

PRM

39 ×0.3

CTM

Citations per year, relative to Anil Korkut Anil Korkut (= 1×) peers Alex J. Eustace

Countries citing papers authored by Anil Korkut

Since Specialization

Citations

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

Fields of papers citing papers by Anil Korkut

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anil Korkut

This figure shows the co-authorship network connecting the top 25 collaborators of Anil Korkut. A scholar is included among the top collaborators of Anil Korkut 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 Anil Korkut. Anil Korkut 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:

20 of 20 papers shown

1.

Babur, Özgün, et al.. (2026). A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches. Communications Biology. 9(1). 18–18.

2.

Dibra, Denada, Shunbin Xiong, Sydney M. Moyer, et al.. (2024). Mutant p53 protects triple-negative breast adenocarcinomas from ferroptosis in vivo. Science Advances. 10(7). eadk1835–eadk1835. 22 indexed citations

3.

DiPeri, Timothy P., Kurt W. Evans, Ming Zhao, et al.. (2024). Co-clinical Trial of Novel Bispecific Anti-HER2 Antibody Zanidatamab in Patient-Derived Xenografts. Cancer Discovery. 14(5). 828–845. 20 indexed citations

4.

Meric‐Bernstam, Funda, Jeffrey S. Ross, Karthikeyan Murugesan, et al.. (2024). KRAS Allelic Variants in Biliary Tract Cancers. JAMA Network Open. 7(5). e249840–e249840. 8 indexed citations

5.

Makawita, Shalini, Sunyoung Lee, Lawrence N. Kwong, et al.. (2024). Comprehensive Immunogenomic Profiling of IDH1-/2-Altered Cholangiocarcinoma. JCO Precision Oncology. 8(8). e2300544–e2300544. 8 indexed citations

6.

Luna, Augustin, et al.. (2023). Mapping the functional interactions at the tumor-immune checkpoint interface. Communications Biology. 6(1). 462–462. 11 indexed citations

7.

Li, Xubin, Gonghong Yan, Jacob H. Elnaggar, et al.. (2022). Precision Combination Therapies Based on Recurrent Oncogenic Coalterations. Cancer Discovery. 12(6). 1542–1559. 20 indexed citations

8.

Akçakanat, Argun, Xiaofeng Zheng, Tae‐Beom Kim, et al.. (2021). Genomic, Transcriptomic, and Proteomic Profiling of Metastatic Breast Cancer. Clinical Cancer Research. 27(11). 3243–3252. 17 indexed citations

9.

Babur, Özgün, Augustin Luna, Anil Korkut, et al.. (2021). Causal interactions from proteomic profiles: Molecular data meet pathway knowledge. Patterns. 2(6). 100257–100257. 41 indexed citations

10.

Luna, Augustin, Metin Can Siper, Anil Korkut, et al.. (2021). Analyzing causal relationships in proteomic profiles using CausalPath. STAR Protocols. 2(4). 100955–100955. 6 indexed citations

11.

Zhao, Ming, Kurt W. Evans, Erkan Yuca, et al.. (2021). Combining Neratinib with CDK4/6, mTOR, and MEK Inhibitors in Models of HER2-positive Cancer. Clinical Cancer Research. 27(6). 1681–1694. 44 indexed citations

12.

Nyman, Elin, Richard R. Stein, Xiaohong Jing, et al.. (2020). Perturbation biology links temporal protein changes to drug responses in a melanoma cell line. PLoS Computational Biology. 16(7). e1007909–e1007909. 13 indexed citations

13.

Yuan, Bo, Ciyue Shen, Augustin Luna, et al.. (2020). CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy. Cell Systems. 12(2). 128–140.e4. 69 indexed citations

14.

Babur, Özgün, Augustin Luna, Anil Korkut, et al.. (2020). Causal Interactions from Proteomic Profiles: Molecular Data Meets Pathway Knowledge. SSRN Electronic Journal.

15.

Donehower, Lawrence A., Thierry Soussi, Anil Korkut, et al.. (2019). Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas. Cell Reports. 28(5). 1370–1384.e5. 421 indexed citations breakdown →

16.

Berger, Ashton C., Anil Korkut, Rupa S. Kanchi, et al.. (2018). Abstract 3303: A comprehensive TCGA Pan-Cancer molecular study of gynecologic and breast cancers. Cancer Research. 78(13_Supplement). 3303–3303. 2 indexed citations

17.

Korkut, Anil, et al.. (2015). BP_Prior v2.9.1 (a.k.a. PERA). Figshare. 1 indexed citations

18.

Miller, Martin L., Ed Reznik, Nicholas P. Gauthier, et al.. (2015). Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Systems. 1(3). 197–209. 86 indexed citations

19.

Kaushik, Poorvi, Evan Molinelli, Martin L. Miller, et al.. (2014). Spatial Normalization of Reverse Phase Protein Array Data. PLoS ONE. 9(12). e97213–e97213. 16 indexed citations

20.

Molinelli, Evan, Anil Korkut, Weiqing Wang, et al.. (2013). Perturbation Biology: Inferring Signaling Networks in Cellular Systems. PLoS Computational Biology. 9(12). e1003290–e1003290. 120 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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