Iya Khalil

1.0k total citations
26 papers, 731 citations indexed

About

Iya Khalil is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Iya Khalil has authored 26 papers receiving a total of 731 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 6 papers in Oncology and 6 papers in Computational Theory and Mathematics. Recurrent topics in Iya Khalil's work include Computational Drug Discovery Methods (6 papers), Bioinformatics and Genomic Networks (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Iya Khalil is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Bioinformatics and Genomic Networks (6 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). Iya Khalil collaborates with scholars based in United States, Austria and United Kingdom. Iya Khalil's co-authors include Catherine M. Hill, Neil Senzer, John Nemunaitis, Aron C. Eklund, Pat F. Fulgham, Chris Jay, A W Tong, Jing Han, Robert E. Miller and Bruce W. Church and has published in prestigious journals such as Journal of Clinical Oncology, Physical review. B, Condensed matter and Blood.

In The Last Decade

Iya Khalil

23 papers receiving 712 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Iya Khalil United States 13 464 242 92 52 50 26 731
Dirk Osterloh Germany 12 656 1.4× 181 0.7× 53 0.6× 45 0.9× 75 1.5× 21 805
Zhiyun Yu China 15 499 1.1× 180 0.7× 19 0.2× 42 0.8× 57 1.1× 22 766
Pavithra Viswanath United States 20 398 0.9× 334 1.4× 31 0.3× 12 0.2× 70 1.4× 37 859
Ruihua Fan China 16 568 1.2× 338 1.4× 32 0.3× 11 0.2× 34 0.7× 44 885
R. J. Maxwell United Kingdom 15 244 0.5× 143 0.6× 72 0.8× 10 0.2× 41 0.8× 27 747
Tzu-Hung Hsiao Taiwan 18 672 1.4× 417 1.7× 90 1.0× 25 0.5× 120 2.4× 32 994
Yuan Chun Ding United States 17 407 0.9× 122 0.5× 120 1.3× 12 0.2× 32 0.6× 42 1.2k
Tanja Tamgüney United States 7 581 1.3× 93 0.4× 16 0.2× 24 0.5× 42 0.8× 8 724
Katherine G. Finegan United Kingdom 13 456 1.0× 143 0.6× 12 0.1× 32 0.6× 46 0.9× 18 741
Rebecca M. Perrett United Kingdom 16 810 1.7× 79 0.3× 86 0.9× 19 0.4× 73 1.5× 26 1.1k

Countries citing papers authored by Iya Khalil

Since Specialization
Citations

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

Fields of papers citing papers by Iya Khalil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Iya Khalil

This figure shows the co-authorship network connecting the top 25 collaborators of Iya Khalil. A scholar is included among the top collaborators of Iya Khalil 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 Iya Khalil. Iya Khalil 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.
Mukherjee, S., et al.. (2025). Foundation models in drug discovery: Phenomenal growth today, transformative potential tomorrow?. Drug Discovery Today. 30(12). 104518–104518.
2.
3.
Berhanu, Paulos, Jonathan Bouchard, Kenneth Elder, et al.. (2020). Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes. Diabetes Therapy. 11(3). 681–699. 18 indexed citations
4.
Das, Rahul, Raymond T. Yan, Craig B. Davis, et al.. (2020). Causal modeling of TCGA, NSCLC, and HNSCC data to identify network drivers of tumor immune subtypes.. Journal of Clinical Oncology. 38(5_suppl). 68–68. 1 indexed citations
5.
6.
Das, Rahul, Leon Furchtgott, Fang‐Shu Ou, et al.. (2018). Causal modeling of CALGB/SWOG 80405 (Alliance) identifies primary (1°) side-related angiogenic drivers of metastatic colorectal cancer (mCRC). Annals of Oncology. 29. viii152–viii152. 1 indexed citations
7.
McGeachie, Michael J., Boris Hayete, Heming Xing, et al.. (2018). Systems biology and in vitro validation identifies family with sequence similarity 129 member A (FAM129A) as an asthma steroid response modulator. Journal of Allergy and Clinical Immunology. 142(5). 1479–1488.e12. 12 indexed citations
8.
Gendelman, Rina, Heming Xing, Olga K. Mirzoeva, et al.. (2017). Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells. Cancer Research. 77(7). 1575–1585. 57 indexed citations
9.
Furchtgott, Leon, Arnold Bolomsky, Fred K. Gruber, et al.. (2017). Multiple Myeloma Drivers of High Risk and Response to Stem Cell Transplantation Identified By Causal Machine Learning: Out-of-Cohort and Experimental Validation. Blood. 130. 3029–3029. 2 indexed citations
10.
Furchtgott, Leon, David Swanson, Boris Hayete, et al.. (2017). Statistical modeling of CALGB 80405 (Alliance) to identify influential factors in metastatic colorectal cancer (CRC) dependent on primary (1o) tumor side.. Journal of Clinical Oncology. 35(15_suppl). 3528–3528. 1 indexed citations
11.
Latourelle, Jeanne C., Michael T. Beste, Tiffany C. Hadzi, et al.. (2017). Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation. The Lancet Neurology. 16(11). 908–916. 106 indexed citations
12.
Ivanov, Vladimir N., Eric Tchetgen Tchetgen, Bruce W. Church, et al.. (2016). Using Clinical Trial and Real World Data to Bridge Efficacy to Effectiveness of Fingolimod in Multiple Sclerosis Patients. Value in Health. 19(7). A426–A426. 1 indexed citations
13.
Gruber, Fred K., Jonathan J. Keats, Kyle McBride, et al.. (2016). Bayesian Network Models of Multiple Myeloma: Drivers of High Risk and Durable Response. Blood. 128(22). 4406–4406. 5 indexed citations
14.
Xing, Heming, Paul McDonagh, Jadwiga Biénkowska, et al.. (2011). Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis. PLoS Computational Biology. 7(3). e1001105–e1001105. 32 indexed citations
15.
Tong, A W, Pat F. Fulgham, Chris Jay, et al.. (2008). MicroRNA profile analysis of human prostate cancers. Cancer Gene Therapy. 16(3). 206–216. 221 indexed citations
16.
Yoshioka, Naohisa, Robert E. Miller, Rina Gendelman, et al.. (2007). A systems biology dynamical model of mammalian G 1 cell cycle progression. Molecular Systems Biology. 3(1). 84–84. 50 indexed citations
17.
Nemunaitis, John, Neil Senzer, Iya Khalil, et al.. (2007). Proof concept for clinical justification of network mapping for personalized cancer therapeutics. Cancer Gene Therapy. 14(8). 686–695. 13 indexed citations
18.
Aksenov, Sergey, et al.. (2005). An integrated approach for inference and mechanistic modeling for advancing drug development. FEBS Letters. 579(8). 1878–1883. 25 indexed citations
19.
Christopher, Rita, Jeffrey J. Fox, Rina Gendelman, et al.. (2004). Data‐Driven Computer Simulation of Human Cancer Cell. Annals of the New York Academy of Sciences. 1020(1). 132–153. 58 indexed citations
20.
Khalil, Iya & Catherine M. Hill. (2004). Systems biology for cancer. Current Opinion in Oncology. 17(1). 44–48. 64 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Dirk Osterloh Breakdown of academic impact, for papers by Zhiyun Yu Breakdown of academic impact, for papers by Pavithra Viswanath Breakdown of academic impact, for papers by Ruihua Fan Breakdown of academic impact, for papers by R. J. Maxwell Breakdown of academic impact, for papers by Tzu-Hung Hsiao Breakdown of academic impact, for papers by Yuan Chun Ding Breakdown of academic impact, for papers by Tanja Tamgüney Breakdown of academic impact, for papers by Katherine G. Finegan Breakdown of academic impact, for papers by Rebecca M. Perrett
Rankless by CCL
2026