Etai Jacob

964 total citations
19 papers, 586 citations indexed

About

Etai Jacob is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, Etai Jacob has authored 19 papers receiving a total of 586 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Pulmonary and Respiratory Medicine and 5 papers in Cancer Research. Recurrent topics in Etai Jacob's work include Protein Structure and Dynamics (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Ferroptosis and cancer prognosis (3 papers). Etai Jacob is often cited by papers focused on Protein Structure and Dynamics (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Ferroptosis and cancer prognosis (3 papers). Etai Jacob collaborates with scholars based in United States, United Kingdom and Israel. Etai Jacob's co-authors include Ron Unger, Cheng‐Zhong Zhang, Ji Woo Park, Sadeem Ahmad, Emily Greenwald, Xin Mu, Sun Hur, Fei Yang, Amnon Horovitz and David Pellman and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Etai Jacob

19 papers receiving 577 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Etai Jacob United States 10 450 182 68 62 42 19 586
Frank Schumann Germany 9 396 0.9× 99 0.5× 22 0.3× 38 0.6× 21 0.5× 11 613
Colin W. Garvie United States 15 785 1.7× 97 0.5× 66 1.0× 187 3.0× 14 0.3× 22 953
Zhenyun Yang United States 13 558 1.2× 238 1.3× 45 0.7× 112 1.8× 61 1.5× 33 777
Annie Bouchard Canada 16 750 1.7× 52 0.3× 43 0.6× 66 1.1× 56 1.3× 22 914
Magdalena B. Rother Netherlands 15 583 1.3× 93 0.5× 48 0.7× 104 1.7× 10 0.2× 22 724
Diane Forget Canada 18 1.1k 2.5× 69 0.4× 71 1.0× 61 1.0× 18 0.4× 28 1.2k
Javier A. Alfaro Poland 14 441 1.0× 100 0.5× 57 0.8× 87 1.4× 10 0.2× 39 662
Mikhail I. Dobrikov United States 16 503 1.1× 69 0.4× 42 0.6× 101 1.6× 65 1.5× 30 693
Julio E. Celis Denmark 9 466 1.0× 108 0.6× 61 0.9× 56 0.9× 44 1.0× 9 585
P. Schutz Sweden 7 562 1.2× 71 0.4× 27 0.4× 96 1.5× 22 0.5× 7 640

Countries citing papers authored by Etai Jacob

Since Specialization
Citations

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

Fields of papers citing papers by Etai Jacob

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Etai Jacob

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

All Works

19 of 19 papers shown
1.
Arango-Argoty, Gustavo, Damián E. Bikiel, Gerald J. Sun, et al.. (2025). AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes. Cancer Cell. 43(5). 875–890.e8. 9 indexed citations
2.
Arango-Argoty, Gustavo, et al.. (2025). Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers. Nature Communications. 16(1). 2101–2101. 7 indexed citations
3.
Arango-Argoty, Gustavo, Marzieh Haghighi, Gerald J. Sun, et al.. (2025). An artificial intelligence-based model for prediction of clonal hematopoiesis variants in cell-free DNA samples. npj Precision Oncology. 9(1). 147–147. 1 indexed citations
4.
Νικολάου, Νικόλαος, et al.. (2025). A machine learning approach for multimodal data fusion for survival prediction in cancer patients. npj Precision Oncology. 9(1). 128–128. 3 indexed citations
5.
Fang, Chao, et al.. (2024). Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer. Journal of Translational Medicine. 22(1). 726–726. 6 indexed citations
6.
Sun, Gerald J., Gustavo Arango-Argoty, Gary J. Doherty, et al.. (2024). Machine learning modeling of patient health signals informs long-term survival on immune checkpoint inhibitor therapy. iScience. 27(9). 110634–110634. 2 indexed citations
7.
Chaunzwa, Tafadzwa L., Jack M. Qian, Qin Li, et al.. (2024). Body Composition in Advanced Non-Small Cell Lung Cancer Treated With Immunotherapy. JAMA Oncology. 10(6). 773–773. 11 indexed citations
8.
Papathanasiou, Stamatis, Shiwei Liu, Gregory J. Brunette, et al.. (2023). Heritable transcriptional defects from aberrations of nuclear architecture. Nature. 619(7968). 184–192. 42 indexed citations
9.
Jacob, Etai, et al.. (2023). Autoencoder-based multimodal prediction of non-small cell lung cancer survival. Scientific Reports. 13(1). 15761–15761. 11 indexed citations
10.
Arango-Argoty, Gustavo & Etai Jacob. (2023). Abstract 1174: Enhancing the utilization of deep learning to predict patient response in small immunotherapy cohorts using real-world data. Cancer Research. 83(7_Supplement). 1174–1174. 1 indexed citations
11.
Jacob, Etai, et al.. (2022). Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Omics Data. 2022 International Joint Conference on Neural Networks (IJCNN). 1–10. 1 indexed citations
12.
Lee, I‐Ju, et al.. (2020). Factors promoting nuclear envelope assembly independent of the canonical ESCRT pathway. The Journal of Cell Biology. 219(6). 27 indexed citations
13.
Golan, Tamar, Roma Parikh, Etai Jacob, et al.. (2019). Adipocytes sensitize melanoma cells to environmental TGF-β cues by repressing the expression of miR-211. Science Signaling. 12(591). 23 indexed citations
14.
Ahmad, Sadeem, Xin Mu, Fei Yang, et al.. (2018). Breaching Self-Tolerance to Alu Duplex RNA Underlies MDA5-Mediated Inflammation. Cell. 172(4). 797–810.e13. 287 indexed citations
15.
Jacob, Etai, et al.. (2016). Cross-linking reveals laminin coiled-coil architecture. Proceedings of the National Academy of Sciences. 113(47). 13384–13389. 20 indexed citations
16.
Jacob, Etai, Ron Unger, & Amnon Horovitz. (2015). Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis. eLife. 4. e08932–e08932. 7 indexed citations
17.
Jacob, Etai, Ron Unger, & Amnon Horovitz. (2013). N-Terminal Domains in Two-Domain Proteins Are Biased to Be Shorter and Predicted to Fold Faster Than Their C-Terminal Counterparts. Cell Reports. 3(4). 1051–1056. 9 indexed citations
18.
Jacob, Etai, Amnon Horovitz, & Ron Unger. (2007). Different mechanistic requirements for prokaryotic and eukaryotic chaperonins: a lattice study. Bioinformatics. 23(13). i240–i248. 22 indexed citations
19.
Jacob, Etai & Ron Unger. (2007). A tale of two tails: why are terminal residues of proteins exposed?. Bioinformatics. 23(2). e225–e230. 97 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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