Michal Chorev

666 total citations
11 papers, 439 citations indexed

About

Michal Chorev is a scholar working on Molecular Biology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Michal Chorev has authored 11 papers receiving a total of 439 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Artificial Intelligence and 3 papers in Cancer Research. Recurrent topics in Michal Chorev's work include RNA Research and Splicing (6 papers), RNA and protein synthesis mechanisms (5 papers) and Cancer-related molecular mechanisms research (3 papers). Michal Chorev is often cited by papers focused on RNA Research and Splicing (6 papers), RNA and protein synthesis mechanisms (5 papers) and Cancer-related molecular mechanisms research (3 papers). Michal Chorev collaborates with scholars based in Israel, Australia and United States. Michal Chorev's co-authors include Liran Carmel, Ella Barkan, Adam Spiro, Varda Shalev, Michal Guindy, Michal Rosen‐Zvi, Yaara Goldschmidt, Esma Herzel, Ehud Karavani and Gideon Koren and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Scientific Reports.

In The Last Decade

Michal Chorev

10 papers receiving 431 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michal Chorev Israel	6	220	104	93	56	53	11	439
Jen‐Yeu Wang United States	11	175 0.8×	30 0.3×	93 1.0×	70 1.3×	19 0.4×	22	399
Sara Kuntz Germany	6	53 0.2×	86 0.8×	107 1.2×	8 0.1×	39 0.7×	7	303
Shin-Yi Lin United States	8	488 2.2×	57 0.5×	53 0.6×	85 1.5×	54 1.0×	10	722
Chhavi Chauhan United States	7	137 0.6×	44 0.4×	36 0.4×	9 0.2×	47 0.9×	12	336
Qiansheng Zhan United States	12	332 1.5×	49 0.5×	8 0.1×	33 0.6×	127 2.4×	36	931
Thomas Wolfgruber United States	13	303 1.4×	26 0.3×	23 0.2×	361 6.4×	88 1.7×	24	599
Harry Subramanian United States	10	119 0.5×	12 0.1×	60 0.6×	12 0.2×	12 0.2×	20	438
Ariel Waisman Argentina	14	334 1.5×	15 0.1×	9 0.1×	17 0.3×	31 0.6×	21	482
David Mauduit Belgium	8	311 1.4×	18 0.2×	15 0.2×	27 0.5×	27 0.5×	9	419
María Gabriela Thomas Argentina	13	639 2.9×	8 0.1×	16 0.2×	34 0.6×	60 1.1×	28	829

Countries citing papers authored by Michal Chorev

Since Specialization

Citations

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

Fields of papers citing papers by Michal Chorev

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michal Chorev

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

All Works

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11 of 11 papers shown

1.

Chorev, Michal, et al.. (2024). Curating Electronic Health Record Data to Assess Causal Inference Effect of Metformin on Hypertension Population Progression to Chronic Kidney Disease. 60–65.

2.

Benninger, Felix, et al.. (2024). Comparing the efficacy of anti-seizure medications using matched cohorts on a large insurance claims database. Epilepsy Research. 201. 107313–107313. 3 indexed citations

3.

Chorev, Michal, Adam Spiro, Ella Evron, et al.. (2023). Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment. PubMed. 2022. 385–394. 2 indexed citations

4.

Akselrod-Ballin, Ayelet, Michal Chorev, Yoel Shoshan, et al.. (2019). Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms. Radiology. 292(2). 331–342. 126 indexed citations

5.

Chorev, Michal, et al.. (2017). Identification of introns harboring functional sequence elements through positional conservation. Scientific Reports. 7(1). 4201–4201. 16 indexed citations

6.

Chorev, Michal, et al.. (2017). A deep neural network witharestricted noisy channel for identification of functional introns. 1–6. 1 indexed citations

7.

Chorev, Michal, et al.. (2017). A Data-Driven Decision-Support Tool for Population Health Policies. Studies in health technology and informatics. 245. 332–336. 1 indexed citations

8.

Levin, Liron, Dan Bar-Yaacov, Amos Bouskila, et al.. (2015). LEMONS – A Tool for the Identification of Splice Junctions in Transcriptomes of Organisms Lacking Reference Genomes. PLoS ONE. 10(11). e0143329–e0143329. 5 indexed citations

9.

Chorev, Michal, et al.. (2015). JuncDB: an exon–exon junction database. Nucleic Acids Research. 44(D1). D101–D109. 7 indexed citations

10.

Chorev, Michal & Liran Carmel. (2013). Computational identification of functional introns: high positional conservation of introns that harbor RNA genes. Nucleic Acids Research. 41(11). 5604–5613. 17 indexed citations

11.

Chorev, Michal & Liran Carmel. (2012). The Function of Introns. Frontiers in Genetics. 3. 55–55. 261 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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