Noam Auslander

1.7k total citations
18 papers, 697 citations indexed

About

Noam Auslander is a scholar working on Molecular Biology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Noam Auslander has authored 18 papers receiving a total of 697 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 6 papers in Cancer Research and 3 papers in Pathology and Forensic Medicine. Recurrent topics in Noam Auslander's work include Bioinformatics and Genomic Networks (5 papers), Cancer Genomics and Diagnostics (4 papers) and Genetic factors in colorectal cancer (3 papers). Noam Auslander is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), Cancer Genomics and Diagnostics (4 papers) and Genetic factors in colorectal cancer (3 papers). Noam Auslander collaborates with scholars based in United States, Israel and Germany. Noam Auslander's co-authors include Eugene V. Koonin, Ayal B. Gussow, Yuri I. Wolf, Guilhem Faure, Feng Zhang, Eytan Ruppin, Sean Benler, Keren Yizhak, Yu Fan and Tim Beißbarth and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Noam Auslander

18 papers receiving 689 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Noam Auslander United States 12 377 147 111 101 79 18 697
Karel H. M. van Wely Spain 21 864 2.3× 63 0.4× 73 0.7× 64 0.6× 130 1.6× 38 1.2k
Kaori Ide Japan 17 706 1.9× 78 0.5× 42 0.4× 87 0.9× 32 0.4× 53 1.1k
Debomita Sengupta India 16 653 1.7× 85 0.6× 78 0.7× 105 1.0× 24 0.3× 31 944
Raymond V. Fucini United States 16 792 2.1× 251 1.7× 64 0.6× 140 1.4× 30 0.4× 20 1.3k
Jin‐Soo Maeng South Korea 13 473 1.3× 295 2.0× 45 0.4× 32 0.3× 43 0.5× 29 796
Arun H. Patil India 17 511 1.4× 67 0.5× 219 2.0× 49 0.5× 20 0.3× 36 903
Georgij Arapidi Russia 15 405 1.1× 54 0.4× 108 1.0× 56 0.6× 20 0.3× 54 672
Yafeng Zhu China 16 642 1.7× 67 0.5× 144 1.3× 73 0.7× 24 0.3× 28 1.0k
Yizhao Luan China 11 424 1.1× 39 0.3× 63 0.6× 156 1.5× 38 0.5× 16 733

Countries citing papers authored by Noam Auslander

Since Specialization
Citations

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

Fields of papers citing papers by Noam Auslander

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Noam Auslander

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

All Works

18 of 18 papers shown
1.
Leu, Julia I-Ju, Anne‐Marie Mes‐Masson, Jennifer Sims‐Mourtada, et al.. (2025). Mutant p53 binds and controls estrogen receptor activity to drive endocrine resistance in ovarian cancer. Genes & Development. 40(3-4). 199–214. 1 indexed citations
2.
Emons, Georg, Noam Auslander, Peter Jo, et al.. (2022). Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy. British Journal of Cancer. 127(4). 766–775. 11 indexed citations
3.
Gussow, Ayal B., Eugene V. Koonin, & Noam Auslander. (2021). Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit. NAR Cancer. 3(2). zcab017–zcab017. 3 indexed citations
4.
Auslander, Noam, Ayal B. Gussow, & Eugene V. Koonin. (2021). Incorporating Machine Learning into Established Bioinformatics Frameworks. International Journal of Molecular Sciences. 22(6). 2903–2903. 67 indexed citations
5.
Gussow, Ayal B., Noam Auslander, Guilhem Faure, et al.. (2020). Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses. Proceedings of the National Academy of Sciences. 117(26). 15193–15199. 156 indexed citations
6.
Auslander, Noam, Ayal B. Gussow, Sean Benler, Yuri I. Wolf, & Eugene V. Koonin. (2020). Seeker: alignment-free identification of bacteriophage genomes by deep learning. Nucleic Acids Research. 48(21). e121–e121. 75 indexed citations
7.
Gussow, Ayal B., Noam Auslander, Yuri I. Wolf, & Eugene V. Koonin. (2020). Prediction of the incubation period for COVID-19 and future virus disease outbreaks. BMC Biology. 18(1). 186–186. 19 indexed citations
8.
Auslander, Noam, Yuri I. Wolf, & Eugene V. Koonin. (2020). Interplay between DNA damage repair and apoptosis shapes cancer evolution through aneuploidy and microsatellite instability. Nature Communications. 11(1). 1234–1234. 31 indexed citations
9.
Auslander, Noam, Daniel M. Ramos, Ivette Zelaya, et al.. (2020). The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases. Molecular Systems Biology. 16(12). e9701–e9701. 5 indexed citations
10.
Auslander, Noam, Daniel M. Ramos, Ivette Zelaya, et al.. (2020). The GENDULF Algorithm: Mining Transcriptomics to Uncover Modifier Genes for Monogenic Diseases. SSRN Electronic Journal. 2 indexed citations
11.
Auslander, Noam, Yuri I. Wolf, & Eugene V. Koonin. (2019). In silico learning of tumor evolution through mutational time series. Proceedings of the National Academy of Sciences. 116(19). 9501–9510. 18 indexed citations
12.
Kwon, So Mee, Anuradha Budhu, Hyun Goo Woo, et al.. (2019). Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer. Scientific Reports. 9(1). 16930–16930. 14 indexed citations
13.
Auslander, Noam, et al.. (2019). Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression. Cancer Informatics. 18. 2411581848–2411581848. 27 indexed citations
14.
Emons, Georg, Melanie Spitzner, Noam Auslander, et al.. (2017). Chemoradiotherapy Resistance in Colorectal Cancer Cells is Mediated by Wnt/β-catenin Signaling. Molecular Cancer Research. 15(11). 1481–1490. 104 indexed citations
15.
Auslander, Noam, Behzad M. Toosi, Keren Yizhak, et al.. (2017). An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer. Molecular Systems Biology. 13(12). 956–956. 37 indexed citations
16.
Ferber, Shiran, Galia Tiram, Ana Sousa‐Herves, et al.. (2017). Co-targeting the tumor endothelium and P-selectin-expressing glioblastoma cells leads to a remarkable therapeutic outcome. eLife. 6. 79 indexed citations
17.
Auslander, Noam, Keren Yizhak, Anuradha Budhu, et al.. (2016). A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer. Scientific Reports. 6(1). 29662–29662. 41 indexed citations
18.
Auslander, Noam, Allon Wagner, Matthew Oberhardt, & Eytan Ruppin. (2016). Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction. PLoS Computational Biology. 12(9). e1005125–e1005125. 7 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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