Axel Martin, MS

Research Biostatistician

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Axel Martin, Research Biostatistician

Education

University of Michigan

Current Research Interest 

Axel Martin is currently pursuing a PhD degree at New-York Univeristy Langone, in Biostatistics focusing on causal inference. His primary research interests focus on the prediction and stratification of survival of late-stage cancer patients as a function of their genomic profiles. He develops flexible ensemble statistical learning methods to create clinically meaningful risk groups that can reveal groups at particularly high risk (OncoCast: https://github.com/AxelitoMartin/OncoCast). Axel’s focus on genomic data also led him to develop, in collaboration of other biostatisticians, robust frameworks for MSKCC genomic data (IMPACT) processing, visualizations and analysis. More recently he has been working with the GENIE statistical team of the Biostatistics department at MSKCC. They develop methods for data processing of clinico-genomic datasets of multiple cancer centers in North America. Axel has been focusing on multi-state modeling and causal inference methods to associate the various treatments given to late-stage lung adenocarcinoma patients and their clinical outcomes.

Publications

  1. Ronglai Shen, Axel Martin, Ai Ni, Matthew Hellmann, Kathryn C. Arbour, Emmet Jordan, Arshi Arora, Ryan Ptashkin, Ahmet Zehir, Mark G. Kris, Charles M. Rudin, Michael F. Berger, David B. Solit, Venkatraman E. Seshan, Maria Arcila, Marc Ladanyi, and Gregory J. Riely
    Harnessing Clinical Sequencing Data for Survival Stratification of Patients With Metastatic Lung Adenocarcinomas
    JCO Precision Oncology 2019 :3, 1-9
  2. Denise D Correa, Jaya Satagopan, Axel Martin, Erica Braun, Maria Kryza-Lacombe, Kenneth Cheung, Ajay Sharma, Sofia Dimitriadoy, Kelli O’Connell, Siok Leong, Sasan Karimi, John Lyo, Lisa M DeAngelis, Irene Orlow, Genetic variants and cognitive functions in patients with brain tumors, Neuro-Oncology, Volume 21, Issue 10, October 2019, Pages 1297–1309, https://doi.org/10.1093/neuonc/noz094
  3.  Chakraborty, S., Martin, A., Guan, Z. et al. Mining mutation contexts across the cancer genome to map tumor site of origin. Nat Commun 12, 3051 (2021). https://doi.org/10.1038/s41467-021-23094-z
  4.  Oetjens, M.T., Martin, A., Veeramah, K.R. et al. Analysis of the canid Y-chromosome phylogeny using short-read sequencing data reveals the presence of distinct haplogroups among Neolithic European dogs. BMC Genomics 19, 350 (2018). https://doi.org/10.1186/s12864-018-4749-z