Axel Martin
@CLaunderer
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Research biostatistician @sloan_kettering. Specialize in machine learning for predictive genomics. Interested in causal inference methods for ITRs.
New York, NY
Joined April 2019
After pulling & QAing #genomics data, the {gnomeR} package (by @Hannah_Fuchs1, @arorarshi, @CLaunderer, @karissawhiting, Mike Curry and more) helps #rstats analysts process it into an analysis-ready format. Docs: https://t.co/0polg4ApCT
#rstats examples below 👇🧵
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Today I want to highlight the {genieBPC} #rstats package by @jessicalavs, @sammi_brown8, @Hannah_Fuchs1, @CLaunderer, @statistishdan, and myself (@karissawhiting). It offers functions for pulling and processing data from the @AACR GENIE BPC Project https://t.co/scsuovoL4Q
#cran
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✨A new release of {cbioportalR} v1.1.0 is now available on CRAN! {cbioportalR} provides #rstats users with user-friendly functions for direct access to genomic and clinical data from the @cbioportal web resource. https://t.co/mwa1N4uvXE Updates include: 🧵
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📢🔥🚨#MSKBiostats @MSKCancerCenter is #hiring a #statistics immuno-oncology postdoc 🔗 https://t.co/CiPxt6qT5e 📩A cover letter, cv, and the names of 3⃣references to Dr. Ronglai Shen (shenr@mskcc.org) 📌Successful applicant will be supervised by Drs. @ronglais & @KPanageas
#WeAreHiring a #statistics immuno-oncology postdoc at #MSKBiostats! To apply, send (1) cover letter, (2) CV, and (3) names of 3 references to Dr. Ronglai Shen (shenr@mskcc.org) https://t.co/sJLyZvfCyw
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🎉👏 #MSKBiostats @jessicalavs, @sammi_brown8, Mike Curry, @CLaunderer, @statistishdan & @karissawhiting recently developed {genieBPC}, a user-friendly #rstats package for processing complex clinico-genomic data from @AACR Project #genieBPC
https://t.co/meBCgIlBgV
academic.oup.com
AbstractMotivation. Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative
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👀We spy {genieBPC} on this list! #MSKBiostats @jessicalavs, @sammi_brown8, Mike Curry, @karissawhiting, @CLaunderer & @statistishdan collaborated on the {genieBPC} R package, a data processing pipeline for working with the @AACR #GENIEBPC data https://t.co/scsuovoL4Q
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Do you work with #genomics data for cancer research? 📢 Check out the first CRAN release of the {cbioportalR} #RStats package to browse and pull @cbioportal data sets directly in R. Examples & docs: https://t.co/tjRHDRSDZJ Feedback Welcome: https://t.co/1EvZnemS8q
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📅 Join us for the next #MSKBiostats seminar next Wed 5/4/2022 10;30am ET with guest speaker @ildiazm on Causal survival analysis under competing risks using longitudinal modified treatment policies 📩 Email Christy Rajcoomar to register: rajcoomc@mskcc.org
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The Australien Government has made an ad about Carbon Capture and Storage, and it’s surprisingly honest and informative. #CCS
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📣Yours truly made an appearance in this month’s @AmstatNews magazine!! @MSKBiostats @compbiopodcast @92Y check it out - “P is for Podcaster and Potter! And P-value!”. Honored to share a bit about myself 🙇🏻♀️
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Beyond excited to share our recent paper in @NatureComms on the prediction of the tissue site of origin of cancer tumors using the hidden genome of #rarevariants! Joint work with @CLaunderer, Zoe Guan, @ColinBBegg, and @ronglais. https://t.co/EsWTWDNjso 1/n
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Using #genomic and #epigenomic meta-features to predict the tissue of origin of cancer from the hidden genome of #RareVariants: new #CancerResearch paper by @SaptarshiCh @CLaunderer @ColinBBegg @ronglais
@NatResCancer
https://t.co/Um1TXHoUWk
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Biostatisticians often get asked, "What exactly do you do?" @rkatlady has a great blog post that breaks down our day (and by hour!). Most of what we do involves: 👩💻🧑💻programming 📝writing 📚reading 🤝meeting & much more!
Revisiting my first blog post, “A Day in the Life of a Biostatistician,” for high school/college students who aren’t sure what a job in biostats might actually entail. To my surprise, I frequently receive emails about this post... 1/2 https://t.co/nsbmjGpiwc
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Excited (abd a bit nervous!) to be on my very first panel! #ENAR2021
1⃣6⃣ On the 16th, @jessicalavs will participate in a panel discussion on challenges, opportunities and all things career in the statistics/data science field. 🕘9-10:45am Oh, The Places You Could Go: Surprising Careers in Statistics and Data Science 3/4 🧵
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My perspective of life in NYC last spring as a biostatistician on Covid-19 response efforts is in this month's @signmagazine. It's very personal writing, but I tried to use my own experiences to capture the escalation and shared emotions of the time.
Katherine Hoffman (@rkatlady) is a biostatistician in the pulmonary and critical care team of a New York City hospital, who found herself part of the #covid19 response when the outbreak first hit in March 2020. This is her story: https://t.co/R77QIxsU3x
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spotlight on #R📦 FACETS by @ronglais @VeSeshan to study Copy Number alterations in WGS, WXS and targeted panel platforms to guide treatment decisions based on tumor purity, ploidy and clonal heterogeneity adjusted integer CN calls. #Rstats #genomics
github.com
Algorithm to implement Fraction and Copy number Estimate from Tumor/normal Sequencing. - mskcc/facets
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Can tumor genomics help predict which patients are at risk for recurrence after resection of LUAD? We show that integration of genomic/clinicopathologic factors improves risk stratification and prediction of recurrence compared to the current TNM model.
jamanetwork.com
This cohort study identifies tumor genomic factors independently associated with recurrence in patients with lung adenocarcinoma and develops a machine-learning prediction model to determine whether...
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Actually for those interested the 📦 ‘s website might be useful — https://t.co/JEjzMyH8J6 — feedback welcome 🙏
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🙇♀️ So excited that our committee was awarded the @ASA_Biometrics grant so that we can continue to build the #BridgetoBiostats 🌉
1⃣ Raise awareness of & generate interest in biostats among underserved and underrepresented HS students in NYC through “Biostats Day” programs 🧮 2⃣ Provide learning opportunities by developing a NYC-based biostat enrichment course 3⃣ Meet & learn from students 🙋🏾♂️👩🏿💻💁🏻 3/3
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🚨 New blog post (a series!) An Illustrated Guide to Targeted Maximum Likelihood Estimation 🎯 Part 1: motivation for “targeting” an estimand for inference and why we can & should incorporate data-adaptive/machine learning models 🧵1/ https://t.co/OKWTvIWU8k
#causaltwitter
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