#MathOnco Issue 106: selecting for good science; immune cell behavior; clinical prediction; drug-induced resistance; reinforcement learning
This week in
Math Oncology
Mar. 12, 2020 ~ Issue 106
From the editor
Hello!
Today's issue of "This week in Mathematical Oncology" includes models of immune cell behavior, clinical prediction, drug-induced resistance, reinforcement learning, and frequency-dependent interactions.
There's also an interesting preprint on how to "select" for good science, which has in it a great description of the power of "hypothesis-generating" models that many in math onco are fond of (including myself).
Enjoy!
-Jeffrey West
#MathOnco Publications
Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data
Authors: Sayaka Miura, Tracy Vu, Jiamin Deng, Tiffany Buturla, Olumide Oladeinde, Jiyeong Choi, Sudhir Kumar
Modeling immune cell behavior across scales in cancer
Authors: Sahak Z. Makaryan, Colin G. Cess, Stacey D. Finley
Imaging of intratumoral heterogeneity in high-grade glioma
Authors: Leland S. Hu, Andrea Hawkins-Daarud, Lujia Wang, Jing Li, Kristin R. Swanson
Validation and Utility Testing of Clinical Prediction Models
Authors: Amin Adibi, Mohsen Sadatsafavi, John P. A. Ioannidis
The microcosmos of intratumor heterogeneity: the space-time of cancer evolution
Authors: Michalina Janiszewska
#MathOnco Preprints
Drug-induced resistance in micrometastases: analysis of spatio-temporal cell lineages
Authors: Judith Pérez-Velázquez, Katarzyna A. Rejniak
The Unreasonable Effectiveness of Inverse Reinforcement Learning in Advancing Cancer Research
Authors: John Kalantari, Heidi Nelson, Nicholas Chia
Frequency-dependent interactions determine outcome of competition between two breast cancer cell lines
Authors: Audrey R Freischel, Mehdi Damaghi, Jessica J Cunningham, Arig Ibrahim-Hashim, Robert J Gillies, Robert A Gatenby, Joel S Brown
A novel mathematical model of heterogeneous cell proliferation
Authors: Sean T. Vittadello, Scott W. McCue, Gency Gunasingh, Nikolas K. Haass, Matthew J. Simpson
The natural selection of good science
Authors: Alexander J. Stewart, Joshua B. Plotkin
Thomas Ballinger: "Observable, like Jupyter, is a computational notebook that’s great for doing data science and visualization, where “notebook” refers to a series of cells containing prose, code, and visualizations. Observable runs a superset of JavaScript, entirely in your browser, so it draws on a different ecosystem than the Python one you may be used to. This tutorial shows Jupyter users how to make the most of Observable."
#MathOnco - Book of the month
Tales of Impossibility: The 2000-Year Quest to Solve the Mathematical Problems of Antiquity
David S Richeson: "Tales of Impossibility recounts the intriguing story of the so-called problems of antiquity, four of the most famous and studied questions in the history of mathematics. First posed by the ancient Greeks, these compass and straightedge problems—squaring the circle, trisecting an angle, doubling the cube, and inscribing regular polygons in a circle—have served as ever-present muses for mathematicians for more than two millennia. David Richeson follows the trail of these problems to show that ultimately, their proofs—demonstrating the impossibility of solving them using only a compass and straightedge—depended upon and resulted in the growth of mathematics."
Jobs
Computational Approaches to Breast Cancer Evolution - Postdoc (Marc Ryser)
Postdoctoral Fellow in Mathematical Oncology (Russell Rockne)
Pre-leukemic Dynamics – MSc or PhD Studentship (Morgan Craig)
Quantitative Systems Pharmacology (QSP) Modeler - Cell Therapy (Dean Bottino)
Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)
Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)
Do you see something we missed? Reply to this email to send us an idea for next week's issue.
The #MathOnco newsletter is maintained by Jeffrey West.
If you were forwarded this email, subscribe for free here to get it delivered every week.