#MathOnco Issue 68: patient-specific; hack-a-thons; cancer risk; information theory classifiers
This week in
Math Oncology
May 30, 2019 ~ Issue 68
From the editor
#MathOnco friends,
This issue is particularly jam-packed with neat papers! Scroll down for patient-specific modeling, hack-a-thons, cancer risk, information theory classifiers, and more. I also came across LIME (Local Interpretable Model-agnostic Explanations): one method of making sense (hence the "interpretable" in the acronym) of machine learning classifiers.
-Jeffrey West
#MathOnco Publications
Multi-stage models for the failure of complex systems, cascading disasters, and the onset of disease
Authors: Anthony J. Webster
“Hacking” Our Way across Interdisciplinary Boundaries
Authors: Joshua Pan, Kaitlyn Johnson
Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data
Authors: Xiaoran Lai, Oliver M Geier, Thomas Fleischer, Øystein Garred, ..., Olav Engebråten, Alvaro Köhn-Luque, Arnoldo Frigessi
Patient-specific tumor growth trajectories determine persistent and resistant cancer cell populations during treatment with targeted therapies
Authors: Clemens Grassberger, David M McClatchy, Changran Geng, Sophia C Kamran, Florian Fintelmann, Yosef E Maruvka, Zofia Piotrowska, Henning Willers, Lecia V. Sequist, Aaron N. Hata and Harald Paganetti
#MathOnco Preprints
When will the cancer start? Elucidating the correlations between cancer initiation times and lifetime cancer risks
Authors: Hamid Teimouri, Maria Kochugaeva, Anatoly B. Kolomeisky
From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response
Authors: Jill A. Gallaher, Susan C. Massey, Andrea C. Hawkins-Daarud, Sonal S. Noticewala, ..., Luis Gonzalez-Cuyar, Joseph Juliano, Orlando Gil, Kristin R. Swanson, Peter Canoll, Alexander R. A. Anderson
Information Theoretic Feature Selection Methods for Single Cell RNA-Sequencing
Authors: Umang Varma, Justin Colacino, Anna Gilbert
A Review of Mathematical Models of Metastatic Cancer
Authors: Katrina Hoban
LIME: Local Interpretable Model-agnostic Explanations
github/marcotcr: "This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this preprint.
#MathOnco - Book of the month
Evolutionary Dynamics: Exploring the Equations of Life
Martin Nowak: This book, "draws on the languages of biology and mathematics to outline the mathematical principles according to which life evolves [and] presents a range of analytical tools that can be used to this end: fitness landscapes, mutation matrices, genomic sequence space, random drift, quasispecies, replicators, the Prisoner’s Dilemma, games in finite and infinite populations, evolutionary graph theory, games on grids, evolutionary kaleidoscopes, fractals, and spatial chaos."
Most clicked links of April
Can we afford to ignore the role of space in cancer and pre-cancerous tissue any longer?
Prediction of Bone Metastasis in Inflammatory Breast Cancer Using a Markov Chain Model
Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion
Jobs
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.