[I]. Tumor evolvability and intra-tumor heterogeneity: Phylogenies derived from matched transcriptome reveal the evolution of cell populations and temporal order of perturbed pathways in breast cancer brain metastases / Yifeng Tao, Haoyun Lei, Adrian V. Lee, Jian Ma, and Russell Schwartz
Modeling the evolution of Ploidy in a resource restricted environment / Gregory Kimmel, Jill Barnholtz-Sloan, Hanlee Ji, Philipp Altrock, and Noemi Andor.

[II]. Imaging and scientific visualization for cancer research: cmIF : a Python library for scalable multiplex imaging pipelines / Jennifer Eng, Elmar Bucher, Elliot Gray, Lydia Grace Campbell, Guillaume Thibault, Laura Heiser, Summer Gibbs, Joe W. Gray, Koei Chin, and Young Hwan Chang.

[III]. Statistical methods and data mining for cancer research (SMDM): Accurate and flexible Bayesian mutation call from multi-regional tumor samples / Takuya Moriyama, Seiya Imoto, Satoru Miyano, and Rui Yamaguchi
Flexible data trimming for different machine learning methods in omics-based personalized oncology / Victor Tkachev, Anton Buzdin, and Nicolas Borisov.

[IV]. Spatio-temporal tumor modeling and simulation (STTMS): Towards model-based characterization of biomechanical tumor growth phenotypes / Daniel Abler, Philippe Büchler, and Russell C. Rockne
Population modeling of tumor growth curves, the reduced Gompertz model and prediction of the age of a tumor / Cristina Vaghi, Anne Rodallec, Raphaelle Fanciullino, Joseph Ciccolini, Jonathan Mochel, Michalis Mastri, John M. L. Ebos, Clair Poignard, and Sebastien Benzekry.