A picture guide to cancer progression and monotonic accumulation models: evolutionary assumptions, plausible interpretations, and alternative uses
R Diaz-Uriarte - arXiv preprint arXiv:2312.06824, 2023 - arxiv.org
Cancer progression and monotonic accumulation models were developed to discover
dependencies in the irreversible acquisition of binary traits from cross-sectional data. They …
dependencies in the irreversible acquisition of binary traits from cross-sectional data. They …
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Abstract The Kullback–Leibler (KL) divergence is frequently used in data science. For
discrete distributions on large state spaces, approximations of probability vectors may result …
discrete distributions on large state spaces, approximations of probability vectors may result …
Overcoming Observation Bias for Cancer Progression Modeling
R Schill, M Klever, A Lösch, YL Hu, S Vocht… - … on Research in …, 2024 - Springer
Cancers evolve by accumulating genetic alterations, such as mutations and copy number
changes. The chronological order of these events is important for understanding the …
changes. The chronological order of these events is important for understanding the …
Overcoming Observation Bias for Cancer Progression Modeling
N Beerenwinkel - Research in Computational Molecular Biology: 28th … - books.google.com
Cancers evolve by accumulating genetic alterations, such as mutations and copy number
changes. The chronological order of these events is important for understanding the …
changes. The chronological order of these events is important for understanding the …