Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...
A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...
A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...
The key contributions of this work are as follows: To the best of our knowledge, we introduce the first application of a Bayesian neural network framework that integrates evolutionary sequence ...
Network Models in Cancer: Advances in Systems-Level Insights and Precision Oncology ...
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