The intertwined quest for understanding biological intelligence and creating artificial intelligence | Stanford HAI
In this post, Dr. Surya Ganguli explores how AI, neuroscience, psychology and cognitive science, along with allied disciplines in the mathematical, physical and social sciences, have in the past, and will continue in the future, to work together in pursuit of the intertwined quest to understand and create intelligent systems.
Accounting for proximal variants improves neoantigen prediction | Nature Genetics
Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. Researchers evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also affect neoantigen binding predictions.
Molecular Sets (MOSES): a collaborative benchmarking platform for generative drug discovery | Medium
The ongoing research in machine learning, in particular, deep learning, brings up the issues of reproducibility and fair comparison of different approaches. While there are multiple methods for generating novel molecular structures with machine learning models, there is no conventional way to run and evaluate the performance of these generative models. The MOSES platform provides a standardized benchmarking dataset, a set of open-sourced models with unified implementation, and metrics to evaluate and assess the results of generation.