Research team used ProteinMPNN to expand the sequence space of synthetic binding proteins (SBPs), improving their solubility ...

Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

The three-dimensional world of ordinary experience—the universe filled with galaxies, stars, planets, houses, boulders, and ...

In a major leap forward for genetic and biomedical research, scientists have developed a powerful new artificial intelligence tool that can predict the 3D shape of chromosomes inside individual cells ...

Abstract: Deep reinforcement learning (DRL) integrates the feature representation ability of deep learning with the decision-making ability of reinforcement learning so that it can achieve powerful ...

In recent years, advancements in bioinformatics have led to its widespread application along with machine learning techniques for identifying treatment targets for various diseases (13).Gene chips, a ...

However, microbiome data are often characterized by the limited samples and high-dimensional features, which pose a great challenge for machine learning methods. Therefore, this paper proposes a novel ...

To address this need, we present “Pfly”; a customizable deep learning model for peptide detectability prediction. Afterward, the flyer and non-flyer datasets were merged, duplicated peptides between ...

Discussion: This study integrates bioinformatics, machine learning, and molecular validation to identify the shared molecular mechanisms of psoriasis and CD, uncovering novel biomarkers and potential ...

aArtificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA bDepartment of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer ...