Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated w...
PURPOSE: Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheVa...
Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ...
Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual...
The landscape of farming and plant breeding is rapidly transforming due to the complex requirements of our world. The explosion of collectible data has started a revolution in agriculture to the point where innovation must occur. To a commercial orga...
The aim of this study was to evaluate factors influencing the performance of Hucul horses and to develop a prediction model, based on artificial neural (AI) networks for predict horses' classification, relying on their performance value assessment du...
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...
Longitudinal 'omics analytical methods are extensively used in the evolving field of precision medicine, by enabling 'big data' recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbat...
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data....
Proceedings of the National Academy of Sciences of the United States of America
Jun 21, 2022
Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, pr...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.