Rare diseases affect 30 million people in the USA and more than 300–400 million
worldwide, often causing chronic illness, disability, and premature death. Traditional …

A variety of machine learning methods such as naive Bayesian, support vector machines
and more recently deep neural networks are demonstrating their utility for drug discovery …

One of the most pressing challenges in genomic medicine is to understand the role played
by genetic variation in health and disease. Thanks to the exploration of genomic variants at …

Rare diseases, an emerging global public health priority, require an evidence-based
estimate of the global point prevalence to inform public policy. We used the publicly …

Abstract For over 50 years Mendelian Inheritance in Man has chronicled the collective
knowledge of the field of medical genetics. It initially cataloged the known X-linked …

The information about the genetic basis of human diseases lies at the heart of precision
medicine and drug discovery. However, to realize its full potential to support these goals …

Digital data are anticipated to transform medicine. However, most of today's medical data
lack interoperability: hidden in isolated databases, incompatible systems and proprietary …

Abstract ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) at the National Center for
Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical …

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery
(PDD), which relies primarily on knowledge about phenotype changes associated with …

Deep phenotyping has been defined as the precise and comprehensive analysis of
phenotypic abnormalities in which the individual components of the phenotype are observed …