Human Phenotype Project Data for Personalized Medicine Approaches

Prof. Eran Segal's gropup at the Weizmann Institute of Sciuence develop a robust and generalizable artificial intelligence (AI) models that can leverage multimodel data of the Human Phenotype Project (HPP) and other data sources for biomedical research in the field of metabolic and cardiovascular diseases and sleep medicine. We have established the HPP, a large-scale deep-phenotype prospective longitudinal cohort with >13,000 participants to date, aimed at identifying novel molecular markers with diagnostic, prognostic and therapeutic value, and at developing prediction models for disease onset and progression. Included in the cohort are ~7,700 people with 40 diseases such as metabolic diseases, neurological disorders, and cardiovascular disease, where some people have multiple comorbidities. For each participant, their deep phenotype profile consists of diverse modalities, including imaging (e.g., ultrasound, retina scans), time-series (e.g., electrocardiogram; ECG, continuous glucose monitor‐ing; CGM and sleep), multi-omics (e.g., transcriptome, genetics, microbiome, metabolome), tabular (blood tests, body measures), and ques‐tionnaire and APP data types (e.g., lifestyle and nutritional habits). Repeated measures are collected every two years. Collectively, this rich and heterogeneous dataset represents a unique opportunity for developing and validating foundation AI models that can transform the biomedical field. We are also open to partnering with other project that benefit from the HPP data and advanced computational capabilities