Computational and Mathematical Methods to Study the Complexity of Regulatory Networks in Mammalian Cells
The Ma’ayan Laboratory applies machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation.
Our research team develops software systems to help experimental biologists form novel hypotheses from high-throughput data, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular systems. Read More
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Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age
Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model the uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. Read More
Wang Z, Li L, Glicksberg BS, Israel A, Dudley JT, Ma’ayan A. Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age. Journal of Biomedical Informatics 2017 Nov 4 pii: S1532-0464(17)30240-X.