Machine Learning for Healthcare and Life Sciences
The Machine Learning for Healthcare and Life Sciences group specializes in developing algorithms that learn to recognize complex patterns within rich and massive data. The group explores ways to make intelligent data-driven decisions. The team's focus lies in developing and applying machine learning and data mining tools to an array of different challenging problems from clinical genomic analysis, through designing clinical decision support systems, to analyzing real world evidence for personalized medicine.
The team has participated in several EU consortia in FP6 and FP7 and is now seeking to partner with computational, experimental, and clinical groups in submitting H2020 proposals that involve analysis of complex omics, clinical, and environmental data. Of particular interest are projects that integrate various data modalities recorded over time, where the team can utilize its expertise in building computational tools to model the system dynamics and to infer causal interactions between the various entities. Contact Chen Yanover or Yaara Goldschmidt for more info.
- Real World Evidence
- Clinical Genomic Analysis
- Far Reaching Research projects
- Decision support systems