Analyse-Predict
Analysis, Modelling and Prediction (AMP)
The Analysis, Modelling and Prediction (AMP) workstream drives the development and adoption of computational tools and best practices for analyzing and predicting variant effects from MAVEs and computational models to advance mechanistic and clinical insights.
Our workstream is dedicated to advancing computational tools and approaches that unlock the potential of variant effect analysis for scientific discovery and clinical impact. We focus on two complementary areas: computational tools for modelling variant impacts (including variant effect predictors and beyond) and computational methods for analyzing data from multiplexed assays of variant effect (MAVEs).
Joseph Marsh
University of Edinburgh
Dr. Marsh leads a group in the MRC Human Genetics Unit, University of Edinburgh. His work seeks to understand the molecular mechanisms by which mutations affect proteins and thereby cause disease, combining computational modelling and high-throughput experiments to improve the interpretation of protein-coding variants.
Dr. Vikas Pejaver
Institute for Genomic Health and the Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health and the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His work focuses on rare genetic variants and diseases, using machine learning and advanced computational techniques on genetic, molecular and clinical data. He actively collaborates within the ClinGen and IGVF Consortia.
Mafalda Dias (CRG)
Jonathan Frazer (CRG)
Sushant Kumar (University of Toronto)
Kresten Lindorff-Larsen (Copenhagen University)
David McCandlish (Cold Spring Harbor Laboratory)
Kyriaki Michailidou (Cyprus Institute of Neurology and Genetics)
Victoria Offord (Wellcome Sanger Institute)
Rose Orenbuch (Harvard Medical School)
Fabrizio Pucci (Université Libre de Bruxelles)
Courtney Shearer (Harvard University)
Jimmie Ye (UC San Francisco)
Juannan Zhou (University of Florida)
Become a Member
The Alliance welcomes individuals from academia, industry, government or other entities anywhere in the world.
The AMP workstream drives the development and adoption of computational tools and best practices for analyzing and predicting variant effects from MAVEs and computational models to advance mechanistic and clinical insights.