jmarshlab.bsky.social
👤 Joined on 20 November 2024
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@jmarshlab.bsky.socialHow do computational predictors compare with experimental assays (MAVEs) for variant classification? Across five hereditary cancer genes, MAVE scores were more conservative (fewer pathogenic calls) yet more decisive (fewer indeterminate calls) after ACMG/AMP calibration. doi.org/10.1186/s130...
Show PostThe AVE Alliance recently appointed Prof. Joe Marsh @jmarshlab.bsky.social to its Executive Committee. This is in addition to his role as co-chair of the Analysis, Modelling and Prediction workstream. Learn more about AVE's Executive Committee and leadership: www.varianteffect.org/leadership/
Show PostGreat to see this piece with @jmarshlab.bsky.social talking about developing a resource cataloguing computational models for predicting variant effects, what makes non-coding predictors so different from their coding counterparts, and whether VEPs will ever replace deep mutational scanning 👇🏽
Show PostToday I am wearing #blue4ReNU for ReNU Syndrome awareness day 💙 4th February (or 4/2), was chosen by families to reflect the 4-2 in RNU4-2 🧬 ReNU syndrome is a severe neurodevelopmental disorder impacting ~100,000 individuals globally 🧠 🌎 lnkd.in/dvruKxZH #ReNUSyndrome @renusyndrome.bsky.social
Show PostThis was a team effort involving effort from many including IGC colleagues @jmarshlab.bsky.social @csemple.bsky.social @ailithewing.bsky.social, @bioggrimes.bsky.social, plus others not on bluesky. Important contributions from Agavni Mesropian and others in the group of Josep M Llovet in Bacelona
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