Lab news

We welcome Ron Saad who is joining the group as a Master student. Ron has a BSc from the Technion and is completing his service in the IDF.

A new article by Lazareva et al. benchmarked eight active module identification algorithms on multiple real and randomized biological networks. The authors conclude that Hagai’s DOMINO algorithm was the only algorithm that produced more meaningful candidate disease modules on real than on random networks. Congrats Hagai!

A new article by Duan et al. conducted an extensive benchmark on ten algorithms for multi-omic clustering. The study used nine cancers and four omics and conducted multiple common and novel tests. It concluded that the NEMO algorithm developed by Nimrod, as well as SNF, performed well in all criteria, and recommended them for cancer subtyping.…

Prof. Igor Ulitsky (Weizmann Institute) was selected as the 2020 Life Sciences Laureate of the Blavatnik Awards for Young Scientists in Israel. He received the award in a recent ceremony. Igor did his PhD in Shamir group and is a leader in understanding the non-coding human genome. See here for a video on his award.…

Congratulations to David Pellow for the publication of the SCAPP algorithm. SCAPP, developed jointly with I. Mizrahi’s group, is a new tool for extraction and assembly of plasmids from metagenomic samples.  

The paper , by Yael Ben-Ari and colleagues, on utilizing universal hitting sets in the MSP algorithm, has been accepted to the ACM-BCB conference.  

Papers by Omer, Nimrod, David, Hagai and Dan Coster were accepted to ISMB/ECCB 2021. All will be presented as posters, and the first four in talks as well. See here for the details. Congratulations!

Congratulations to Dan Coster who won The Celia and Marcus Maus Prize for MSc students in Computer Science, for the academic year 2020-2021.

Congratulations to Nimrod Rappoport who won the Dan David Scholarship in molecular medicine.

Omer and Dan’s study on predicting deterioration in COVID-19 patients was accepted to the ICLR 2021 Workshop: Machine Learning for Preventing and Combating Pandemics. The study is a collaboration with Maya from the group and the clinical partners I. Attar, S. Shenhar-Tsafraty, S. Berliner, G. Rahav and O. Rogowski.