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Menu Skip to content Menu Home About Us News Research Publications Resources Team Positions Available Alumni Summer Program Courses Contact Us Ma'ayan Laboratory, Computational Systems Biology icahn.mssm.edu / Inside Mount Sinai Blog Search Menu Skip to content Home About Us News Research Publications Resources Team Positions Available Alumni Summer Program Courses Contact Us 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 Featured Publication Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ranked gene lists contain genes that were previously published in association with the search terms, as well as genes predicted to be associated with the terms based on data integration from multiple sources. The search results are presented with interactive visualizations. To predict gene function, Geneshot utilizes gene-gene similarity matrices from processed RNA-seq data, or from gene-gene co-occurrence data obtained from multiple sources. In addition, Geneshot can be used to analyze the novelty of gene sets and augment gene sets with additional relevant genes. Read More Citation: Lachmann A, Schilder BM, Wojciechowicz ML, Torre D, Kuleshov MV, Keenan AB, Ma’ayan A. Geneshot: search engine for ranking genes from arbitrary text queries . Nucleic Acids Research (2019) 47(W1):W571-W577. Recent News FAIR Your Data Ten Renowned Mount Sinai Faculty Members Honored at Convocation Mount Sinai Researchers Develop Software to Measure the Findability, Accessibility, Interoperability, and Reusability of Biomedical Digital Research Objects Finding Needles in the Haystack: Integrating ‘Omics Data to Predict Toxicity Mount Sinai Researchers Develop Tool that Analyzes Biomedical Data within Minutes Big Data, Networks Identify Cell Signaling Pathways in Lung Cancer Mount Sinai Health System Top 10 Researchers Mount Sinai Researchers Receive NIH Grant to Develop New Ways to Share and Reuse Research Data Students Harness Big Data to Help Solve Medical Challenges The FAIR Data-Sharing Movement: BD2K Centers Make Headway Apply Now for Summer 2020! Program in Biomedical Big Data Science Mount Sinai Center for Bioinformatics Analyzing, visualizing and mining data from omics studies for drug discovery Illuminating the Druggable Genome Knowledge Management Center at ISMMS Featured Software & Apps Enrichr An integrative web-based and mobile gene-list enrichment analysis tool Harmonizome Integrated knowledge about genes and proteins RNA-seq Data Analysis in the Cloud Automatically generates interactive RNA-seq data analysis notebooks Featured MOOCs on Coursera Big Data Science Enroll Now Network Analysis in Systems Biology Enroll Now...