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

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lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs
Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18,705 human and 11,274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at

Citation: Marino GB, Wojciechowicz ML, Clarke DJB, Kuleshov MV, Xie Z, Jeon M, Lachmann A, Ma’ayan A. lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs. Database (Oxford) 2023 Mar 4;2023:baad009.