Welcome to the Metabolomics and Metagenomics Lab
Hosein joined Carnegie Mellon University in 2017. He received his B.Sc. in mathematics and EE from Sharif University of Technology, Iran, and Ph.D. in ECE from the University of California, San Diego, working with internationally recognized computational biology leader Pavel Pevzner. He worked for two years as a Bioinformatics Scientist at Illumina before returning to UCSD as a project scientist. In collaboration with researchers from St. Petersburg Academic University of the Russian Academy of Sciences, he developed error-tolerant approaches to connect metagenomics and metabolomics datasets for discovering novel antibiotics. This work resulted in the first discovery of an antibiotic (called Informatipeptin) in a completely automated fashion in 2013.
The research in MetaboloGenomics lab focuses on the development of computational metabolomics and metagenomics methods for antibiotic discovery and microbiome analysis. Microbes in human body interact with their human host and with each other through small molecules (e.g. antibiotics and signaling molecules). Mining the human metagenome has shown that the human microbiome has a great potential for production of small molecules. Most of such molecules remain unknown, despite the fact that they can play a crucial role on human health. MetaboloGenomics lab is specifically interested in analyzing large scale mass spectrometry and metagenomics data using tools from machine learning, genome mining, signal processing, graph theory, and statistics to discover novel genetically synthesized small molecules in the human microbiome. Moreover, Metabologenomics lab focuses on identifying potential ways in which our microbiome may affect health (through identifying novel molecules that may mediate effects).
07/21/21 – Zeyuan, Liu and Louis‘s paper “MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition” has been published in Bioinformatics Journal
06/17/21 – Liu Cao and Mustafa Guler‘s paper “MolDiscovery: learning mass spectrometry fragmentation of small molecules” has been published in Nature Communications
06/02/21 – Bahar Behsaz’s paper “Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery” has been published in Nature Communications
04/15/21 – Mihir Mongia’s paper “Repository scale classification and decomposition of tandem mass spectral data” has been published in Scientific Reports
02/11/21 – Arash Gholamidavoodi, Sean Chang, Hyun Gon Yoo, Anubhav Baweja, Mihir Mongia and Hosein Mohimani’s paper “ForestDSH: a universal hash design for discrete probability distributions” has been published in Data Mining and Knowledge Discovery
10/25/20 – Our lab receives Computational Tool Development for Integrative Systems Biology Data Analysis award from the U.S. Department of Energy. ($1.05M)
01/28/20 – Mihir Mongia , Ben Soudry and Arash Gholamidavoodi ’s paper, “Efficient Database Search via Tensor Distribution Sensitive Bucketing” , has been accepted for PAKDD 2020.
01/09/20 – Mohsen Ferdosi and Arash Gholamidavoodi’s paper, “Measuring Mutual Information Between All Pairs of Variables in Subquadratic Complexity” , has been accepted for AISTATS 2020. Congratulations Mohsen and Arash!
10/01/19 – Our lab receives NIH New Innovator Award ($2.2M)
08/05/19 – Liu Cao and Egor Scherbin’s paper, “Association Networks”, has been accepted at mSystems. Congratulations Liu and Egor!
07/23/19 – Liu Cao’s paper, “MetaMiner“, has been accepted at Cell-Systems. Congratulations Liu!
02/19/19 – Hosein Mohimani named Sloan Research Fellow
10/02/18 – Carnegie Mellon School of Computer Science: New Algorithm Efficiently Finds Antibiotic Candidates
02/60/18 – Pittsburgh Post Gazette: A new CMU algorithm can help thwart antibiotic resistance
01/30/18 – WESA, Pittsburgh’s NPR News Station: Finding A Better Antibiotic Just Got Easier, As Scientists Discover A New Way To Sift Through Data
01/22/18 – Carnegie Mellon School of Computer Sciences: Computational Method Speeds Hunt for New Antibiotics
11/18/16 – University of California San Diego: Big Data for Chemistry: New method helps identify antibiotics in mass spectrometry datasets