January ABC Seminar: Efrat Muller – Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

January ABC Seminar: Efrat Muller – Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

Posted on by Tarissa Arnold Mages

The human gut microbiome, and its metabolic activity in particular, have been implicated in a wide range of disease states, including metabolic disorders, inflammatory bowel diseases, and colorectal cancer. This growing appreciation for the impact of the gut microbiome’s metabolism on human health has given rise to studies that generate both microbiome and metabolome high-throughput data from human gut microbiome samples. Truly integrated analysis of both omic datasets, however, remains a challenging task. My research aims to develop new frameworks for analyzing and integrating these datasets using a combination of machine learning, metabolic modeling, and network analysis. I’ll specifically present two projects: the first evaluates the robustness of microbiome-metabolome associations using machine learning and meta-analysis models; the second aims to identify “multi-omic modules” that capture both cross-omic associations and associations with disease simultaneously. Taken together, these frameworks can enhance our understanding of microbiome-metabolome connections and equip the microbiome research community with novel methods for such integrated data analysis.

Speaker: Efrat Muller
Affiliation: Prof. Elhanan Borenstein Lab, Tel Aviv University
Position: Doctoral Candidate
Hosted by: Jennifer Dawkins, Gerber Lab

Date: Monday January 22, 2024
Time: 10:00AM-11:00AM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Links:
Google Scholar
Linkedin

Efrat Muller is a Computer Science PhD student studying computational methods for human microbiome research, under the supervision of Professor Elhanan Borenstein at Tel Aviv University. Prior to her PhD, she worked in product management and data analysis positions at Medial EarlySign(a startup in the digital health industry) and at Intel Corporation’s Big Data and Machine Learning group. Efrat completed a B.Sc. and M.Sc. in computer science with honors from Ben-Gurion University. She is generally enthusiastic about any intersection of computer science, machine learning, healthcare and well-being.

Efrat received the EMEA Google Generation Scholarship (2022), Edmond J. Safra Center Excellent Research Student Prize (2022), Naamat scholarship for female PhD students (2022), and prizes for excellence in teaching (Discrete Math course for CS students, 2019-2021).

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October ABC Seminar: Weiruo Zhang, PhD, Stanford University

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

Posted on by Tarissa Arnold Mages

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Efrat Muller, Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

Efrat Muller, Tel Aviv University – “Methods for integrating metagenomics and metabolomics data”

Speaker Name: Efrat Muller
Affiliation: Prof. Elhanan Borenstein Lab, Tel Aviv University
Position: Doctoral Candidate
Hosted by: Jennifer Dawkins, Gerber Lab

The human gut microbiome, and its metabolic activity in particular, have been implicated in a wide range of disease states, including metabolic disorders, inflammatory bowel diseases, and colorectal cancer. This growing appreciation for the impact of the gut microbiome’s metabolism on human health has given rise to studies that generate both microbiome and metabolome high-throughput data from human gut microbiome samples. Truly integrated analysis of both omic datasets, however, remains a challenging task. My research aims to develop new frameworks for analyzing and integrating these datasets using a combination of machine learning, metabolic modeling, and network analysis. I’ll specifically present two projects: the first evaluates the robustness of microbiome-metabolome associations using machine learning and meta-analysis models; the second aims to identify “multi-omic modules” that capture both cross-omic associations and associations with disease simultaneously. Taken together, these frameworks can enhance our understanding of microbiome-metabolome connections and equip the microbiome research community with novel methods for such integrated data analysis.

Links
Google Scholar
Linkedin

Efrat Muller is a Computer Science PhD student studying computational methods for human microbiome research, under the supervision of Professor Elhanan Borenstein at Tel Aviv University.  Prior to her PhD, she worked in product management and data analysis positions at Medial EarlySign(a startup in the digital health industry) and at Intel Corporation’s Big Data and Machine Learning group.  Efrat completed a B.Sc. and M.Sc. in computer science with honors from Ben-Gurion University.  She is generally enthusiastic about any intersection of computer science, machine learning, healthcare and well-being.

Efrat received the EMEA Google Generation Scholarship (2022), Edmond J. Safra Center Excellent Research Student Prize (2022), Naamat scholarship for female PhD students (2022), and prizes for excellence in teaching (Discrete Math course for CS students, 2019-2021).

Click here to be added to our mail list.

Gerber Lab at ICML Workshop on Computational Biology 2023

Gerber Lab at ICML Workshop on Computational Biology 2023

Posted on by Tarissa Arnold Mages

The ICML Workshop on Computational Biology (WCB) highlights how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. It aims to bring together interdisciplinary ML researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.

The Gerber Lab had the following two papers accepted:

Gerber GK, Bhattarai SK, Du M, Glickman MS, Bucci V. Discovery of Host-Microbiome Interactions Using Multi-Modal, Sparse, Time-Aware, Bayesian Network-Structured Neural Topic Models. International Conference on Machine Learning Workshop on Computational Biology, 2023.

Uppal G, Urtecho G, Richardson M, Moody T, Wang HH, Gerber GK. MC-SPACE: Microbial communities from spatially associated counts engine. International Conference on Machine Learning Workshop on Computational Biology, 2023.

 

Nov. 14, 2022: Martin Jankowiak, PhD, Broad Institute

Posted on by Tim Janicki

“Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection,” https://partners.zoom.us/j/86162386947; We develop Bayesian Viral Allele Selection (BVAS), a method that leverages the millions of SARS-CoV-2 viral genomes that have been sequenced across the globe to identify mutations linked to increased viral fitness.