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AI for understanding molecular complexity in precision medicine

23/02/2022 11:00 to 11:45
  • Lecture Series Infection & Immunity – Next-generation of multi-omics research:going to the single cell

Speaker

  • Prof. Dr. Natasa
    Przulj

ICREA Research Professor at Barcelona Supercomputing Center and a Full Professor of Biomedical Data Science at University College London (UCL) Computer Science Department


ABSTRACT     

We are faced with a flood of molecular and clinical data. We are measuring interactions between various bio-molecules in and around a cell that form large, complex systems. Patient omics datasets are also increasingly becoming available. These systems-level network data provide heterogeneous, but complementary information about cells, tissues and diseases. The challenge is how to mine them collectively to answer fundamental biological and medical questions. This is nontrivial, because of computational intractability of many underlying problems on networks (also called graphs), necessitating the development of approximate algorithms (heuristic methods) for finding approximate solutions.

We develop artificial intelligence (AI) methods for extracting new biomedical knowledge from the wiring patterns of systems-level, heterogeneous biomedical networks. Our graphlet-based and other methods uncover the patterns in molecular networks and in the multi-scale organization of these networks indicative of biological function, translating the information hidden in the network topology into domain-specific knowledge. We also introduce a versatile data fusion (integration) machine learning (ML) framework to address key challenges in precision medicine from the wiring patterns of biomedical network data: better stratification of patients, prediction of driver genes in cancer, and re-purposing of approved drugs to particular patients and patient groups, including Covid-19 patients. Our new methods stem from novel network science algorithms coupled with graph-regularized non-negative matrix tri-factorization (NMTF), a machine learning technique for dimensionality reduction, inference and co-clustering of heterogeneous datasets. We utilize our new framework to develop methodologies for performing other related tasks, including disease re-classification from modern, heterogeneous molecular omic data, inferring new Gene Ontology relationships, aligning multiple molecular networks, and uncovering new cancer mechanisms.


RESPONSIBLE SCIENTISTS
Gunnar
Dittmar
Alexander
Skupin

ABOUT THE LECTURE & WORKSHOP SERIES – INFECTION & IMMUNITY – NEXT-GENERATION OF MULTI-OMICS RESEARCH: GOING TO THE SINGLE CELL

The LIH lecture and workshops series in Infection & Immunity (Next-generation of multi-omics research:going to the single cell), supported by the FNR and jointly organized with the University of Luxembourg-LCSB, are gathering internationally recognised speakers to address these research topics during 2021-2022.

Attendance to the lecture is free of charge.

SHEDULE

11.00 – 11.45 am – LECTURE

WEBEX OR IN PERSON

Please note that the lecture will take place in a hybrid format and you may register for in-person attendance by sending an email to Florence Henry or join virtually via Webex.

WEBINAR VIA WEBEX

Event number: 2734 893 4419
Event password: EGs5NTpMp72

IN PERSON

University of Luxembourg
Maison des Sciences Humaines
Campus Belval
Black Box
11 Porte des Sciences
L-4366 Esch-sur-Alzette

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