2:32 p.m.
Abstract
Metabolism is central to all processes of life and the metabolome -- large-scale
measurement of the quantities of small molecular entities in cells and tissues -- gives a
readout of cellular functioning at a point in time. Harnessing metabolomic information
together with transcriptomic information about gene expression allows for multi-level
insights into genetic dysregulation and its cellular effects. I will describe a
multi-omics approach based on genome-scale modelling that is able to integrate the two
levels and provide insights into the systems-level deregulation of cellular function due
to ageing by transforming the cellular reaction space into a constraint-based linear
optimisation problem. Metabolic models such as these and their interpretation depends on
publicly available data about small molecular metabolites. Chemical ontologies provide
structured classifications of chemical entities that can be used for navigation and
filtering of chemical space. ChEBI is a prominent example of a chemical ontology, widely
used in life science contexts including to annotate metabolites in genome-scale models.
However, ChEBI is manually maintained and as such does not scale to the full range of
metabolites in all organisms. There is a need for tools that are able to automatically
classify chemical data into chemical ontologies, which can be framed as a hierarchical
multi-class classification problem, based on chemical structures, which are represented as
connected graphs of atoms and bonds. I will discuss recent efforts to evaluate machine
learning approaches for this task, comparing different learning frameworks including
logistic regression, decision trees and LSTMs, and different encoding approaches for the
chemical structures, including cheminformatics 'fingerprints' (feature vectors)
and character-based encodings from chemical line notation structural representations.
*Speaker:
Janna Hastings is a computer scientist who applies semantic technologies to address
challenges in scientific data management and discovery. She is currently a post-doctoral
researcher at the Department of Theoretical Computer Science, Otto-von-Guericke University
Magdeburg, Germany, and an expert consultant to the Center for Behaviour Change,
University College London. She holds a PhD in Computational Biology, an MSc in Computer
Science and an MA in Philosophy.
For a full list of publications, see Google Scholar:
https://scholar.google.com/citations?hl=en&user=cz-hhPUAAAAJ.
*Date:
Thursday, October 29, 2020, at 11:30
*Zoom link:
https://supsi.zoom.us/j/98358510987?pwd=Z2hLRTY4TytweDRCcHB4MkFFaTIzdz09
ID meeting: 983 5851 0987
Passcode: 183530