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Advancing Biological Modeling and Di...

Tan, Jimin,

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Advancing Biological Modeling and Discovery With Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Biological Modeling and Discovery With Machine Learning // Jimin Tan.
Author:	Tan, Jimin,
Description:	1 electronic resource (183 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
Contained By:	Dissertations Abstracts International86-09B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31564400
ISBN:	9798304997164

Advancing Biological Modeling and Discovery With Machine Learning /
Tan, Jimin,

Advancing Biological Modeling and Discovery With Machine Learning /Jimin Tan. - 1 electronic resource (183 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: B.

Understanding the mechanisms underlying biological systems has enabled us to engineer interventions and even reverse certain biological processes. Learning and modeling biological systems remain challenging due to their complexity. Machine learning, particularly deep neural networks, has emerged as a powerful data-driven approach for modeling complex systems and has been successful in various areas. However, its efficacy is constrained by data and computational limitations, necessitating the development of domain-specific and efficient models for biological research. In this thesis, we use machine learning to explore two distinct areas of biology: 3D chromatin architecture and tumor microenvironments. We present two novel approaches that demonstrate the potential of machine learning in advancing biological discovery.First, we introduce C.Origami, a multimodal deep neural network that predicts cell-type-specific chromatin organization using DNA sequence and genomic features. This tool enables in silico experiments to examine the impact of genetic changes on chromatin interactions and facilitates the discovery of novel chromatin regulation circuits in disease-related cells.Second, we propose CANVAS, a self-supervised representation learning framework for analyzing the tumor microenvironment using high-dimensional multiplexed imaging data. CANVAS employs a vision transformer architecture and masked image modeling training objective to capture subtle morphological differences and characterize distinct tumor microenvironment signatures without supervision.These approaches demonstrate the power of tailored machine learning models both in advancing our understanding of complex biological systems and as engines driving new discoveries.

English

ISBN: 9798304997164Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Computational biology

Advancing Biological Modeling and Discovery With Machine Learning /
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500 $a Advisors: Tsirigos, Aristotelis; Fenyo, David Committee members: Boeke, Jef D.; Cho, Kyunghyun; Moreira, Andre L.; Maurano, Matthew T.
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