Machine Learning Methods for High Throughput Biological Data
Machine learning is becoming a pivotal tool in the analysis of datasets generated from high-throughput biological omics experiments. However, omics data introduces distinctive algorithmic challenges that set it apart from other domains where machine learning is applied. These challenges encompass is...
Main Author: | Murphy, Michael A. |
---|---|
Other Authors: | Fraenkel, Ernest |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2024
|
Online Access: | https://hdl.handle.net/1721.1/154024 https://orcid.org/0000-0002-7343-8383 |
Similar Items
-
High‐throughput sequencing for biology and medicine
by: Wendy Weijia Soon, et al.
Published: (2013-01-01) -
Machine learning augmented high throughput formulations
by: Shi, Shi Jun
Published: (2023) -
High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
by: Lu Zhichao, et al.
Published: (2024-05-01) -
Structural genomics and high throughput structural biology /
by: Sundstrom, Michael, et al.
Published: (2006) -
Biomedical Informatics and Computational Biology for High-Throughput Data Analysis
by: Bairong Shen, et al.
Published: (2014-01-01)