Understanding information at the biomolecular level using statistics and machine learning
type: CreativeWork
description: Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
abstract: Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
author: Luu, Alan M.
language: en
language: eng
keywords: biology, statistics, machine learning, genomics, Central Dogma, DNA, RNA, protein, information, genome editing, CRISPR, next-generation sequencing, deep learning, genetic engineering, keratinocyte, cancer, immune system, CRISPR base editor, squamous cell carcinoma, basal cell carcinoma, RNA-Seq, scRNA-Seq, convolutional neural network, deep metric learning, multimodal learning, T-Cell receptors, neural network interpretation, epitope, MCMC
date published: 2022-05
date modified: 2026-07-22T22:24:54Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2142/115452