Deep Learning

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
Integrative analysis of multi-omic datasets remains a challenge due to gaps and heterogeneity. We present a bespoke unsupervised deep learning model that generates synthetic multi-omic data for 1,523 cancer cell lines, completing the gaps and increasing the number of molecular and phenotypic profiles by 32.
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
Transformer-based deep learning integrates multi-omic data with cancer pathways
Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types.
Transformer-based deep learning integrates multi-omic data with cancer pathways
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has …
Zhaoxiang (Simon) Cai, Emanuel Gonçalves, Rebecca C Poulos, Syd Barthorpe, Srikanth S Manda, Natasha Lucas, Alexandra Beck, Daniel Bucio-Noble, Michael Dausmann, Caitlin Hall, Michael Hecker, Jennifer Koh, Sadia Mahboob, Iman Mali, James Morris, Laura Richardson, Akila J Seneviratne, Erin Sykes, Frances Thomas, Sara Valentini, Steven G Williams, Yangxiu Wu, Dylan Xavier, Karen L MacKenzie, Peter G Hains, Brett Tully, Phillip J Robinson, Qing Zhong, Mathew J Garnett, Roger R Reddel
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Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Introduction Proteomic data provide unique insights into the molecular behaviour of cells in both healthy and disease contexts. …
Jun 6, 2022 12:00 AM — Jun 9, 2022 12:00 AM Minneapolis Convention Center, Minneapolis, Minnesota, USA
Zhaoxiang Cai, Emanuel Gonçalves, Rebecca C Poulos, Syd Barthorpe, Srikanth S Manda, Natasha Lucas, Alexandra Beck, Daniel Bucio-Noble, Michael Dausmann, Caitlin Hall, Michael Hecker, Jennifer Koh, Sadia Mahboob, Iman Mali, James Morris, Laura Richardson, Akila J Seneviratne, Erin Sykes, Frances Thomas, Sara Valentini, Steven G Williams, Yangxiu Wu, Dylan Xavier, Karen L MacKenzie, Peter G Hains, Brett Tully, Phillip J Robinson, Qing Zhong, Mathew J Garnett, Roger R Reddel
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Proteomic data can reveal novel associations between genotype and phenotype, beyond what is apparent from genomics or transcriptomics alone. However, a lack of large proteomic datasets across a range of cancer types has limited our understanding of proteome network organisation and regulation.
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Integrating multi-omics data with biological knowledge by Transformer-based deep learning
Omics data analysis, powered by machine learning, has significantly improved cancer diagnosis and prognosis. However, most machine …
Nov 25, 2021 12:00 AM — 12:00 AM Virtual/Australia
Zhaoxiang Cai, Rebecca C Poulos, Qing Zhong
A pan-cancer proteomic map of 949 human cell lines
Omics data analysis, powered by machine learning, has significantly improved cancer diagnosis and prognosis. However, most machine …
Nov 23, 2021 12:00 AM — 12:00 AM Virtual/Australia
Rebecca C Poulos, Emanuel Gonçalves, Zhaoxiang Cai, Syd Barthorpe, Srikanth S Manda, Natasha Lucas, Alexandra Beck, Daniel Bucio-Noble, Michael Dausmann, Caitlin Hall, Michael Hecker, Jennifer Koh, Sadia Mahboob, Iman Mali, James Morris, Laura Richardson, Akila J Seneviratne, Erin Sykes, Frances Thomas, Sara Valentini, Steven G Williams, Yangxiu Wu, Dylan Xavier, Karen L MacKenzie, Peter G Hains, Brett Tully, Phillip J Robinson, Qing Zhong, Mathew J Garnett, Roger R Reddel
Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Proteomic data can reveal novel associations between genotype and phenotype, beyond what is apparent from genomics or transcriptomics …
Sep 13, 2021 12:00 AM — 12:00 AM Virtual/Australia
Zhaoxiang Cai, Rebecca C Poulos, Qing Zhong
Vaccine misinformation identification (1st prize winner)
The spread of misinformation can undermine confidence in vaccination. The capacity for social media to quickly and effectively spread information or misinformation is a pressing question for governments and global agencies.
Ocular Disease Intelligent Recognition (3rd prize winner)
In ophthalmology, fundus screening is an economic and effective way to prevent blindness as early as possible that cased by diabetes, glaucoma, cataract, age-related macular degeneration (AMD) and many other causes.