ORCID Profile
0000-0003-3809-0817
Current Organisation
University of Sydney
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Publisher: Elsevier BV
Date: 02-2022
Publisher: American Chemical Society (ACS)
Date: 27-10-2014
DOI: 10.1021/AC503300Y
Abstract: This study introduced a barcode-like design into a paper-based blood typing device by integrating with smartphone-based technology. The concept of presenting a paper-based blood typing assay in a barcode-like pattern significantly enhanced the adaptability of the assay to the smartphone technology. The fabrication of this device involved the use of a printing technique to define hydrophilic bar channels which were, respectively, treated with Anti-A, -B, and -D antibodies. These channels were then used to perform blood typing assays by introducing a blood s le. Blood type can be visually identified from eluting lengths in bar channels. A smartphone-based analytical application was designed to read the bar channels, analogous to scanning a barcode, interpret this information, and then report results to users. The proposed paper-based blood typing device is rapidly read by smartphones and easy for the user to operate. We envisage that the adaptation of paper-based devices to the widely accepted smartphone technology will increase the capability of paper-based diagnostics with rapid assay result interpretation, data storage, and transmission.
Publisher: Wiley
Date: 22-09-2022
Abstract: Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large‐scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large‐scale proteomic data with drug‐related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data‐independent acquisition mass spectrometry (DIA‐MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high‐performance computing. Finally, we explore the value of pre‐clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.
Publisher: Cold Spring Harbor Laboratory
Date: 03-2022
DOI: 10.1101/2022.02.26.482008
Abstract: The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downs ling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at cellmodelpassports.sanger.ac.uk , is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.
Publisher: Cold Spring Harbor Laboratory
Date: 31-10-2022
DOI: 10.1101/2022.10.27.514141
Abstract: 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. We develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE and TCGA, we show that DeePathNet outperforms traditional methods for the prediction of drug response and classification of cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximising the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at github.com/CMRI-ProCan/DeePathNet .
Publisher: Elsevier BV
Date: 08-2022
No related grants have been discovered for Zhaoxiang Cai.