Computer scientists developing method to identify disease biomarkers with high accuracy

Thursday, October 28, 2021

Researchers at the Cheriton School of Computer Science are incorporating a deep learning network into a more accurate method to identify disease biomarkers. The new method achieves up to 98 per cent detection of peptide features in a dataset. That means scientists and medical practitioners have a greater chance of discovering possible diseases through tissue sample analysis.

Multiple techniques can detect diseases by analyzing the protein structure of bio-samples. Computer programs increasingly play a part in this process by examining the large amount of data produced in such tests to pinpoint specific markers of disease.

“But existing programs are often inaccurate or can be limited by human error in their underlying functions,” said Fatema Tuz Zohora, a PhD candidate supervised by University Professor Ming Li. “What we’ve done in our research is to create a deep neural network that achieves 98 per cent detection of peptide features in a dataset. We’re working to make disease detection more accurate to provide healthcare practitioners with the best tools.”

photo of Fatema Tuz Zohora

Fatema Tuz Zohora is a PhD student working in the Bioinformatics research group, under the supervision of Ming Li, University Professor at the Cheriton School of Computer Science and the Canada Research Chair in Bioinformatics.

Her research area involves the application of deep neural networks and other machine learning algorithms in proteomics data, as well as different clinical data, to develop models for disease biomarker discovery. She is also an Assistant Professor (on study leave) in the Department of Computer Science and Engineering at the Bangladesh University of Engineering and Technology, one of the leading universities in Bangladesh for studying computer science.

Peptides are the chains of amino acids that make up proteins in human tissue. It is these small chains that often display the specific markers of disease. Having better testing means it will be possible to detect diseases earlier and with greater accuracy.

Fatema’s team calls their new deep learning network PointIso. It is a form of machine learning that was trained on an enormous database of existing sequences from bio-samples.

“Other methods for disease biomarker detections usually have lots of parameters, which have to be manually set by field experts,” Fatema said. “But our deep neural network learns the parameters itself, which is more accurate, and makes the disease biomarker discovery approach automated.”

The new program is also unique in that it is not trained to only look for one kind of disease, but also to identify the biomarkers associated with a range of diseases, including heart disease, cancer and even COVID-19.

“It’s applicable for any kind of disease biomarker discovery,” Fatema continued. “And because it is essentially a pattern-recognition model, it can be used for detection of any small objects within a large amount of data. There are so many applications for medicine and science, it’s exciting to see the possibilities opening up through this research and how it can help people.”

To learn more about this research, please see Fatema Tuz Zohora, M. Ziaur Rahman, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li. Deep neural network for detecting arbitrary precision peptide features through attention based segmentation. Scientific Reports 11, 18249 (2021).

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