Moiz Khan Sherwani
University of Copenhagen, Post Doc Researcher
Copenhagen, Denmark
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I did my Doctorate in the field computer vision for medical images. I have been working with images and textual data within medical domain. Currently, I have been involved in several projects related to bioinformatics, ML and multi-omics in veterinary science and evolutionary hologenomics. Apart from the main projects, I am working with other computer vision projects related to medical, infrastructure and other smaller projects. My future projects will include multi-modal models within health science.
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Topics
Unraveling Graphs to Decode Biomedical Complexity
Modern biomedical data is inherently interconnected, genes interact with proteins, diseases share pathways and patients relate through phenotypic and molecular similarity. Traditional deep learning models often fail to capture this complex relational structure. This session explores how Graph Neural Networks (GNNs) are transforming biomedical discovery by enabling us to model these intricate connections directly.
The talk will walk through real-world examples of how graph learning can be applied to biological networks and patient stratification. It will also compare GNN-based approaches to conventional deep learning, highlighting where graph-based reasoning provides unique advantages in interpretability and data efficiency. Attendees will gain practical insights into structuring biomedical data as graphs, selecting the right GNN architectures, and evaluating model trustworthiness. By the end, participants will understand how GNNs can help uncover hidden biological relationships that traditional methods often overlook.
Machine Learning and Multi-Omics
Our application for Data Maker Fest explores the cutting-edge intersection of machine learning and multi-omics. Using advanced algorithms, we are deciphering complex biological data across multiple omics layers. Through multi-omics, our approach reveals hidden patterns, enabling breakthrough insights into human health and disease mechanisms. Our mission is to revolutionize the field of biotechnology through data-driven discovery. I will also present the challenges of data collection and processing before using it in ML algorithms.
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