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Inferring functional networks from rule-based machine learning models

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Host: Dr Shan He


Abstract: Currently, there is a wealth of biotechnologies (e.g. sequencing, proteomics, lipidomics) able to generate a broad range of data types out of biological samples. However, the knowledge gained from such data sources is constrained by the limitations of the analytics techniques. The state-of-the-art machine learning algorithms are able to capture complex patterns with high prediction capacity. However, often it is very difficult if not impossible to extract human-understandable knowledge out of these patterns. In recent years evolutionary machine learning techniques have shown that they are competent methods for biological/biomedical data analytics. They are able to generate interpretable prediction models and, beyond just prediction models, they are able to extract useful knowledge in the form of biomarkers or biological networks.

This talk presents my work in recent years around the FuNeL method (BioData Mining 2016, 9:28) for the inference of functional networks from rule-based machine learning models. FuNeL applies what we call the co-prediction principle of network inference, in which network elements (e.g. genes) are connected only if, within the machine learning models, they act together to make predictions. FuNeL has been thoroughly evaluated on cancer gene expression datasets and shown its complementarity to classic gene co-expression network inference methods, and its capacity to identify networks in which known disease-related genes are key players. Finally, I will cover our more recent experiments in which we thoroughly characterise the impact that the core knowledge representation for rules has in the process of extracting biologically-useful knowledge out of the data datasets.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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