Computational Identification of Metabolites for Pathways Related to Huntingtons Disease Huntingtonu0027s disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. Systems Bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HDrelated pathways, which were previously identified from our previous work on multisource data integration These include: i) creation of pathwaytopathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathwaytopathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.

Integrating Deep Textual Features to Probability Matrix Factorization for Metabolitedisease Association Prediction Metabolic disorders play an important role in the development of many common diseases, including obesity, diabetes and coronary heart disease. Identifying key metabolites associated with disease can help us understand the mechanism of disease better and improve clinical diagnosis. Predicting diseasesrelated metabolites through computational approaches can provide potential biomarkers for further biological experiments. Text annotations on metabolites in existing databases provide prior information, which could provide more information about metabolites. However, current approaches havenu0027t taken this information into consideration. In this work, we proposed a probability matrix factorization method which combined deep textual features to predict metabolitedisease associations. The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases. Then, associations between metabolites and diseases are predicted through the matrix factorization based on these textual features. The main contributions in the work is that our model shows that adding textual features could help to improve the prediction of metabolitedisease associations. Case studies have indicated our model got predictive ability for diseasesrelated metabolites.