Predicting Variant Pathogenicity with Machine Learning

There are roughly 22,000 protein-coding genes in the human body, many of which play important roles in biological functions. The proteins fold in 3D space, and this is most often necessary for function. A genetic variant can disrupt the secondary structure of a protein (one aspect of structure) or eliminate a site important in protein-protein interaction or post-translational modification. The loss of function or deregulation can result in disease. Thus, there is great biomedical interest in identifying disease-causing single-nucleotide variants. We hypothesize that we can accurately predict variant pathogenicity. We used machine learning to predict the pathogenicity of a set of 28,369 single-nucleotide variants across 10 genes. The data are acquired from publicly available saturation mutagenesis data sets, which generate every possible amino acid substitution at every position in a protein. Our approach employs a support vector machine using linear, polynomial, and RBF kernel functions. The problem is implemented as a binary classification problem, where a label of 1 indicates a disease-causing variant and a label of 0 indicates a benign variant. The model predicts pathogenicity based on amino acid, post-translational modification, and secondary structure information. We cleaned and analyzed the data with custom Python scripts. Our results show average balanced accuracy scores for classifying pathogenicity of approximately 57.9%, 60.3%, and 60.3% for the linear, polynomial, and RBF kernels, respectively. Therefore, the model is an improvement over random guessing but has room for improvement.https://digitalscholarship.unlv.edu/durep_posters/1045/thumbnail.jp

The Human Affectome

Over the last decades, the interdisciplinary field of the affective sciences has seen proliferation rather than integration of theoretical perspectives. This is due to differences in metaphysical and mechanistic assumptions about human affective phenomena (what they are and how they work) which, shaped by academic motivations and values, have determined the affective constructs and operationalizations. An assumption on the purpose of affective phenomenacan be used as a teleological principle to guide the construction of a common set of metaphysical and mechanistic assumptions—a framework for human affective research. In this capstone paper for the special issue “Towards an Integrated Understanding of the Human Affectome”, we gather the tiered purpose of human affective phenomena to synthesize assumptions that account for human affective phenomenacollectively. This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research