Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling

Abstract Variation is a key concept in every biological aspect, particularly in medical genetics. In this field, each genetic variant is evaluated mostly as an independent entity in respect of its clinical importance. This approach may be sufficient to detect the pathogenic variants in single-gene disorders. However, in most of the complex diseases, the combination of the variants in specific gene networks, rather than the presence of a certain single variant, predominates. Therefore, in case of a complex disease, the disease status can be evaluated by considering it as the success level of a team composed of certain variants. To assess the feasibility of this approach, we tested the effectiveness of high-dimensional modelling of gene network-restricted variants in distinguishing a disease status. To evaluate the proposed method, we selected two gene networks, mTOR and TGF-β. For each pathway, we generated 400 control and 400 patient group samples. The considered mTOR and TGF-β pathways contain 31 and 93 genes of varying sizes, respectively. We generated Chaos Game Representation images for each gene sequence to obtain 2-D binary patterns. Produced patterns were arranged in succession, and a 3-D tensor structure was achieved for each gene network. Features for each data sample were acquired by exploiting Enhanced Multivariance Products Representation to 3-D data. The features were split as training and testing vectors. The training vectors were employed to train a Support Vector Machines classification model. We managed to achieve more than 96% and 99% classification accuracies for mTOR and TGF-β networks, respectively, using a limited amount of training samples.