Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood. Despite recent advances in deep-learning genomic approaches, accurately predicting and prioritizing clinically relevant mutations in gene regulatory regions remains a major challenge. Here we introduce Deep VRegulome, a deep-learning method for prediction and interpretation of functionally disruptive variants in the human regulome, which combines 700 DNABERT fine-tuned models, trained on vast amounts of ENCODE gene regulatory regions, with variant scoring, motif analysis, attention-based visualization, and survival analysis. We showcase its application on TCGA glioblastoma WGS dataset in prioritizing survival-associated mutations and regulatory regions. The analysis identified 572 splice-disrupting and 9,837 transcription-factor binding site altering mutations occurring in greater than 10% of glioblastoma samples. Survival analysis linked 1352 mutations and 563 disrupted regulatory regions to patient outcomes, enabling stratification via non-coding mutation signatures. All the code, fine-tuned models, and an interactive data portal are publicly available.

However, their performance usually suffers from the overfitting problem seriously.

There are roughly two different approaches in the literature for knowledge graph completion (KGC), namely distance based (DB) models and tensor factorization based (TFB) models. Although both approaches have their own advantages and disadvantages over each other, TFB models cannot attain state-of-the-art performance due to overfitting problem, and therefore various regularizers are employed for TFB models. In the paper, authors propose a regularizer for TFB models, namely Duality-induced Regularization (DURA), which is inspired by the score functions of the DB models. They come up with a dual problem which involves a distance based KGC model, and show that when the aforementioned regularizer is employed for the primal problem (i.e. TFB model), both problems become equivalent.