AIMC Journal:
Bioinformatics advances

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As many readmissions are considered preventable, predictive modeling offers a valuable tool for early i...

SUMMARY: The application of machine learning methods to biomedical applications has seen many successes. However, working with transcriptomic data on supervised learning tasks is challenging due to its high dimensionality, low patient numbers, and cl...

MOTIVATION: We recently introduced RNA-knowledge graph (KG), an ontology-based KG that integrates biological data on RNAs from over 60 public databases. RNA-KG captures functional relationships and interactions between RNA molecules and other biomole...

MOTIVATION: Contextual integration of multiomic datasets from the same patient could improve the accuracy of subtype prediction algorithms to help with better prognosis and management of breast cancer. Previous machine learning models have underexplo...

MOTIVATION: Cells are dynamic, continually responding to intra- and extracellular signals. Measuring the response to these signals in individual cells is challenging. Signal transduction is fast, but reporters for downstream gene expression are slow:...

MOTIVATION: The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) is a rare but significant challenge in antiretroviral therapy (ART). MDR, which may arise from prolonged drug exposure, treatment failures, or transmi...