SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology

arXiv:2405.08226v1 Announce Type: new
Abstract: Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling effective diagnosis, treatment, and prevention strategies. However, predicting patient outcomes through integration of all available multi-omics data is an under-study research direction. Here, we present SeNMo (Self-normalizing Network for Multi-omics), a deep neural network trained on multi-omics data across 33 cancer types. SeNMo is efficient in …

abstract analysis arxiv cancer cs.lg data data analysis deep learning diagnosis enabling however integration oncology patient prevention research strategies through treatment type understanding