Anca-Cristina Perpelea, Ruxandra Sfeatcu, Mihail Tușaliu, Mihaela Tănase, Marina Meleșcanu Imre, Alexandra Ripszky Totan, Cristian Funieru, Dragoș Nicolae Nicolescu, Silviu-Mirel Pițuru
Exploring the Threefold Viewpoint on Children's Oral Health in a Cross-Sectional Study
Healthcare (Basel, Switzerland)
deep learningbased multiomics data integration methods capability reveal mechanisms cancer development discover cancer biomarkers identify pathogenic targets however current methods ignore potential correlations samples integrating multiomics data addition providing accurate biological explanations still poses significant challenges due complexity deep learning models therefore urgent need deep learningbased multiomics integration method explore potential correlations samples provide model interpretability herein propose novel interpretable multiomics data integration method deepkegg cancer recurrence prediction biomarker discovery deepkegg biological hierarchical module designed local connections neuron nodes model interpretability based biological relationship genesmirnas pathways addition pathway selfattention module constructed explore correlation different samples generate potential pathway feature representation enhancing prediction performance model lastly attributionbased feature importance calculation method utilized discover biomarkers related cancer recurrence provide biological interpretation model experimental results demonstrate deepkegg outperforms stateoftheart methods 5fold cross validation furthermore case studies also indicate deepkegg serves effective tool biomarker discovery code available httpsgithubcomlanbiolabdeepkegg