Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50
Abstract Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the…
Computational models
Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data
Abstract The accurate prediction of patients with complex diseases, such as Alzheimer’s disease (AD), as well as disease stages, including…
Two distinct binding modes provide the RNA-binding protein RbFox with extraordinary sequence specificity
Abstract Specificity of RNA-binding proteins for target sequences varies considerably. Yet, it is not understood how certain few proteins achieve…
Evaluating cancer etiology and risk with a mathematical model of tumor evolution
Abstract Recent evidence arising from DNA sequencing of healthy human tissues has clearly indicated that our organs accumulate a relevant…
Charting differentially methylated regions in cancer with Rocker-meth
Abstract Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new…
A stacking ensemble deep learning approach to cancer type classification based on TCGA data
Abstract Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid,…
Long-term cancer survival prediction using multimodal deep learning
Abstract The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal…
Wx: a neural network-based feature selection algorithm for transcriptomic data
Abstract Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study…