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…
Abstract The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal…
Abstract Patient-derived cell lines are often used in pre-clinical cancer research, but some cell lines are too different from tumors…
Abstract Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment,…
Abstract Many proteins contain intrinsically disordered regions (IDRs) which carry out important functions without relying on a single well-defined conformation.…
Abstract The goal of the National Cancer Institute’s (NCI’s) Genomic Data Commons (GDC) is to provide the cancer research community…
Abstract Selecting appropriate cell lines to represent a disease is crucial for the success of biomedical research, because the usage…
Abstract One of the ways in which genes can become activated in tumors is by somatic structural genomic rearrangements leading…
Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events…
Abstract The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome…
Abstract A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between…