BACKGROUND: Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as ...
The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages ...
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present Methyla...
BACKGROUND: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural n...
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cance...
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients a...
BACKGROUND: The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as...
Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply...
We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. Differential methylation among tumor entities relates to differences in ...