Methodological breakthroughs over the past four decades have repeatedly revolutionized
transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to …

Extensive efforts have been directed at the discovery, investigation and clinical monitoring of
targeted therapeutics. These efforts may be facilitated by the convenient access of the …

Tumor-infiltrating immune cells are highly relevant for prognosis and identification of
immunotherapy targets in hepatocellular carcinoma (HCC). The recently developed …

Background Machine learning is now a standard tool for cancer prediction based on gene
expression data. However, deep learning is still new for this task, and there is no clear …

Objective: Secreted modular calcium-binding proteins (SMOCs) are extracellular
glycoproteins of the secreted protein, acidic, and rich in cysteine-related modular calcium …

Background Understanding the biological differences between sexes in cancer is essential
for personalized treatment and prevention. We hypothesized that the extreme …

Background The tumor microenvironment (TME) is increasingly appreciated as an important
determinant of cancer outcome, including in multiple myeloma (MM). However, most …

Data discrepancy between preclinical and clinical datasets poses a major challenge for
accurate drug response prediction based on gene expression data. Different methods of …

Background With the rapid advancement of genomic sequencing techniques, massive
production of gene expression data is becoming possible, which prompts the development …

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 …