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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

cancer treatment --- extreme learning --- independent prognostic power --- AID/APOBEC --- HP --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- artificial intelligence --- epigenetics --- comorbidity score --- denoising autoencoders --- protein --- single-biomarkers --- gene signature extraction --- high-throughput analysis --- concatenated deep feature --- feature selection --- differential gene expression analysis --- colorectal cancer --- ovarian cancer --- multiple-biomarkers --- gefitinib --- cancer biomarkers --- classification --- cancer biomarker --- mutation --- hierarchical clustering analysis --- HNSCC --- cell-free DNA --- network analysis --- drug resistance --- hTERT --- variable selection --- KRAS mutation --- single-cell sequencing --- network target --- skin cutaneous melanoma --- telomeres --- Neoantigen Prediction --- datasets --- clinical/environmental factors --- StAR --- PD-L1 --- miRNA --- circulating tumor DNA (ctDNA) --- false discovery rate --- predictive model --- Computational Immunology --- brain metastases --- observed survival interval --- next generation sequencing --- brain --- machine learning --- cancer prognosis --- copy number aberration --- mutable motif --- steroidogenic enzymes --- tumor --- mortality --- tumor microenvironment --- somatic mutation --- transcriptional signatures --- omics profiles --- mitochondrial metabolism --- Bufadienolide-like chemicals --- cancer-related pathways --- intratumor heterogeneity --- estrogen --- locoregionally advanced --- RNA --- feature extraction and interpretation --- treatment de-escalation --- activation induced deaminase --- knockoffs --- R package --- copy number variation --- gene loss biomarkers --- cancer CRISPR --- overall survival --- histopathological imaging --- self-organizing map --- Network Analysis --- oral cancer --- biostatistics --- firehose --- Bioinformatics tool --- alternative splicing --- biomarkers --- diseases genes --- histopathological imaging features --- imaging --- TCGA --- decision support systems --- The Cancer Genome Atlas --- molecular subtypes --- molecular mechanism --- omics --- curative surgery --- network pharmacology --- methylation --- bioinformatics --- neurological disorders --- precision medicine --- cancer modeling --- miRNAs --- breast cancer detection --- functional analysis --- biomarker signature --- anti-cancer --- hormone sensitive cancers --- deep learning --- DNA sequence profile --- pancreatic cancer --- telomerase --- Monte Carlo --- mixture of normal distributions --- survival analysis --- tumor infiltrating lymphocytes --- curation --- pathophysiology --- GEO DataSets --- head and neck cancer --- gene expression analysis --- erlotinib --- meta-analysis --- traditional Chinese medicine --- breast cancer --- TCGA mining --- breast cancer prognosis --- microarray --- DNA --- interaction --- health strengthening herb --- cancer --- genomic instability