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The adipokine adiponectin is very concentrated in plasma, and decreased levels of adiponectin are associated with pathological conditions such as obesity, diabetes, cardiovascular diseases, and metabolic syndrome. When produced in its full-length form, adiponectin self-associates to generate multimeric complexes. The full-length form of adiponectin can be cleaved by the globular form of elastase that is produced locally, and the resulting biological effects are exerted in a paracrine or autocrine manner. The different forms of adiponectin bind to specific receptors consisting of two G-protein-independent, seven-transmembrane-spanning receptors, called AdipoR1 and AdipoR2, while T-cadherin has been identified as a potential receptor for high molecular weight complexes of adiponectin. Adiponectin exerts a key role in cellular metabolism, regulating glucose levels as well as fatty acid breakdown. However, its biological effects are heterogeneous, involving multiple target tissues. The Special Issue “Mechanisms of Adiponectin Action” highlights the pleiotropic role of this hormone through 3 research articles and 7 reviews. These papers focus on the recent knowledge regarding adiponectin in different target tissues, both in healthy and in diseased conditions.

adiponectin --- atherosclerosis --- cholesterol efflux --- diabetes --- inflammation --- endometrium --- implantation --- transcriptome --- microarray --- adiponectin --- pig --- fertility --- adipose tissue --- reproductive tract --- adipokines --- cell signaling --- skeletal muscle --- regeneration --- adiponectin isoforms --- exercise --- training --- adiponectin --- AMPK --- BIAcore --- extracellular signal-regulated kinase (ERK) --- matricellular proteins --- neuritogenesis --- NGF? --- PC12 cells --- Secreted protein --- acidic and rich in cysteine (SPARC) --- adiponectin --- muscle --- myopathies --- adiponectin --- metabolism --- AdipoRon --- lipotoxicity --- adiponectin --- adiponectin inducer --- kojyl cinnamate ester derivative --- adipogenesis --- hair growth-related factor --- human follicular dermal papilla cell --- obesity --- adipokines --- adiponectin --- breast cancer --- ovarian cancer --- endometrial cancer --- cervix cancer --- estrogen receptor --- adiponectin --- obesity --- cancer --- adiponectin --- adipose tissue --- obesity --- endocrine cancer --- n/a

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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.

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