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Skin inflammation is associated with a wide range of conditions which represent major health issues worldwide. Skin and mucosal surfaces represent the primary interface between the human body and the environment, susceptible to numerous factors whose action results in diseases produced by chemical substances, mechanical trauma, microbial agents, radiation, etc. Inflammation, a complex network of interactions between soluble molecules and cells, represents the main modality of the skin’s response to injuries. Numerous studies have revealed close links between chronic inflammation, oxidative stress, and carcinogenesis. Chronic inflammation induces the activation of various cell types and an increase in the production of reactive oxygen species, promoting the initiation of a malignant process. Identifying specific biomarkers is essential for understanding molecular mechanisms and developing therapies appropriate to the patient’s characteristics.Personalized medicine is an emerging field of medicine that has the potential to predict which therapy will be safe and efficacious for specific patients using an individual’s genetic profile to guide decisions regarding the diagnosis, treatment, as well as prevention of disease. This book gathers articles that present recent advancements in research involving the mechanisms that underlie the development of inflammatory skin disorders, skin and mucosal inflammation in general.

sea-buckthorn seed oil --- long-chain fatty acids --- skin dysplastic keratinocytes --- UVA --- CD36 --- SR-B2 --- atopic dermatitis --- eczema --- JAK inhibitors --- systematic review --- meta-analysis --- evidence-based medicine --- immune-mediated skin diseases --- target therapy --- skin conditions and systemic inflammatory diseases --- systemic sclerosis --- biomarker --- calumenin --- S100A6 --- cytohesin 2 --- cannabinoids --- inflammation --- gut-lung-skin barrier --- signaling pathways --- inflammatory biomarkers --- psoriasis vulgaris --- prevalence --- comorbidities --- risk factors --- systemic lupus erythematous --- lupus nephritis --- lipid peroxidation --- DNA oxidation --- oxidized proteins --- carbohydrate oxidation --- antioxidative stress strategies --- biomarkers --- IgY --- psoriatic dermatitis --- imiquimod --- C57 BL/6 mice --- alginate --- biomaterial --- dressing --- fibers --- hydrogel --- nanofibers --- commercially available --- wound care --- wound healing --- cutaneous melanoma --- epigenetic regulation --- drug resistance --- therapeutic targets --- epigenetic therapy --- immune response --- Sutton nevi --- halo nevi --- skin tumor --- psoriasis --- proteome analysis --- estrogen --- menopause --- cSCC --- AK --- sialylation --- sialyltransferase --- sialidase --- COVID-19 --- pyoderma gangrenosum --- immunosuppression --- cyclosporine --- corticosteroids --- autoinflammatory disease --- androgenic alopecia --- clinical trial --- hair density --- platelet-rich plasma --- 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.

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