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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

single cell RNA sequencing --- bioinformatics --- precision medicine --- cell cluster --- trajectory analyses

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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.

Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection

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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.

Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection

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The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions.

Medicine --- Pharmaceutical industries --- COVID-19 --- drug repurposing --- topological data analysis --- persistent Betti function --- SARS-CoV-2 --- network-based pharmacology --- combination therapy --- nucleoside GS-441524 --- fluoxetine --- synergy --- antidepressant --- natural compounds --- QSAR --- molecular docking --- drug repositioning --- UK Biobank --- vaccine --- LC-2/ad cell line --- drug discovery --- docking --- MM-GBSA calculation --- molecular dynamics --- cytotoxicity assay --- GWAS --- multiple sclerosis --- oxidative stress --- repurposing --- ADME-Tox --- bioinformatics --- complex network analysis --- modularity clustering --- ATC code --- hidradenitis suppurativa --- acne inversa --- transcriptome --- proteome --- comorbid disorder --- biomarker --- signaling pathway --- druggable gene --- drug-repositioning --- MEK inhibitor --- MM/GBSA --- Glide docking --- MD simulation --- MM/PBSA --- single-cell RNA sequencing --- pulmonary fibrosis --- biological networks --- p38α MAPK --- allosteric inhibitors --- in silico screening --- computer-aided drug discovery --- network analysis --- psychiatric disorders --- medications --- psychiatry --- mental disorders --- toxoplasmosis --- Toxoplasma gondii --- in vitro screening --- drug targets --- drug-disease interaction --- target-disease interaction --- DPP4 inhibitors --- lipid rafts

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The Special Issue "In Vitro and In Vivo Models of Colorectal Cancer for Clinical Application", edited by Marta Baiocchi and Ann Zeuner for Cancers, collects original research papers and reviews, depicting the current state and the perspectives of CRC models for preclinical and translational research. Original research papers published in this issue focus on some of the hottest topics in CRC research, such as circulating tumor cells, epigenetic regulation of stemness states, new therapeutic targets, molecular CRC classification and experimental CRC models such as organoids and PDXs. Additionally, four reviews on CRC stem cells, immunotherapy and drug discovery provide an updated viewpoint on key topics linking benchtop to bedside research in CRC.

Research & information: general --- Biology, life sciences --- Microbiology (non-medical) --- colorectal cancer --- organoids --- 3D bioprinting --- patient-derived xenograft --- cancer-on-chip --- drug combination --- cancer stem cells --- drug resistance --- clinical trials --- tumor-initiating cells --- tumor heterogeneity --- patient-derived cancer models --- single-cell RNA-sequencing --- tumor metabolism --- transcriptional programs --- tumor cell differentiation --- immunotherapy --- methods --- chromosomal instability --- DNA damage --- targeted therapy --- decitabine --- colon cancer --- DNA methylation --- clinical translation study --- machine learning --- patient-derived tumor organoid --- precision medicine --- radiation response --- rectal cancer --- PDX model --- CRC --- mutation analysis --- histological examination --- animal models --- in vitro culture --- cancer stem cell methods --- SATB2 --- colorectal carcinoma --- prognosis --- CDX2 --- circulating tumor cells --- CTC cluster --- size-based method --- ScreenCell® --- epithelial mesenchymal transition --- hypoxia --- HIF-1α --- immunofluorescence analysis --- sequential filtration