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Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research.

Research & information: general --- Biology, life sciences --- target identification --- target validation --- label-free method for drugs --- anti-angiogenesis --- mechanism of action --- receptor tyrosine kinases --- curcumin --- natural products --- lipid --- lipidomics --- cardiac metaplasia --- Barrett's esophagus --- esophageal adenocarcinoma --- microbiota --- DNA sensing --- IFI16 --- cGAS --- innate immunity --- protein interactions --- virus-host interactions --- post-translational modifications --- mass spectrometry --- proteomics --- transcriptomics --- multi-omics --- multi-omics analysis --- study design --- bioinformatics --- machine learning --- analysis flow --- metabolomics --- planned myocardial infarction (PMI) --- myocardial infarction (MI) --- exercise --- heart --- cheminformatics --- batch variations --- eicosanoids --- fetal calf serum --- peroxisomes --- host-pathogen interactions --- secretome --- macrophages --- acute myeloid leukemia --- HL-60 cell line --- ATRA --- induced differentiation --- transcriptome --- proteome --- transcription factors --- key molecules --- regulatory pathway modelling --- SRM --- endometriosis --- inflammation --- target identification --- target validation --- label-free method for drugs --- anti-angiogenesis --- mechanism of action --- receptor tyrosine kinases --- curcumin --- natural products --- lipid --- lipidomics --- cardiac metaplasia --- Barrett's esophagus --- esophageal adenocarcinoma --- microbiota --- DNA sensing --- IFI16 --- cGAS --- innate immunity --- protein interactions --- virus-host interactions --- post-translational modifications --- mass spectrometry --- proteomics --- transcriptomics --- multi-omics --- multi-omics analysis --- study design --- bioinformatics --- machine learning --- analysis flow --- metabolomics --- planned myocardial infarction (PMI) --- myocardial infarction (MI) --- exercise --- heart --- cheminformatics --- batch variations --- eicosanoids --- fetal calf serum --- peroxisomes --- host-pathogen interactions --- secretome --- macrophages --- acute myeloid leukemia --- HL-60 cell line --- ATRA --- induced differentiation --- transcriptome --- proteome --- transcription factors --- key molecules --- regulatory pathway modelling --- SRM --- endometriosis --- inflammation

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research.

Research & information: general --- Biology, life sciences --- target identification --- target validation --- label-free method for drugs --- anti-angiogenesis --- mechanism of action --- receptor tyrosine kinases --- curcumin --- natural products --- lipid --- lipidomics --- cardiac metaplasia --- Barrett’s esophagus --- esophageal adenocarcinoma --- microbiota --- DNA sensing --- IFI16 --- cGAS --- innate immunity --- protein interactions --- virus–host interactions --- post-translational modifications --- mass spectrometry --- proteomics --- transcriptomics --- multi-omics --- multi-omics analysis --- study design --- bioinformatics --- machine learning --- analysis flow --- metabolomics --- planned myocardial infarction (PMI) --- myocardial infarction (MI) --- exercise --- heart --- cheminformatics --- batch variations --- eicosanoids --- fetal calf serum --- peroxisomes --- host-pathogen interactions --- secretome --- macrophages --- acute myeloid leukemia --- HL-60 cell line --- ATRA --- induced differentiation --- transcriptome --- proteome --- transcription factors --- key molecules --- regulatory pathway modelling --- SRM --- endometriosis --- inflammation --- n/a --- Barrett's esophagus --- virus-host interactions

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Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

Research & information: general --- Biology, life sciences --- tumor mutational burden --- DNA damage repair genes --- immunotherapy --- biomarker --- biomedical informatics --- breast cancer --- estrogen receptor alpha --- persistent organic pollutants --- drug-drug interaction networks --- molecular docking --- NGS --- ctDNA --- VAF --- liquid biopsy --- filtering --- variant calling --- DEGs --- diagnosis --- ovarian cancer --- PUS7 --- RMGs --- CPA4 --- bladder urothelial carcinoma --- immune cells --- T cell exhaustion --- checkpoint --- architectural distortion --- image processing --- depth-wise convolutional neural network --- mammography --- bladder cancer --- Annexin family --- survival analysis --- prognostic signature --- therapeutic target --- R Shiny application --- RNA-seq --- proteomics --- multi-omics analysis --- T-cell acute lymphoblastic leukemia --- CCLE --- sitagliptin --- thyroid cancer (THCA) --- papillary thyroid cancer (PTCa) --- thyroidectomy --- metastasis --- drug resistance --- biomarker identification --- transcriptomics --- machine learning --- prediction --- variable selection --- major histocompatibility complex --- bidirectional long short-term memory neural network --- deep learning --- cancer --- incidence --- mortality --- modeling --- forecasting --- Google Trends --- Romania --- ARIMA --- TBATS --- NNAR --- tumor mutational burden --- DNA damage repair genes --- immunotherapy --- biomarker --- biomedical informatics --- breast cancer --- estrogen receptor alpha --- persistent organic pollutants --- drug-drug interaction networks --- molecular docking --- NGS --- ctDNA --- VAF --- liquid biopsy --- filtering --- variant calling --- DEGs --- diagnosis --- ovarian cancer --- PUS7 --- RMGs --- CPA4 --- bladder urothelial carcinoma --- immune cells --- T cell exhaustion --- checkpoint --- architectural distortion --- image processing --- depth-wise convolutional neural network --- mammography --- bladder cancer --- Annexin family --- survival analysis --- prognostic signature --- therapeutic target --- R Shiny application --- RNA-seq --- proteomics --- multi-omics analysis --- T-cell acute lymphoblastic leukemia --- CCLE --- sitagliptin --- thyroid cancer (THCA) --- papillary thyroid cancer (PTCa) --- thyroidectomy --- metastasis --- drug resistance --- biomarker identification --- transcriptomics --- machine learning --- prediction --- variable selection --- major histocompatibility complex --- bidirectional long short-term memory neural network --- deep learning --- cancer --- incidence --- mortality --- modeling --- forecasting --- Google Trends --- Romania --- ARIMA --- TBATS --- NNAR