T-BioInfo platform for processing, analysis and visualization of complex omics data (i.e. genomics)

STREAMLINE AND SCALE UP YOUR RESEARCH BY LEVERAGING OUR MULTI-OMICS T-BIOINFO PLATFORM. The T-BioInfo platform provides user-friendly and intuitive access to multiple standard and custom data analysis methods.
These include processing various omics datasets like Genomics, Transcriptomics, Epigenomics, Metagenomics, Mass-spec Metabolomics and Proteomics as well as structural biology data. The platform also provides analysis and integration capabilities for heterogeneous datasets using supervised and unsupervised machine learning. Learn more: server.t-bio.info
Tags: transcriptomics, single cell transcriptomics, research, data analysis, python for biomedical data analysis, R for biomedical data analysis, big data analysis projects, transcriptomics data analysis, biomarker discovery, gene expression, machine learning, factor regression analysis, principal component analysis, statistical analysis, differential gene expression, gene enrichment analysis, data preparation for downstream analysis, cell line data and preparation, supervised machine learning, unsupervised machine learning, clustering, quantitative analysis, classification, regression, transcriptomics, single cell transcriptomics, research, data analysis, python for biomedical data analysis, R for biomedical data analysis, transcriptomics data analysis, biomarker discovery, gene expression, machine learning, factor regression analysis, principal component analysis, statistical analysis, differential gene expression, data preparation, clustering, quantitative analysis, classification, regression,
machine learning, biomedical data analysis, statistical analysis, unsupervised analysis, deep learning, random forest, support vector machine, big data clustering, conventional regression based methods, dimensionality reduction, conventional reduction methods, data visualization, data visualization techniques, Variance and covariance, Multivariate statistical analysis approaches, Continuous and Ordinal Data, Principal component analysis, Feature engineering, Clustering objects and features, Binary decision trees, Support vector machine, Discriminant analysis, Model accuracy and specificity, Feature selection for biomarker discovery, Methods for feature significance, Factor regression analysis and interpretation, Using regression for missing data, Data distributions, Generalized linear models, Logistic regression, Network analysis, Signaling and metabolic pathways, Gene expression networks, Overview of bayesian network, Exploratory analysis, Data mining, Feature selection, Convolutional Neural Networks, Deep Learning, Multi-Layer Perceptrons, Model Optimization, Variant analysis
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metagenomics, microbiome, metagenomic analysis, big data, online, mentor guided, training, program, lifesciences, data processing, annotation, visualization, agriculture, microbial communities, data analysis, human health, metagenomic sequencing data analysis, metagenomic analysis, microbial diversity, visualization of 16S metagenomics, ribosomal RNA, microbiology, dada2, qiime2, qiime, mothur, qiime tutorial, mapping or alignment, bioinformatics, next generation sequencing analysis, r coding, machine learning, molecular biology
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