Metabolomics is the study of the metabolome, the complete set of small-molecule chemicals found in a biological sample (cells, tissues, biofluids, etc.). ArrayGen Bioinformatics services are powered by AI (Artificial Intelligence) in metabolomics are transforming how biological data is analyzed, interpreted, and applied. These services combine computational biology with machine learning, deep learning, and natural language processing to automate and improve the accuracy of complex data-driven tasks in genomics, proteomics, transcriptomics, and more.
LC-MS/MS or GC-MS
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TCGA Expression Map is an interactive resource for exploring RNA-seq differential expression across 24 human cancer types, covering 42,461 human genes from the TCGA (The Cancer Genome Atlas) study. This dataset includes RNA-seq data from more than 10,000 cancer patients across multiple tumor types. After careful preprocessing, we performed differential expression analysis using standard differential expression methods, comparing case vs. control samples for each cancer type to generate reliable log2 fold change (log2FC) values.
ArrayGen HeatMap Generator is an online tool for researchers to generate heatmap from RNASeq differential Log2 fold change based on case and control. It also accepts FPKM expression values.
An ML Framework is a machine learning–driven platform for biomarker which identifies DEGs using gene expression data and trains a deep neural network (DNN) for disease classification, and applies SHAP to highlight predictive biomarker genes with insights.