The integration of Artificial Intelligence (AI) and Machine Learning (ML) into cancer genomics is revolutionizing precision oncology by enabling scalable, accurate, and clinically actionable analysis of complex genomic data. ArrayGen offers AI‑empowered cancer genomics services that turn raw sequencing data into actionable clinical insights. With automated pipelines for germline & somatic variant detection, rich annotation from leading databases, and expert‐guided interpretation in line with ACMG/AMP standards, we enable precise clinical reporting from assays like WGS, WES, and targeted panels. By integrating transcriptomic and epigenomic data, plus AI/ML‑based prioritization, our cancer genomics platform supports earlier detection, optimal therapy choice, and improved outcomes — especially for underrepresented populations. Quality, reproducibility, and data security form the backbone of all our services.
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We strive for excellence through scientific leadership, collaboration, and a strong focus on quality. Our teams are driven to deliver high impact bioinformatics and big data solutions that push the boundaries of innovation.
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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.