Academy

Multi-omics Data Integration

Multi-omics combines various layers of biological data—such as genomics, transcriptomics (gene expression), epigenomics (methylation), and proteomics—to give a holistic view of cellular processes. By integrating these datasets, researchers can capture complex interactions between DNA, RNA, proteins, and chemical modifications that underlie health and disease.

• Genomics: DNA sequence and copy number data revealing genetic variations.
• Transcriptomics: mRNA and microRNA expression levels indicating gene activity.
• Epigenomics: DNA methylation patterns that regulate gene expression.
• Proteomics: Protein abundance and modifications reflecting functional outcomes.

Integration involves aligning features across samples, normalizing measurements, and selecting relevant signals—transforming raw data into analysis-ready tables that machine learning algorithms can use to identify patterns and build predictive models.

Machine Learning in Cancer Genomics

Machine learning applies computational models to detect patterns in large-scale biological datasets. In cancer genomics, these methods help classify tumor types, predict patient outcomes, and discover biomarkers by learning from multi-omics profiles.

1. Data Preprocessing: Convert raw counts to standardized units (e.g., FPKM for gene expression), filter noise, and handle missing values.
2. Feature Engineering: Align genes across omics layers, normalize values (z-score), and select significant features via statistical tests like ANOVA.
3. Model Training: Use algorithms such as random forests, support vector machines, or deep neural networks to learn relationships between omics features and cancer subtypes.
4. Evaluation: Assess performance using metrics like precision, recall, F1-score for classification; silhouette score or survival analysis p-values for clustering; and mean squared error for imputation tasks.

By standardizing workflows and providing ready-made datasets, platforms like MLOmics lower the barrier to entry—empowering researchers without advanced bioinformatics expertise to develop robust, reproducible cancer models.