AI for Genomics
Artificial intelligence applications in genomic research and computational biology
Discover how artificial intelligence is revolutionizing genomic research, from DNA methylation analysis to personalized medicine. These posts explore cutting-edge computational approaches to understanding biological systems at the genomic level.
Unraveling Human Biology: From Organism to Atom
Setting the Foundation for Digital Biology
A comprehensive exploration of human biology across multiple organizational levels, from the entire organism to atomic structure. This foundational article sets the stage for understanding computational genomics and digital biology approaches.
Decoding DNA: Sequence Encoding Guide
Comprehensive Methods for ML Applications
Complete guide to DNA sequence encoding techniques for machine learning, covering one-hot encoding, k-mer tokenization, BPE, embeddings, and advanced methods with practical implementations and performance comparisons.
Statistical Methods in Genomics
Essential biostatistics for genomic analysis
Comprehensive overview of the statistical foundations underlying modern genomics research, including multiple testing correction, differential expression analysis, and population genetics methods.
Advanced Statistical Testing
Hypothesis testing in biological contexts
Deep exploration of statistical hypothesis testing specifically applied to genomic and biological datasets, with focus on experimental design and interpretation.
Basic Statistical Testing
Foundational statistical concepts
Introduction to fundamental statistical testing methods used in genomics research, covering t-tests, significance testing, and data interpretation.
Research Areas
DNA Methylation
Epigenomic analysis and imputation methods using transfer learning
Deep Learning
Neural network architectures for genomic sequence analysis
Predictive Modeling
Machine learning approaches for biomarker discovery
Upcoming Topics
- Single-Cell Genomics: AI approaches to cellular heterogeneity analysis
- Pharmacogenomics: Personalized medicine through AI-driven drug discovery
- Multi-omics Integration: Computational frameworks for systems biology
- Evolutionary Genomics: Machine learning in phylogenetics and population studies
Advancing genomic research through the power of artificial intelligence and computational innovation.