🧬 AI4Bio Learning Hub
Computational Biology & Machine Learning Tutorials
Welcome to my personal learning hub. Here I document key concepts, algorithms, and insights in bioinformatics. Explore tutorials on single-cell analysis, deep learning architectures, and statistical methods.
🐭 Meet Healshu (希欧-鼠) — Inspired by the mythical Ershu (耳鼠) from Classic of Mountains and Seas (山海经).
This curious creature spends its days listening to the whispers of cells, decoding biological secrets through algorithms.
When a hidden pattern emerges from the data, you might hear a soft "hew-hew~" of discovery.
11
Tutorials
4
Core Topics
∞
Learning
Single-Cell Analysis
Decoding cellular heterogeneity, dynamics, and perturbations
Trajectory Inference Overview
Understand Pseudotime, RNA velocity, and lineage tracing algorithms.
Perturbation Modeling
Computational methods for predicting cellular responses to CRISPR or drugs.
Drug Response Prediction
Updated
Deep learning approaches for predicting drug sensitivity at single-cell resolution.
Multi-Omics Integration
Frameworks for integrating RNA, ATAC, protein, and spatial transcriptomics.
Cell Annotation & Type Identification
Comprehensive guide to computational tools for assigning cell type identities in scRNA-seq.
Spatial Transcriptomics Technologies
New
Comprehensive guide to spatially-resolved gene expression analysis: from sequencing-based to imaging-based methods.
Genomics & Variant Analysis
Sequence modeling and genome engineering
Content In Development
Tutorials on DNA language models and Variant Effect Prediction are currently being written.
Get notified when readyMathematical Foundations
The math toolkit powering computational biology
Foundation Models & DL
Large-scale pre-training for biological systems
Attention Mechanisms
Master self-attention, multi-head attention, and how Transformers weigh sequence importance.
Neural Architectures
Deep dive into CNNs, RNNs, and Transformers applied to biological sequences.
Tokenization Strategies
New
Explore BPE, k-mer, and other tokenization methods for DNA, RNA, and protein sequences.
Essential Resources
Foundational statistics and references