| Topic | Slides | Video |
|---|---|---|
| Topic 1.1: Introduction to Genomics and ML
Central dogma of molecular biology review |
Slides | - |
| Topic 1.2: Introduction to Genomics and ML
Types of genomic data: DNA sequences, RNA-seq, ChIP-seq, ATAC-seq |
Slides | - |
| Topic 1.3: Introduction to Genomics and ML
Overview of machine learning in biological contexts |
Slides | - |
| Topic 1.4: Introduction to Genomics and ML
Key databases and resources (NCBI, Ensembl, UCSC Genome Browser) |
Slides | - |
| Topic 2.1: Mathematical Foundations
Linear algebra for genomics applications |
Slides | - |
| Topic 2.2: Mathematical Foundations
Statistics and probability in biological data |
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| Topic 2.3: Mathematical Foundations
Information theory basics (entropy, mutual information) |
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| Topic 2.4: Mathematical Foundations
Introduction to Python/R for genomics |
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| Topic 3.1: Data Preprocessing and Feature Engineering
Genomic Data Types and Formats (FASTA, FASTQ, SAM/BAM, VCF, GFF/GTF formats) |
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| Topic 3.2: Data Preprocessing and Feature Engineering
Genomic Data Types and Formats (Quality control and preprocessing pipelines) |
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| Topic 3.3: Data Preprocessing and Feature Engineering
Genomic Data Types and Formats (Sequence alignment and variant calling basics) |
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| Topic 3.4: Data Preprocessing and Feature Engineering
Genomic Data Types and Formats (Data normalization techniques) |
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| Topic 3.5: Data Preprocessing and Feature Engineering
Feature Representation (k-mer representations and n-gram models) |
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| Topic 3.6: Data Preprocessing and Feature Engineering
Feature Representation (One-hot encoding for biological sequences) |
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| Topic 3.7: Data Preprocessing and Feature Engineering
Feature Representation (Position weight matrices (PWMs)) |
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| Topic 3.8: Data Preprocessing and Feature Engineering
Feature Representation (Physicochemical properties as features) |
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| Topic 4.1: Classical Machine Learning in Genomics
Supervised Learning Applications (Classification problems: gene function prediction, disease classification) |
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| Topic 4.2: Classical Machine Learning in Genomics
Supervised Learning Applications (Regression: gene expression prediction, quantitative trait analysis) |
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| Topic 4.3: Classical Machine Learning in Genomics
Supervised Learning Applications (Support vector machines for sequence classification) |
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| Topic 4.4: Classical Machine Learning in Genomics
Supervised Learning Applications (Random forests for genomic feature selection) |
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| Topic 4.5: Classical Machine Learning in Genomics
Unsupervised Learning (Principal component analysis (PCA) for population structure) |
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| Topic 4.6: Classical Machine Learning in Genomics
Unsupervised Learning (Clustering methods for gene expression analysis) |
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| Topic 4.7: Classical Machine Learning in Genomics
Unsupervised Learning (Hidden Markov Models (HMMs) for sequence analysis) |
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| Topic 4.8: Classical Machine Learning in Genomics
Unsupervised Learning (Dimensionality reduction techniques) |
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| Topic 4.9: Classical Machine Learning in Genomics
Ensemble Methods and Model Selection (Bagging and boosting in genomics) |
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| Topic 4.10: Classical Machine Learning in Genomics
Ensemble Methods and Model Selection (Cross-validation strategies for genomic data) |
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| Topic 4.11: Classical Machine Learning in Genomics
Ensemble Methods and Model Selection (Dealing with class imbalance) |
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| Topic 4.12: Classical Machine Learning in Genomics
Ensemble Methods and Model Selection (Feature selection and regularization) |
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| Topic 5.1: Deep Learning Fundamentals
Neural Networks for Genomics (Perceptrons and multilayer networks) |
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| Topic 5.2: Deep Learning Fundamentals
Neural Networks for Genomics (Backpropagation algorithm) |
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| Topic 5.3: Deep Learning Fundamentals
Neural Networks for Genomics (Activation functions and their biological interpretations) |
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| Topic 5.4: Deep Learning Fundamentals
Neural Networks for Genomics (Overfitting and regularization techniques) |
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| Topic 5.5: Deep Learning Fundamentals
Convolutional Neural Networks (CNN architectures for sequence data) |
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| Topic 5.6: Deep Learning Fundamentals
Convolutional Neural Networks (Motif discovery using CNNs) |
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| Topic 5.7: Deep Learning Fundamentals
Convolutional Neural Networks (DeepBind and similar tools) |
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| Topic 5.8: Deep Learning Fundamentals
Convolutional Neural Networks (Transcription factor binding site prediction) |
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| Topic 5.9: Deep Learning Fundamentals
Recurrent Neural Networks (LSTM and GRU architectures) |
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| Topic 5.10: Deep Learning Fundamentals
Recurrent Neural Networks (Sequence-to-sequence models) |
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| Topic 5.11: Deep Learning Fundamentals
Recurrent Neural Networks (Protein secondary structure prediction) |
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| Topic 5.12: Deep Learning Fundamentals
Recurrent Neural Networks (RNA structure prediction) |
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| Topic 6.1: Advanced Deep Learning Applications
Attention Mechanisms and Transformers (Self-attention in biological sequences) |
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| Topic 6.2: Advanced Deep Learning Applications
Attention Mechanisms and Transformers (BERT-like models for genomics (DNABERT, ProtBERT)) |
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| Topic 6.3: Advanced Deep Learning Applications
Attention Mechanisms and Transformers (Positional encoding for genomic sequences) |
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| Topic 6.4: Advanced Deep Learning Applications
Attention Mechanisms and Transformers (Long-range dependency modeling) |
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| Topic 6.5: Advanced Deep Learning Applications
Generative Models (Variational autoencoders (VAEs) for genomic data) |
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| Topic 6.6: Advanced Deep Learning Applications
Generative Models (Generative adversarial networks (GANs)) |
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| Topic 6.7: Advanced Deep Learning Applications
Generative Models (Sequence generation and design) |
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| Topic 6.8: Advanced Deep Learning Applications
Generative Models (Data augmentation techniques) |
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| Topic 7.1: Epigenetics and Regulatory Genomics
Epigenetic Data Analysis (Histone modification patterns) |
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| Topic 7.2: Epigenetics and Regulatory Genomics
Epigenetic Data Analysis (DNA methylation analysis) |
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| Topic 7.3: Epigenetics and Regulatory Genomics
Epigenetic Data Analysis (HChromatin accessibility (ATAC-seq, FAIRE-seq)) |
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| Topic 7.4: Epigenetics and Regulatory Genomics
Epigenetic Data Analysis (3D genome organization (Hi-C data)) |
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| Topic 7.5: Epigenetics and Regulatory Genomics
Machine Learning for Regulatory Elements (Enhancer-promoter interaction prediction) |
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| Topic 7.6: Epigenetics and Regulatory Genomics
Machine Learning for Regulatory Elements (Chromatin state segmentation) |
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| Topic 7.7: Epigenetics and Regulatory Genomics
Machine Learning for Regulatory Elements (Epigenome-wide association studies (EWAS)) |
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| Topic 7.8: Epigenetics and Regulatory Genomics
Machine Learning for Regulatory Elements (Multi-omics integration approaches) |
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| Topic 8.1: Single-Cell Genomics
Single-Cell RNA Sequencing (scRNA-seq data characteristics and challenges) |
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| Topic 8.2: Single-Cell Genomics
Single-Cell RNA Sequencing (Dimensionality reduction (t-SNE, UMAP)) |
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| Topic 8.3: Single-Cell Genomics
Single-Cell RNA Sequencing (Cell type identification and clustering) |
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| Topic 8.4: Single-Cell Genomics
Single-Cell RNA Sequencing (Pseudotime analysis and trajectory inference) |
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| Topic 8.5: Single-Cell Genomics
Deep Learning for Single-Cell Data (Autoencoders for dimensionality reduction) |
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| Topic 8.6: Single-Cell Genomics
Deep Learning for Single-Cell Data (Deep generative models (scVI, scGAN)) |
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| Topic 8.7: Single-Cell Genomics
Deep Learning for Single-Cell Data (Batch effect correction) |
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| Topic 8.8: Single-Cell Genomics
Deep Learning for Single-Cell Data (Cell-cell communication inference) |
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| Topic 9.1: Integration and Applications
Multi-Omics Integration (Data fusion strategies) |
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| Topic 9.2: Integration and Applications
Multi-Omics Integration (Network-based approaches) |
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| Topic 9.3: Integration and Applications
Multi-Omics Integration (Pathway analysis and functional annotation) |
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| Topic 9.4: Integration and Applications
Multi-Omics Integration (Precision medicine applications) |
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| Topic 9.5: Integration and Applications
Current Challenges and Future Directions (Interpretability in genomic deep learning) |
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| Topic 9.6: Integration and Applications
Current Challenges and Future Directions (Fairness and bias in genomic algorithms) |
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| Topic 9.7: Integration and Applications
Current Challenges and Future Directions (Privacy-preserving genomic analysis) |
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| Topic 9.8: Integration and Applications
Current Challenges and Future Directions (Large language models for genomics) |
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