MIT Deep Learning Genomics - Lecture 14 - Deep Learning for Gene Expression Analysis (Spring20)
Lecture 14 - RNA, PCA, t-SNE, Embeddings (Spring20)
MIT 6.874 Lecture 14. Spring 2020
Course website: https://mit6874.github.io/
Lecture slides: https://mit6874.github.io/assets/sp2020/slides/L14_PredictingExpressionSplicing.pdf
Today: Predicting gene expression and splicing
0. Review: Expression, unsupervised learning, clustering
1. Up-sampling: predict 20,000 genes from 1000 genes
2. Compressive sensing: Composite measurements
3. DeepChrome+LSTMs: predict expression from chromatin
4. Predicting splicing from sequence: 1000s of features
5. Unsupervised deep learning: Restricted Boltzmann mach.
6. Multi-modal programs: Expr+DNA+miRNARMBs Liang
Видео MIT Deep Learning Genomics - Lecture 14 - Deep Learning for Gene Expression Analysis (Spring20) канала Manolis Kellis
MIT 6.874 Lecture 14. Spring 2020
Course website: https://mit6874.github.io/
Lecture slides: https://mit6874.github.io/assets/sp2020/slides/L14_PredictingExpressionSplicing.pdf
Today: Predicting gene expression and splicing
0. Review: Expression, unsupervised learning, clustering
1. Up-sampling: predict 20,000 genes from 1000 genes
2. Compressive sensing: Composite measurements
3. DeepChrome+LSTMs: predict expression from chromatin
4. Predicting splicing from sequence: 1000s of features
5. Unsupervised deep learning: Restricted Boltzmann mach.
6. Multi-modal programs: Expr+DNA+miRNARMBs Liang
Видео MIT Deep Learning Genomics - Lecture 14 - Deep Learning for Gene Expression Analysis (Spring20) канала Manolis Kellis
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