❓Top answers to the question:
How should one start to learn computational biology?
🌐 See the thread
📲Channel: @Bioinformatics
#computational_biology #question
How should one start to learn computational biology?
🌐 See the thread
📲Channel: @Bioinformatics
#computational_biology #question
👍5
📖 A Primer for Computational Biology
💥 Free Ebook from Oregon State University
🌐 Study
📲Channel: @Bioinformatics
#book #computational_biology
💥 Free Ebook from Oregon State University
🌐 Study
📲Channel: @Bioinformatics
#book #computational_biology
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🎞 Statistical Methods in Computational Biology
💥Free online recorded course videos from UCLA
📽 Watch introduction session
🖇 Videos of other sessions
📲Channel: @Bioinformatics
#video #computational_biology #statistics
💥Free online recorded course videos from UCLA
📽 Watch introduction session
🖇 Videos of other sessions
📲Channel: @Bioinformatics
#video #computational_biology #statistics
YouTube
STATS M254 - Stat Methods in Comp Bio - Lec 1 (Overview: Statistical Inference vs. Machine Learning)
Papers to read: https://www.dropbox.com/sh/mkoz1j0m38mwwa8/AAAGNKUFBifCE7mcXO9y5Dhda?dl=0
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Forwarded from Network Analysis Resources & Updates
🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology
💥Free recorded course by Prof. Marinka Zitnik
💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
💥Free recorded course by Prof. Marinka Zitnik
💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #computational_biology
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 18 - GNNs in Computational Biology
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XVImFC
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD
We are glad to invite Prof.…
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