- Course overview
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- What is machine learning?
- ML in drug discovery: why now?
- ML in the drug discovery pipeline
- Getting started in ML using WEKA
- Hands-on with WEKA
- Identifying targets for cancer using gene expression profiles
- Other tools utilising ML or NLP for drug discovery
- Summary
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- References
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What is machine learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on designing algorithms to allow learning from large amounts of a given type of data. The learning process uses the “training data” to teach the computer how to make sense of the data and allow it to make predictions about new data, known as a “test set”. Machine learning models learn from data and improve from experience, without any human intervention.
Machine learning algorithms can learn from structured data (i.e., organised in a matrix format) or unstructured data (e.g., images, DNA sequences, graphs, scientific publications). Deep learning (DL) is a subset of machine learning that includes high-level representation learning using artificial neural networks. Natural language processing (NLP) is a branch of AI that allows machines to understand human language and can be used to extract information from textual data such as scientific literature. Both ML and DL can be used to develop NLP applications. Figure 1 shows the relationship between AI, ML, DL, and NLP.
