- Course overview
- Search within this course
- 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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References
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- Wellcome Trust Case Control Consortium (2010) Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls Nature 464(7289):713-20
- Wellcome Trust Case Control Consortium (2007) Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants Nat Genet 39(11):1329-37
- Malik et al. (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes Nat Genet 50(4):524-537
- Matthiesen R & Jensen O N (2008) Analysis of mass spectrometry data in proteomics Methods Mol Biol 453:105-22
- Chin S T et al. (2018) Cross-platform mass spectrometry annotation in breathomics of oesophageal-gastric cancer Sci Rep 8(1):5139
- Mendez D et al. (2019) ChEMBL: towards direct deposition of bioassay data Nucleic Acids Res 47(D1):D930-D940
- Smith S M et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL Neuroimage 23 Suppl 1:S208-19
- Alfaro-Almagro F et al. (2018) Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank Neuroimage 166:400-424
- Vamathevan J et al. (2019) Applications of machine learning in drug discovery and development Nature reviews. Drug Discovery, 18(6):463-477
- Frank E, Hall M A & Witten I H (2016). The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition, 2016
- Jansen R et al. (2002) Identification of genes that are associated with DNA repeats in prokaryotes Mol Microbiol 43(6):1565-75
- Mojica F J M & Garrett R A (2012) Discovery and Seminal Developments in the CRISPR Field In: Barrangou R., van der Oost J. (eds) CRISPR-Cas Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34657-6_1
- Mojica F J M et al. (2005) Intervening sequences of regularly spaced prokaryotic repeats derive from foreign genetic elements J Mol Evol 60(2):174-82
- Lander E S (2016) The Heroes of CRISPR Cell 164(1-2):18-28
- Scott A (2018) How CRISPR is transforming drug discovery Nature 555(7695):S10-S11
- Behan F et al. (2019) Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens Nature 568(7753):511-516
- Iorio F et al. (2016) A Landscape of Pharmacogenomic Interactions in Cancer Cell 166(3):740-754
- Mountjoy E (2020) Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci doi: https://doi.org/10.1101/2020.09.16.299271