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1 - 4 of 4 results for: STATS 202

BIOMEDIN 215: Data Driven Medicine

With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: CS 106A; familiarity with statistics ( STATS 202) and biology. Recommended: one of CS 246 (previously CS 345A), STATS 305, or CS 229.
Terms: Aut | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Shah, N. (PI)

BIOMEDIN 225: Data Driven Medicine: Lectures

With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics ( STATS 202) and biology.
Terms: Aut | Units: 2 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Shah, N. (PI)

STATS 202: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit

STATS 155: Statistical Methods in Computational Genetics

The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as micro-arrays and data base searches. How to formulate biological problems in an algorithmic decomposed form, and building blocks common to many problems such as Markovian models, multivariate analyses. Some software covered in labs (Python, Biopython, XGobi, MrBayes, HMMER, Probe). Prerequisites: knowledge of probability equivalent to STATS 116, STATS 202 and one class in computing at the CS 106 level. (WIM)
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Holmes, S. (PI)
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