This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, Bayesian methods, pattern recognition, data fusion, time series analysis, etc. We will discuss the following biological problems: 1) Analysis of high throughput biological data, such as gene expression data, focusing on issues ranging from data acquisition to pattern recognition and classification. 2) Computational genomics, focusing on gene finding, motifs detection, sequence evolution and comparative genomics. 3) Systems biology, concerning how to combine sequence, expression and other biological data sources (protein-protein interaction, protein-DNA binding and more) to infer the structure and function of different systems in the cell.
This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, Bayesian methods, pattern recognition, data fusion, time series analysis, etc. We will discuss the following biological problems: 1) Analysis of high throughput biological data, such as gene expression data, focusing on issues ranging from data acquisition to pattern recognition and classification. 2) Computational genomics, focusing on gene finding, motifs detection, sequence evolution and comparative genomics. 3) Systems biology, concerning how to combine sequence, expression and other biological data sources (protein-protein interaction, protein-DNA binding and more) to infer the structure and function of different systems in the cell.
This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, Bayesian methods, pattern recognition, data fusion, time series analysis, etc. We will discuss the following biological problems: 1) Analysis of high throughput biological data, such as gene expression data, focusing on issues ranging from data acquisition to pattern recognition and classification. 2) Computational genomics, focusing on gene finding, motifs detection, sequence evolution and comparative genomics. 3) Systems biology, concerning how to combine sequence, expression and other biological data sources (protein-protein interaction, protein-DNA binding and more) to infer the structure and function of different systems in the cell.
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