RSS Feed for Evolution: Comparative genomics https://solvonauts.org/%3Faction%3Drss_search%26term%3DEvolution%3A+Comparative+genomics RSS Feed for Evolution: Comparative genomics 6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT) This course focuses on the algorithmic and machine learning foundations of computational biology combining theory with practice We study the principles of algorithm design for biological datasets and analyze influential problems and techniques We use these to analyze real datasets from large scale studies in genomics and proteomics The topics covered include Genomes Biological Sequence Analysis Hidden Markov Models Gene Finding RNA Folding Sequence Alignment Genome Assembly Networks Gene Expression Analysis Regulatory Motifs Graph Algorithms Scale free Networks Network Motifs Network Evolution Evolution Comparative Genomics Phylogenetics Genome Duplication Genome Rearrangements Evolutionary Theory Rapid Evolution This course focuses on the algorithmic and machine learning foundations of computational biology combining theory with practice We study the principles of algorithm design for biological datasets and analyze influential problems and techniques We use these to analyze real datasets from large scale studies in genomics and proteomics The topics covered include Genomes Biological Sequence Analysis Hidden Markov Models Gene Finding RNA Folding Sequence Alignment Genome Assembly Networks Gene Expression Analysis Regulatory Motifs Graph Algorithms Scale free Networks Network Motifs Network Evolution Evolution Comparative Genomics Phylogenetics Genome Duplication Genome Rearrangements Evolutionary Theory Rapid Evolution http://dspace.mit.edu/handle/1721.1/55901 http://dspace.mit.edu/handle/1721.1/55901 6.895 Computational Biology: Genomes, Networks, Evolution (MIT) This course focuses on the algorithmic and machine learning foundations of computational biology combining theory with practice We study the principles of algorithm design for biological datasets and analyze influential problems and techniques We use these to analyze real datasets from large scale studies in genomics and proteomics The topics covered include Genomes Biological Sequence Analysis Hidden Markov Models Gene Finding RNA Folding Sequence Alignment Genome Assembly Networks Gene Expression Analysis Regulatory Motifs Graph Algorithms Scale free Networks Network Motifs Network Evolution Evolution Comparative Genomics Phylogenetics Genome Duplication Genome Rearrangements Evolutionary Theory Rapid Evolution https://dspace.mit.edu/handle/1721.1/55901 https://dspace.mit.edu/handle/1721.1/55901