RSS Feed for genome rearrangements https://solvonauts.org/%3Faction%3Drss_search%26term%3Dgenome+rearrangements RSS Feed for genome rearrangements 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 6.047 Computational Biology: Genomes, Networks, Evolution (MIT) 6.047 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://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-genomes-networks-evolution-fall-2008 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-genomes-networks-evolution-fall-2008 6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT) This course is offered to undergraduates and addresses several algorithmic challenges in computational biology The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets Topics covered include biological sequence analysis gene identification regulatory motif discovery genome assembly genome duplication and rearrangements evolutionary theory clustering algorithms and scale free networks This course is offered to undergraduates and addresses several algorithmic challenges in computational biology The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets Topics covered include biological sequence analysis gene identification regulatory motif discovery genome assembly genome duplication and rearrangements evolutionary theory clustering algorithms and scale free networks http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-algorithms-for-computational-biology-spring-2005 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-algorithms-for-computational-biology-spring-2005 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 6.096 Algorithms for Computational Biology (MIT) This course is offered to undergraduates and addresses several algorithmic challenges in computational biology The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets Topics covered include biological sequence analysis gene identification regulatory motif discovery genome assembly genome duplication and rearrangements evolutionary theory clustering algorithms and scale free networks https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-algorithms-for-computational-biology-spring-2005 https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-algorithms-for-computational-biology-spring-2005