ISBN 13: 9780534952624
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Examinations None. Class schedule Date. Molecular modeling—Macromolecular forcefields. Molecular modeling—optimization methods. Transcription factor binding motifs compute-motif-pvalue. Supervised learning for transcription factor binding. Semi-automated genome annotation and epigenomic imputation. Transcriptome quantification and differential expression.
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Single-cell RNA-seq analytics: dimensionality reduction, clustering. Other terms appear in italics in their definition. Many of our algorithms are presented first through English sen- tences and then in pseudo code format pseudo code conventions are described in Sec- tion 2. In some cases the pseudo code provides a level of detail that should help readers interested in actual implementation.
Computational Molecular Biology course
Summaries are provided for the longer chapters. They may require knowledge of computer science tech- niques not presented in the book. Finally exercises marked with a diamond o are research problems that have not been solved as far as the authors know.
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At the end of the book we provide answers or hints to selected exercises. If you find any, or have any suggestions for improvement, we will be glad to hear from you.
Please send error reports or any other comments to us at bio dcc. Setubal Institute de Computac. We thank in advance all readers interested in helping us make this a better book. We believe that without such a meeting we would not be writing this preface, so we are thankful to have had that opportunity. We got a lot of encouragement from them, and also some helpful hints. Bonnie in particular was very kind in giving us copies of her course notes at an early stage.