Publisher's Synopsis
Computational biology is a promptly expanding field, and the number and variety of computational methods used for DNA and protein sequence analysis is rising day after day. These algorithms are tremendously appreciated to biotechnology companies and to researchers and teachers in universities. Beginning in the 1980s, computational biology drew on further developments in computer science, including a number of aspects of artificial intelligence (AI). Among these were knowledge representation, which contributed to the development of ontologies (the representation of concepts and their relationships) that codify biological knowledge in "computer-readable" form, and natural-language processing, which provided a technological means for mining information from text in the scientific literature. Perhaps most significantly, the subfield of machine learning found wide use in biology, from modeling sequences for purposes of pattern recognition to the analysis of high-dimensional (complex) data from large-scale gene-expression studies. Computational Methods in Molecular Biology provide important tools for solving many of the key problems in Bioinformatics including determining the function of a newly discovered genetic sequence; determining the evolutionary relationships among genes, proteins, and entire species, predicting the structure and function of proteins, classification of DNA and protein sequences, motif identification and many more. This book clarifies the latest computer technology for analyzing DNA, RNA, and protein sequences. The aim of this work is to bring together scientists from different fields of expertise in order to discuss biological problems and propose new ideas and techniques in today's applications, in the areas of molecular biology, medicine and generally in the application of computational techniques. The rapid characterization of an emerging disease will provide invaluable information in the prevention and control of the disease. Thus, computational studies may be particularly important for emerging diseases, where understanding of the diseases is often urgently needed before their widespread. Compared to the lab bench work, computational methods can provide a relatively fast and efficient approach to derive theoretical models based on experimental data, to simulate/predict biological processes and to provide working hypotheses for rational designs of new experiments. It usually takes years and months to characterize a single protein experimentally. However, it only takes about hours, minutes, or even seconds to infer the biological functions once the computational model is constructed for the specific requirement. Protein threading is a computational technique for protein backbone structure prediction. It makes a structural fold prediction from the amino-acid sequence by recognizing a native-like fold of a query protein (if there is any) in a database of experimentally determined structures. The identified folds and predicted structures (even not highly accurate) could provide significant amount of information about the proteins' functions and give useful guidance to experimentalists to conduct further experiments for functional investigation. Hence, a good computational framework can play important roles in the research of emerging diseases. Computational biology is a promptly expanding field, and the number and variety of computational methods used for DNA and protein sequence analysis is rising day after day. These algorithms are tremendously appreciated to biotechnology companies and to researchers and teachers in universities. Beginning in the 1980s, computational biology drew on further developments in computer science, including a number of aspects of artificial intelligence (AI). Among these were knowledge representation, which contributed to the development of ontologies (the representation of concepts and their relationships) that codify biological knowledge in "computer-readable" form, and natural-language processing, which provided a technological means for mining information from text in the scientific literature. Perhaps most significantly, the subfield of machine learning found wide use in biology, from modeling sequences for purposes of pattern recognition to the analysis of high-dimensional (complex) data from large-scale gene-expression studies. Computational Methods in Molecular Biology provide important tools for solving many of the key problems in Bioinformatics including determining the function of a newly discovered genetic sequence; determining the evolutionary relationships among genes, proteins, and entire species, predicting the structure and function of proteins, classification of DNA and protein sequences, motif identification and many more. This book clarifies the latest computer technology for analyzing DNA, RNA, and protein sequences. The aim of this work is to bring together scientists from different fields of expertise in order to discuss biological problems and propose new ideas and techniques in today's applications, in the areas of molecular biology, medicine and generally in the application of computational techniques. The rapid characterization of an emerging disease will provide invaluable information in the prevention and control of the disease. Thus, computational studies may be particularly important for emerging diseases, where understanding of the diseases is often urgently needed before their widespread. Compared to the lab bench work, computational methods can provide a relatively fast and efficient approach to derive theoretical models based on experimental data, to simulate/predict biological processes and to provide working hypotheses for rational designs of new experiments. It usually takes years and months to characterize a single protein experimentally. However, it only takes about hours, minutes, or even seconds to infer the biological functions once the computational model is constructed for the specific requirement. Protein threading is a computational technique for protein backbone structure prediction. It makes a structural fold prediction from the amino-acid sequence by recognizing a native-like fold of a query protein (if there is any) in a database of experimentally determined structures. The identified folds and predicted structures (even not highly accurate) could provide significant amount of information about the proteins' functions and give useful guidance to experimentalists to conduct further experiments for functional investigation. Hence, a good computational framework can play important roles in the research of emerging diseases. Computational biology is a promptly expanding field, and the number and variety of computational methods used for DNA and protein sequence analysis is rising day after day. These algorithms are tremendously appreciated to biotechnology companies and to researchers and teachers in universities. Beginning in the 1980s, computational biology drew on further developments in computer science, including a number of aspects of artificial intelligence (AI). Among these were knowledge representation, which contributed to the development of ontologies (the representation of concepts and their relationships) that codify biological knowledge in "computer-readable" form, and natural-language processing, which provided a technological means for mining information from text in the scientific literature. Perhaps most significantly, the subfield of machine learning found wide use in biology, from modeling sequences for purposes of pattern recognition to the analysis of high-dimensional (complex) data from large-scale gene-expression studies. Computational Methods in Molecular Biology provide important tools for solving many of the key problems in Bioinformatics including determining the function of a newly discovered genetic sequence; determining the evolutionary relationships among genes, proteins, and entire species, predicting the structure and function of proteins, classification of DNA and protein sequences, motif identification and many more. This book clarifies the latest computer technology for analyzing DNA, RNA, and protein sequences. The aim of this work is to bring together scientists from different fields of expertise in order to discuss biological problems and propose new ideas and techniques in today's applications, in the areas of molecular biology, medicine and generally in the application of computational techniques. The rapid characterization of an emerging disease will provide invaluable information in the prevention and control of the disease. Thus, computational studies may be particularly important for emerging diseases, where understanding of the diseases is often urgently needed before their widespread. Compared to the lab bench work, computational methods can provide a relatively fast and efficient approach to derive theoretical models based on experimental data, to simulate/predict biological processes and to provide working hypotheses for rational designs of new experiments. It usually takes years and months to characterize a single protein experimentally. However, it only takes about hours, minutes, or even seconds to infer the biological functions once the computational model is constructed for the specific requirement. Protein threading is a computational technique for protein backbone structure prediction. It makes a structural fold prediction from the amino-acid sequence by recognizing a native-like fold of a query protein (if there is any) in a database of experimentally determined structures. The identified folds and predicted structures (even not highly accurate) could provide significant amount of information about the proteins' functions and give useful guidance to experimentalists to conduct further experiments for functional investigation. Hence, a good computational framework can play important roles in the research of emerging diseases.
