We welcome original research and review articles. : Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (9780511790492) by Durbin, Richard Eddy, Sean R. This chapter is an attempt to highlight some of the commonly used algorithms for the biological sequence analysis ranging. In this Special Issue, we will explore the potential of applying artificial intelligence and related computational techniques to mine and model a significant amount of biological sequence data for annotation and functional analysis. With the increase in huge amount of biological sequence data from large genome and proteome sequencing projects, efforts have been made to develop computational algorithms and databases to manage the information. Sequence alignment is based on the fact that all living organisms are related by evolution. Biological sequence analysis compares, aligns, indexes, and analyzes biological sequences and thus plays a crucial role in bioinformatics and modern biology. For example, in the context of learning with structured data, graph inference, semi-supervised learning, and novel combinations of optimization and learning algorithms. Biological sequences generally refer to sequences of nucleotides or amino acids. In recent years, bioinformatics has already induced significant new developments of general interest in deep learning. The respective chapters provide detailed information on biological databases, sequence alignment, molecular evolution, next-generation sequencing, systems.
Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. Sequences evolving over species and clades through mutations include insertions, deletions (indels), and mismatches. Artificial Intelligence (AI) has recently had a profound impact on areas such as image and speech recognition.ĪI is now rapidly propagating into the bioinformatics field, greatly improving the success rates and lowering the cost of sequential functional annotation. Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modem molecular genetics. Programmatically, biological sequence analysis is not much different than comparing strings and text, and thus, developing the concept of alignment is important. The rapid increase in biological data dimension is a challenge for traditional computational analysis methods. These algorithms take pairs of sequences of bases making up DNA or sequences of amino acids making up proteins and provide optimal alignments of the sequences. Artificial Intelligence (AI) has recently had. Technological advances in multi-omics (genomics, transcriptomics, and proteomics) have led to a deluge of molecular data from a rapidly growing number of biological sequence samples. 13 Multiple Sequence Alignment 5 Homology Search Tools (pdf) 9, Some other ways of formulation 6 A Note on. The rapid increase in biological data dimension is a challenge for traditional computational analysis methods.
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