Author Description

In recent years, computational methods have been employed extensively in bioinformatics researches, including protein classification, motif detection, phylogenetics tree reconstruction, traditional Chinese medicine analysis, etc. In term of the explosion of various biology omics data, traditional bioinformatics algorithms fall behind the big data era. Big data computational techniques have advanced quickly over the past few years. Several novel methods were reported in the scholar and industry fields. For example, affinity propagation was published in Science as a novel clustering algorithm, and deep learning has become a hot topic in the predictions and classifications which is capable of processing big data. Parallel mechanisms, such as Spark and Hadoop, are also developed to speed up the algorithms. Computer scientists devote themselves to the advanced large scale data analysis. However, the applications on bioinformatics and genomics are limited and fall behind the techniques. This thematic volume is a collection of reviews of recent large-scale computational techniques in genomics applications. This reference represents an exchange of ideas from different researchers along with their genomics data. The topics covered in this book include: - Ancestral Genome Reconstruction on Whole Genome Level - Predicting Protein Submitochondrial Locations: The 10th Anniversary - Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites