Machine Learning Techniques for High-Throughput Structure and Function Analysis for Proteomics …

Call for Papers With the development of high-throughput sequencing techniques, more and more sequencing data is available, such as genomics reads, transcriptomes data, and proteomics sequences. It is critical to use the data to uncover their structure and function. Genomics function can also be identified from the predicted results, such as motif identification, regulatory regions detection, and even epigenomics and disease relationship prediction. Machine learning methods are important techniques for this task, especially for the ensemble learning, large scale data process, various kernel design, and imbalanced classification methods. We invite authors to contribute original research manuscripts to this special issue, focusing on the advanced machine learning algorithms and their applications in proteomics or genomics sequences analysis. Potential topics include, but are not limited to: Protein structure and function prediction with…


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