【05/25專題演講】國立政治大學資訊科學系張家銘助理教授-Computational protein function prediction
＊講者：張家銘助理教授(國立政治大學資訊科學系)
＊題目：Computational protein function prediction
＊時間：109年5月25日(一)15：30-17：00
＊地點：民生校區五育樓4樓401教室
＊摘要：
Biological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments. We have developed, GODoc, a novel and effective strategy to incorporate a training procedure into the k-nearest neighbor algorithm (instance-based learning) which is capable of solving the Gene Ontology (GO) multiple-label prediction problem, which is especially notable given the thousands of GO terms. In the CAFA3 competition (68 teams), GODoc ranks 10th in Cellular Component Ontology. In the term-centric task, GODoc performs third and is tied for first for the biofilm formation of Pseudomonas aeruginosa and the long-term memory of Drosophila melanogaster, respectively. Besides GO prediction, we present PSLCNN, a model using deep neural networks to predict protein subcellular localization for eukaryotes and prokaryotes. Compared with the state-of-the-art tools, PSLCNN achieves the best performance for prokaryotes and is comparable for eukaryotes.
＊題目：Computational protein function prediction
＊時間：109年5月25日(一)15：30-17：00
＊地點：民生校區五育樓4樓401教室
＊摘要：
Biological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments. We have developed, GODoc, a novel and effective strategy to incorporate a training procedure into the k-nearest neighbor algorithm (instance-based learning) which is capable of solving the Gene Ontology (GO) multiple-label prediction problem, which is especially notable given the thousands of GO terms. In the CAFA3 competition (68 teams), GODoc ranks 10th in Cellular Component Ontology. In the term-centric task, GODoc performs third and is tied for first for the biofilm formation of Pseudomonas aeruginosa and the long-term memory of Drosophila melanogaster, respectively. Besides GO prediction, we present PSLCNN, a model using deep neural networks to predict protein subcellular localization for eukaryotes and prokaryotes. Compared with the state-of-the-art tools, PSLCNN achieves the best performance for prokaryotes and is comparable for eukaryotes.
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