Extreme Learning Machine for Protein Subcellular Localization from Primary Sequence
نویسندگان
چکیده
منابع مشابه
SubCellProt: Predicting Protein Subcellular Localization Using Machine Learning Approaches
High-throughput genome sequencing projects continue to churn out enormous amounts of raw sequence data. However, most of this raw sequence data is unannotated and, hence, not very useful. Among the various approaches to decipher the function of a protein, one is to determine its localization. Experimental approaches for proteome annotation including determination of a protein's subcellular loca...
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Over the years, large-scale genomic and proteomic efforts have produced large amounts of sequence data. One of the key challenges in the post-genomic era is to predict functions and roles of gene products. Proteins are essential to the structure and function of all living cells, and many of the them are enzymes or subunits of enzymes that catalyze chemical reactions. Other types of proteins pla...
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The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...
متن کاملSequence conserved for subcellular localization.
The more proteins diverged in sequence, the more difficult it becomes for bioinformatics to infer similarities of protein function and structure from sequence. The precise thresholds used in automated genome annotations depend on the particular aspect of protein function transferred by homology. Here, we presented the first large-scale analysis of the relation between sequence similarity and id...
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ژورنال
عنوان ژورنال: Hans Journal of Data Mining
سال: 2013
ISSN: 2163-145X,2163-1468
DOI: 10.12677/hjdm.2013.31002