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首页> 外文期刊>Genomics >iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families
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iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families

机译：INR-2L：通过Chou的5步骤规则和一般PSEAAC开发了一种基于两个基于序列的预测因子，用于识别核受体及其家庭

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摘要

Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their subfamilies, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.
机译：核受体蛋白（NRPS）在调节基因表达方面对作用至关重要。随着基因组后时代的NRP迅速增长，强烈推荐从其主要序列中准确地鉴定NRP和它们的子家庭。几种常规方法已被用于歧视NRP及其亚属，但未达到相当大的结果。在续集中，开发了两级新的计算模型“INR-2 L”。两种离散方法即：二肽组合物和三肽组合物用于配制NRPS序列。此外，描述符空间都合并以构建混合空间。此外，采用特征选择技术最小冗余和最大相关性，以便选择突出特征，并降低噪声和冗余。实验结果表明，所提出的INR-2 L模型取得了突出的结果。预计建议的计算模型可能是学术界和研究界的实用而有效的工具。

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