کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8646166 | 1570072 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
PCD-DPPI: Protein complex detection from dynamic PPI using shuffled frog-leaping algorithm
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
Protein Protein InteractionDCMPPIBiclusteringMIPSDIPACCUSNSWCCpositive predictive value - ارزش پیش بینی مثبتShuffled frog leaping algorithm - الگوریتم شلاق زدن قورباغهRecall - به خاطر آوردنGene expression - بیان ژنTandem affinity purification - تصفیه وابستگی دو طرفهSensitivity - حساسیتAccuracy - دقتPrecision - دقت Jaccard index - شاخص ژاکارتTAP - ضربه زدنDatabase of Interacting Proteins - پایگاه داده پروتئین های تعاملProtein complex - پروتئین پیچیده
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
ژنتیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In the post-genome period, the identification of a protein complex from large PPI (protein-protein interaction) networks is a challenging task. Protein complexes plays a vital character in numerous molecular processes of the cell. Although, the maximum computational techniques of protein complex detection have aimed at static PPI networks that cannot represent the logical dynamicity of protein interactions. In recent times, the dynamicity of PPI networks has been exploited by building a set of dynamic PPI subnetworks with respect to every time-point in a gene expression matrix. This paper presents a new technique, called Protein Complex Detection based on Dynamic PPI (PCD-DPPI) to accomplish dynamicity in protein complex detection. The initial stage of the proposed technique is based on shuffled frog-leaping algorithm, an Optimization technique that take out a few sets of genes that are co-regulated beneath some conditions from the input gene expression matrix. An individual extracted gene set is defined as a bicluster. In the second stage, depending on the biclusters, few dynamic PPI subnetworks are taken out from the input static PPI network. By employing a complex detection approach on every dynamic PPI subnetworks, protein complexes are identified and the result is aggregated. The proposed and existing algorithms were applied to various datasets such as DIP, Krogan “Extended”, Krogan “Core”, Gavin2, Gavin6, PPI 1, Collins and Gavinâ¯+â¯Krogan. Experimental results have been proved that the proposed PCD-DPPI efficiently depicts the dynamicity characteristics in static PPI networks and attains expressively better results when compared to other existing standard methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Gene Reports - Volume 12, September 2018, Pages 89-98
Journal: Gene Reports - Volume 12, September 2018, Pages 89-98
نویسندگان
S. Janani, D. Ramyachitra, R. Ranjani Rani,