کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
377672 658811 2015 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering
ترجمه فارسی عنوان
پیش بینی مجتمع پروتئینی از نمودارهای متقابل پروتئین وزن با روش جدید رد و نظارت نشده: خوشه بندی مارکوف پیشرفته تکامل یافته
کلمات کلیدی
الگوریتمهای تکاملی، خوشه بندی مارکف پیشرفت تکاملی، الگوریتم ژنتیک، تجزیه و تحلیل شبکه های بیولوژیکی گسترده، شبکه های متقابل پروتئین و پروتئین وزن، پیش بینی پیچیده پروتئین، ویژگی های عملکرد پروتئین و پروتئین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• EE-MC is a new unsupervised methodology for predicting protein complexes from weighted PPI graphs.
• It is by design able to overcome intrinsic limitations of existing methodologies.
• It outperformed existing methodologies increasing the separation metric by 10–20%.
• 72.58% of the predicted protein complexes in human are enriched for at least one GO function term.

ObjectiveProteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein–protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs.Methods and materialsThe proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms.ResultsUsing public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10–20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term.ConclusionsEE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Artificial Intelligence in Medicine - Volume 63, Issue 3, March 2015, Pages 181–189
نویسندگان
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