نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 کارشناسی ارشد هوش مصنوعی، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، دانشگاه شهید باهنر، کرمان

2 استادیار، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، دانشگاه شهید باهنر، کرمان

چکیده

استنتاج شبکة تنظیم‌کنندة ژن (GRN) با استفاده از داده­های بیان ژن، برای درک وابستگی و نحوة تنظیم ژن‌ها، درک فرآیندهای زیست­شناسی، نحوة رخداد فرآیندها و همچنین جلوگیری از وقوع برخی فرآیندهای ناخواسته (بیماری)، حائز اهمیت است. ساخت صحیح GRN، نیازمند استنتاج صحیح مجموعة پیش­بینی­کننده­ است. به‌طور کلی، مهم‌ترین محدودیت برای استنتاج صحیح مجموعة پیش­بینی­کننده، حجم عظیم ژن­ها، کم بودن تعداد نمونه­ها و امکان نفوذ نویز در داده­های بیان ژن است؛ بنابراین، ارائة روش­هایی کارا برای استنتاج پیش­بینی کننده­ها با قابلیت اطمینان بالا، یک نیاز جدی است. در این مقاله، با استفاده از الگوریتم جستجوی گرانشی(GSA)، یک روش کارا برای استنتاج مجموعة پیش‌بینی‌کننده­ ارائه شده است. به‌ازای هر ژن هدف، یک الگوریتم GSA برای استنتاج زیر‌مجموعة پیش‌بینی کنندة آن ژن استفاده ­شده است. در هر جمعیت، یک جرم نشان دهندة زیر‌مجموعة پیش‌بینی کنندة مرتبط با آن ژن هدف است. جمعیت اولیه به‌ازای هر ژن هدف، براساس ضریب همبستگی پیرسون تولید می­شود. برای هدایت الگوریتم GSA، از معیار ارزیابی میانگین آنتروپی شرطی (MCE) استفاده شده است. نتایج تجربی حاصل از اعمال این روش روی داده­های زیست‌شناسی نشان می­دهد که، روش پیشنهادی دقت بالایی برای استنتاج مجموعة پیش­بینی­کننده دارد. به‌علاوه، نتایج روی داده­های زیست‌شناسی با مقیاس کوچک و بزرگ نشان می­دهند که، میزان دقت روش پیشنهادی برای استنتاج GRN بیشتر از روش­های مشابه  است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The Predictor Set Inference in Gene Regulatory Network Using Gravitational Search Algorithm and Mean Conditional Entropy Fitness Measure

نویسندگان [English]

  • Mina Jafari 1
  • Behnam Ghavami 2
  • Vahid Sattari Naeini 2

1 MSc, Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Assistant Professor, Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

چکیده [English]

The inference of Gene Regulatory Network (GRN) using gene expression data is significantly important in order to understand gene dependencies, regulatory functions among genes, biological processes, way of process occurrence and avoiding some unplanned processes (disease). The accurate inference of GRN needs the accurate inference of predictor set. Generally, the main limitations of the predictor set inference are the small number of samples, the large number of genes and also the possibility influence of noise in gene expression data. Hence, providing efficient methods to infer predictor set with high reliability is a serious need. In this paper, an efficient method is proposed to infer predictor set using Gravitational Search Algorithm (GSA). A GSA is used for each target gene to infer the predictor subset of the gene. In a population, a mass represents a predictor subset of the associated gene. The initial population per target gene is generated by Pearson Correlation Coefficient (PCC). In order to guide the GSA, Mean Conditional Entropy (MCE) is used as the assessment criterion. Experimental results show that the proposed method has a good ability to infer the predictor set with high reliability. In addition, we also compared the proposed algorithm with a recent similar method based on genetic algorithm. Comparison results reveal the advantage of the proposed algorithm on biological datasets with small data volumes and large network scales.

کلیدواژه‌ها [English]

  • Gene Regulatory Network (GRN)
  • Genomics
  • Mean Conditional Entropy (MCE)
  • Gravitational Search Algorithm (GSA)
  • Pearson Correlation Coefficient
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