کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8850045 | 1618661 | 2017 | 26 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An enhanced topologically significant directed random walk in cancer classification using gene expression datasets
ترجمه فارسی عنوان
یک روش پیشرفته تصادفی جهت بالا رفتن در طبقه بندی سرطان با استفاده از مجموعه داده های ژنی بیان شده است
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کلمات کلیدی
الگوریتم راه رفتن تصادفی راننده، پارامتر تنظیم گروه خاص، مسیر
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
بوم شناسی
چکیده انگلیسی
Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Saudi Journal of Biological Sciences - Volume 24, Issue 8, December 2017, Pages 1828-1841
Journal: Saudi Journal of Biological Sciences - Volume 24, Issue 8, December 2017, Pages 1828-1841
نویسندگان
Choon Sen Seah, Shahreen Kasim, Mohd Farhan Md Fudzee, Jeffrey Mark Law Tze Ping, Mohd Saberi Mohamad, Rd Rohmat Saedudin, Mohd Arfian Ismail,