دانلود پروژه ها و تحقیق های دانشجویی

مقاله Improved Technique for Automated Classification of Protein Subcellular Location Patterns inFluorescence Microscope Images تحت word دارای 5 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است

فایل ورد مقاله Improved Technique for Automated Classification of Protein Subcellular Location Patterns inFluorescence Microscope Images تحت word کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.

این پروژه توسط مرکز مرکز پروژه های دانشجویی آماده و تنظیم شده است

توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله Improved Technique for Automated Classification of Protein Subcellular Location Patterns inFluorescence Microscope Images تحت word ،به هیچ وجه بهم ریختگی وجود ندارد

بخشی از متن مقاله Improved Technique for Automated Classification of Protein Subcellular Location Patterns inFluorescence Microscope Images تحت word :

سال انتشار: 1389

محل انتشار: هفدهمین کنفرانس مهندسی پزشکی ایران

تعداد صفحات: 5

چکیده:

The genomic revolution promises a complete understanding of the mechanisms by which cells and tissuescarry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. Knowledge of a protein’s subcellular distribution can contribute to a complete understanding of its function. Processing of subcellular image sets is still mostly manual and it causes the process inefficient and error-prone. But in recent years, try to perform high-resolution; high-throughputanalysis for ten thousands of expressed proteins in the many cell types and cellular conditions under which they may be found creates. In this review, we describe a systematic approach for interpreting protein subcellular distributions using modified threshold adjacency statistics (MTAS) set of Subcellular Location Features (SLF). Previous work that uses threshold adjacency statistics (TAS), introduces a set of Subcellular Location Featureswhich are computed by counting the number of threshold pixels adjacent. But here a novel method has been used that determines a modified features set, to improve the recognition of protein subcellular location patterns in 2D fluorescence microscope images with high accuracy and high speed.