|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|466281||697819||2016||15 صفحه PDF||سفارش دهید||دانلود کنید|
• Micro/nanoscale devices that detect biological targets especially for early cancer diagnosis generate large datasets.
• Challenges like high background noise, slow analysis, and low signal-to-noise ratio make data analysis significantly tedious.
• A novel algorithm is implemented on GPU that automates the rapid detection of biological targets in larger datasets.
• The machine-learning approach records events, computes features and classifies future pulses into their respective types.
• The approach detects cells with an accuracy of 70% and demonstrates a speedup of 3-4X over serial implementation.
Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis.
Journal: Computer Methods and Programs in Biomedicine - Volume 134, October 2016, Pages 53–67