An accelerated framework for the classification of biological targets from solid-state micropore data.

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.

GPU-based real-time detection and analysis of biological targets using solid-state nanopores.

The emergence of nanoscale devices has provided robust interfaces to biomolecules that faithfully transduce and define fundamental interactions of living systems. Measuring single-event behavior of important targets like DNA, and diseased cells has been achieved with a number of devices and systems. An important dimension to these systems, often discounted, is real-time computational decision-making from measured data. This paper describes an adaptive approach that can record single-molecule or single-cell events in real-time and automatically analyze patterns from the measured data. The automated analysis of measured data is done using a static threshold technique and two variations of a dynamic threshold technique: baseline-tracker and moving average filtering. Dynamic techniques for threshold detection enable noise suppression in the measured data and precise detection of patterns, but at the cost of more complex software as compared to static technique. To mitigate the computational overhead, a real-time system is implemented that uses advanced I/O techniques to minimize the execution stalls, thus enabling the system to process data significantly faster than the electrical measurement setup. Furthermore, the algorithms are implemented on programmable graphics processing units for parallel pattern detection. Our implementation provides five times faster data acquisition and pattern detection than the maximum sampling rate of the electrical measurement setup.