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....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 6; pp. 605 - 616 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104451357&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104451357 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2012 vid: 50 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104451357 NLM22447368 2011562924 10.1007/s11517-012-0893-9 NLM22447368 104451357 ppf: 605 ppct: 11 formats: fmt: @attributes: type: P tig: atl: GPU-based real-time detection and analysis of biological targets using solid-state nanopores. aug: au: Hafeez A Asghar W Rafique MM Iqbal SM Butt AR Hafeez, Abdul Asghar, Waseem Rafique, M Mustafa Iqbal, Samir M Butt, Ali R affil: Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, USA sug: subj: Diagnosis, Computer Assisted Methods Nanotechnology Algorithms Nanomedicine Equipment and Supplies Nanomedicine Methods Information Science Methods Signal Processing, Computer Assisted ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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