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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Related Experiment Video

Updated: Jul 29, 2025

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
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Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks.

Prateek Tripathi1, Costanza Gulli1, Joseph Broomfield2

  • 1Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2AZ, London, UK.

Computers in Biology and Medicine
|May 21, 2023
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Summary

This study introduces a novel AI-powered lab-on-chip system for rapid molecular diagnostics. It uses ISFET sensors and deep learning to detect DNA/RNA, enabling faster infectious disease and cancer biomarker identification.

Keywords:
CMOSCNNsConvolutional neural networksISFETIon-sensitive field effect transistorsLab-on-chipMolecular diagnosticsNucleic acid amplificationSpectrograms

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Area of Science:

  • Biotechnology
  • Artificial Intelligence
  • Molecular Diagnostics

Background:

  • The COVID-19 pandemic revealed critical needs in molecular diagnostics for rapid, private, and secure solutions.
  • Existing methods often lack the speed and portability required for point-of-care applications.
  • There is a growing demand for AI-driven edge computing solutions in healthcare.

Purpose of the Study:

  • To present a proof-of-concept for detecting nucleic acid amplification using ISFET sensors and deep learning.
  • To develop a low-cost, portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers.
  • To enhance molecular diagnostic capabilities through AI-based edge solutions.

Main Methods:

  • Utilized Ion-Sensitive Field-Effect Transistor (ISFET) sensors for nucleic acid detection.
  • Applied deep learning, specifically 2D convolutional neural networks, for signal classification.
  • Transformed sensor signals into spectrograms (time-frequency domain) for improved AI compatibility and performance.

Main Results:

  • Achieved reliable classification of chemical signals using image processing techniques on spectrograms.
  • Demonstrated significant performance improvement compared to time-domain data analysis.
  • Developed a deep learning model with 84% accuracy and a compact 30kB size, suitable for edge devices.

Conclusions:

  • The developed method enables rapid and reliable molecular diagnostics on a portable platform.
  • Spectrogram transformation enhances AI model performance for chemical signal analysis.
  • This approach paves the way for intelligent, AI-enabled lab-on-chip systems for advanced diagnostics.