Signal Processing and Applied Machine Learning for the Detection of Nucleic Acids

Lead Research Organisation: Imperial College London
Department Name: Electrical and Electronic Engineering

Abstract

The rapid detection of microorganisms using DNA-based solutions has positive, far-reaching consequences on society for curing diseases. Unfortunately, current DNA-based methods are expensive, non-portable and require a technically trained operator. These challenges can be tackled from three different angles: (i) improving and developing novel detection chemistries; (ii) introducing new engineering solutions (such as semiconductor-based chemical imaging devices); and (iii) developing signal processing and machine learning algorithms in order to extract as much information from the data as possible.

The primary aim of this project focuses on the latter: addressing the role of signal processing, machine learning and data science for enhancing DNA-based solutions for problems in healthcare. Examples of such problems include: detecting pathogens, quantifying how much of a pathogen exists, and detecting multiple pathogens in a single chemical reaction. The goal is to touch on these issues using data obtained from: standard instruments, state-of-the-art instruments, and then tackling new data obtained from semiconductor-based chemical imaging devices. Although the novelty of this work lies in developing new algorithms for extracting information from data (obtained from DNA-based experiments), the benefits of this project inherently extend to those enhancing detection chemistries and developing new engineering solutions.

Research Areas
Artificial intelligence technologies, Digital signal processing and Mathematical Biology

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509486/1 01/10/2016 30/09/2021
2029408 Studentship EP/N509486/1 01/10/2017 30/06/2021 Ahmad Moniri
 
Title A METHOD FOR ANALYSIS OF REAL-TIME AMPLIFICATION DATA 
Description This disclosure relates to methods, systems, computer programs and computer- readable media for the multidimensional analysis of real-time amplification data. A framework is presented that shows that the benefits of standard curves extend beyond absolute quantification when observed in a multidimensional environment. Relating to the field of Machine Learning, the disclosed method combines multiple extracted features (e.g. linear features) in order to analyse real-time amplification data using a multidimensional view. The method involves two new concepts: the multidimensional standard curve and its 'home', the feature space. Together they expand the capabilities of standard curves, allowing for simultaneous absolute quantification, outlier detection and providing insights into amplification kinetics. The new methodology thus enables enhanced quantification of nucleic acids, single-channel multiplexing, outlier detection, characteristic patterns in the multidimensional space related to amplification kinetics and increased robustness for sample identification and quantification. 
IP Reference WO2019234247 
Protection Patent application published
Year Protection Granted 2019
Licensed No
Impact -
 
Title DEVICES AND METHOD FOR DETECTING AN AMPLIFICATION EVENT 
Description A method is disclosed herein for detecting an amplification reaction in a solution containing a biological sample using an array of ion sensors. The amplification reaction is indicative of the presence of a nucleic acid. The method comprises monitoring a signal from each respective sensor of the array of ion sensors, detecting a change in the signal from a first sensor of the array of ion sensors, and comparing the signal from the first sensor with the signal of at least one neighbouring sensor, the at least one neighbouring sensor being proximate to the first sensor in the array. The method further comprises determining, based on the comparing, that an amplification event has occurred in the solution in the vicinity of the first sensor. 
IP Reference WO2019234451 
Protection Patent application published
Year Protection Granted 2019
Licensed No
Impact -