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Artificial Intelligence Tools For Automatic Single Molecule Analysis

Artificial Intelligence Tools For Automatic Single Molecule Analysis
用于自动单分子分析的人工智能工具
批准号:
BB/R022143/1
负责人:
Richard Barrett-Jolley
金额:
$19.18万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
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英文摘要
*What will we do?*Develop and distribute an "artificial intelligence" application to allow fellow scientists to analyze a type of data ("single molecule data") difficult to analyse with existing methods. *Machines can learn, but they take a lot of training*Machines can recognise speech in devices from Amazon's "Alexa" to call centres using artificial intelligence ("AI"). As an example, just ask Alexa what "AI" is, and she will tell you. This has only been possible recently as computers have become sufficiently powerful. The technologies to do it are collectively called "machine learning". One advantage of such "machines" is that they can answer questions unsupervised by people. One limitation is that they require enormous labelled datasets to "learn" in the first place. This is called "training data". We have devised, a "trick" to generate massive datasets, by playing simulated data into recording apparatus and then recording back the resulting signal. Because we control the entire process the data is inherently "labelled" in the way necessary to train intelligent machines. With this technique together with the use of Google Brain's freely available "TensorFlow" AI library, we can create applications that analyze data for us.*Why "Single Molecules"*Many molecules found in animal cells behave as switches. Their individual "on" or "off" state can then be measured as either pulses of light or electrical current giving real-time mechanistic insight. They are important throughout biology with the estimated Global market for drugs targeting one family of these molecular switches (ion channels) alone being $11.5bn. The flip-side is that experiments measuring these "switches" generate big datasets that are difficult and laborious to analyse.*Could single molecule biology contribute to tackling diseases of age, climate change, anti-bacterial resistance and global terrorism?*In short "Yes": Ion channel malfunction in particular, is responsible for many age-related diseases. Interest from Pharma is enormous because they are targets for many drugs from sedatives to heart medicines. Certain insecticides act via their ion channels, but these are toxic to people too. One such agent, the nerve toxin "VX" hit the news when it was used in the assassination of Kim Jong-Nam. Even the relatively safe insect repellent citronella repels mosquitoes by activating ion channels. Permethrin-resistant mosquitoes are resistant because they have a specific mutation in an ion channel creating a real problem in malaria control. Plants too express a range of ion channels with critical roles including salt regulation. Since climate change is increasing the salination of many agricultural regions, there is keen interest in whether biological modification of root ion channels could promote survival of crops in salt-rich soils. Ion channels have also been studied in synthetic biology because they can be activated by chemicals at concentrations far lower than that of other sensors. Many uses have been proposed for these, such as detection of explosives, biological weapons, narcotics or certain diseases. A recent discovery is that bacteria also "talk" to each other by an ion channel dependent biofilm communication network that is necessary for their survival. This has raised the possibility that ion channel blocking drugs could constitute a new generation of antibacterials that are less susceptible to resistance. So indeed single molecule biology could contribute to study of several grand challenges in society. In each case, a limiting factor is currently the time required to study the large datasets generated by these molecules. *How can we help?*We will create a simple to use AI-based analysis application to allow rapid analysis of these data. These will be especially useful to industrial partners who produce large data sets during drug development but have few tools available to analyze this fully.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
CVS role of TRPV: from single channels to HRV assessment
TRPV 的 CVS 作用:从单一通道到 HRV 评估
DOI: --
发表时间: 2018
期刊: FASEB JOURNAL
影响因子: 4.8
作者: [O'Brien Fiona]
通讯作者: O'Brien Fiona
DOI: 10.1371/journal.pone.0267452
发表时间: 2022
期刊: PloS one
影响因子: 3.7
作者: [Ball STM, Celik N, Sayari E, Abdul Kadir L, O'Brien F, Barrett-Jolley R]
通讯作者: Barrett-Jolley R
Comparison of Deep Learning Models for Fully Automated Single Channel Idealization
全自动单通道理想化深度学习模型的比较
DOI: --
发表时间: 2021
期刊: BIOPHYSICAL JOURNAL
影响因子: 3.4
作者: [Ball Sam]
通讯作者: Ball Sam
DOI: 10.1101/2020.01.09.899898
发表时间: 2020
期刊:
影响因子: --
作者: [Haidar O]
通讯作者: Haidar O
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