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Despite our understanding of the many factors that promote ventricular arrhythmias, it remains difficult to predict which specific individuals within a population will be especially susceptible to these events. We present a computational framework that combines supervised machine learning algorithms with population-based cellular mathematical modeling. Using this approach, we identify electrophysiological signatures that classify how myocytes respond to three arrhythmic triggers. Our predictors significantly outperform the standard myocyte-level metrics, and we show that the approach provides insight into the complex mechanisms that differentiate susceptible from resistant cells. Overall, our pipeline improves on current methods and suggests a proof of concept at the cellular level that can be translated to the clinical level.

At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individuals susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variants ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (IKr Block), 2) augmentation of the L-type calcium current (ICaL Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to IKr Block or ICaL Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.

Author contributions: M.V. and E.A.S. designed research; M.V., X.M., and E.A.S. performed research; M.V., X.M., and E.A.S. analyzed data; and M.V. and E.A.S. wrote the paper.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2104019118/-/DCSupplemental.

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Prediction of arrhythmia susceptibility through mathematical modeling and machine learning - pnas.org

This article is part of ourreviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.

The last decades growing interest in deep learning was triggered by the proven capacity of neural networks in computer vision tasks. If you train a neural network with enough labeled photos of cats and dogs, it will be able to find recurring patterns in each category and classify unseen images with decent accuracy.

What else can you do with an image classifier?

In 2019, a group of cybersecurity researchers wondered if they could treat security threat detection as an image classification problem. Their intuition proved to be well-placed, and they were able to create a machine learning model that could detect malware based on images created from the content of application files. A year later, the same technique was used to develop a machine learning system that detects phishing websites.

The combination of binary visualization and machine learning is a powerful technique that can provide new solutions to old problems. It is showing promise in cybersecurity, but it could also be applied to other domains.

The traditional way to detect malware is to search files for known signatures of malicious payloads. Malware detectors maintain a database of virus definitions which include opcode sequences or code snippets, and they search new files for the presence of these signatures. Unfortunately, malware developers can easily circumvent such detection methods using different techniques such as obfuscating their code or using polymorphism techniques to mutate their code at runtime.

Dynamic analysis tools try to detect malicious behavior during runtime, but they are slow and require the setup of a sandbox environment to test suspicious programs.

In recent years, researchers have also tried a range of machine learning techniques to detect malware. These ML models have managed to make progress on some of the challenges of malware detection, including code obfuscation. But they present new challenges, including the need to learn too many features and a virtual environment to analyze the target samples.

Binary visualization can redefine malware detection by turning it into a computer vision problem. In this methodology, files are run through algorithms that transform binary and ASCII values to color codes.

In a paper published in 2019, researchers at the University of Plymouth and the University of Peloponnese showed that when benign and malicious files were visualized using this method, new patterns emerge that separate malicious and safe files. These differences would have gone unnoticed using classic malware detection methods.

According to the paper, Malicious files have a tendency for often including ASCII characters of various categories, presenting a colorful image, while benign files have a cleaner picture and distribution of values.

When you have such detectable patterns, you can train an artificial neural network to tell the difference between malicious and safe files. The researchers created a dataset of visualized binary files that included both benign and malign files. The dataset contained a variety of malicious payloads (viruses, worms, trojans, rootkits, etc.) and file types (.exe, .doc, .pdf, .txt, etc.).

The researchers then used the images to train a classifier neural network. The architecture they used is the self-organizing incremental neural network (SOINN), which is fast and is especially good at dealing with noisy data. They also used an image preprocessing technique to shrink the binary images into 1,024-dimension feature vectors, which makes it much easier and compute-efficient to learn patterns in the input data.

The resulting neural network was efficient enough to compute a training dataset with 4,000 samples in 15 seconds on a personal workstation with an Intel Core i5 processor.

Experiments by the researchers showed that the deep learning model was especially good at detecting malware in .doc and .pdf files, which are the preferred medium for ransomware attacks. The researchers suggested that the models performance can be improved if it is adjusted to take the filetype as one of its learning dimensions. Overall, the algorithm achieved an average detection rate of around 74 percent.

Phishing attacks are becoming a growing problem for organizations and individuals. Many phishing attacks trick the victims into clicking on a link to a malicious website that poses as a legitimate service, where they end up entering sensitive information such as credentials or financial information.

Traditional approaches for detecting phishing websites revolve around blacklisting malicious domains or whitelisting safe domains. The former method misses new phishing websites until someone falls victim, and the latter is too restrictive and requires extensive efforts to provide access to all safe domains.

Other detection methods rely on heuristics. These methods are more accurate than blacklists, but they still fall short of providing optimal detection.

In 2020, a group of researchers at the University of Plymouth and the University of Portsmouth used binary visualization and deep learning to develop a novel method for detecting phishing websites.

The technique uses binary visualization libraries to transform website markup and source code into color values.

As is the case with benign and malign application files, when visualizing websites, unique patterns emerge that separate safe and malicious websites. The researchers write, The legitimate site has a more detailed RGB value because it would be constructed from additional characters sourced from licenses, hyperlinks, and detailed data entry forms. Whereas the phishing counterpart would generally contain a single or no CSS reference, multiple images rather than forms and a single login form with no security scripts. This would create a smaller data input string when scraped.

The example below shows the visual representation of the code of the legitimate PayPal login compared to a fake phishing PayPal website.

The researchers created a dataset of images representing the code of legitimate and malicious websites and used it to train a classification machine learning model.

The architecture they used is MobileNet, a lightweight convolutional neural network (CNN) that is optimized to run on user devices instead of high-capacity cloud servers. CNNs are especially suited for computer vision tasks including image classification and object detection.

Once the model is trained, it is plugged into a phishing detection tool. When the user stumbles on a new website, it first checks whether the URL is included in its database of malicious domains. If its a new domain, then it is transformed through the visualization algorithm and run through the neural network to check if it has the patterns of malicious websites. This two-step architecture makes sure the system uses the speed of blacklist databases and the smart detection of the neural networkbased phishing detection technique.

The researchers experiments showed that the technique could detect phishing websites with 94 percent accuracy. Using visual representation techniques allows to obtain an insight into the structural differences between legitimate and phishing web pages. From our initial experimental results, the method seems promising and being able to fast detection of phishing attacker with high accuracy. Moreover, the method learns from the misclassifications and improves its efficiency, the researchers wrote.

[architecture]

I recently spoke to Stavros Shiaeles, cybersecurity lecturer at the University of Portsmouth and co-author of both papers. According to Shiaeles, the researchers are now in the process of preparing the technique for adoption in real-world applications.

Shiaeles is also exploring the use of binary visualization and machine learning to detect malware traffic in IoT networks.

As machine learning continues to make progress, it will provide scientists new tools to address cybersecurity challenges. Binary visualization shows that with enough creativity and rigor, we can find novel solutions to old problems.

Original post:
Computer vision and deep learning provide new ways to detect cyber threats - TechTalks

Back view of a senior professor talking on a class to large group of students.

AI is becoming strategic for many companies across the world.The technology can be transformative for just about any part of a business.

But AI is not easy to implement.Even top-notch companies have challenges and failures.

So what can be done?Well, one strategy is to provide AI education to the workforce.

If more people are AI literate and can start to participate and contribute to the process, more problemsboth big and smallacross the organization can be tackled, said David Sweenor, who is the Senior Director of Product Marketing at Alteryx.We call this the Democratization of AI and Analytics. A team of 100, 1,000, or 5,000 working on different problems in their areas of expertise certainly will have a bigger impact than if left in the hands of a few.

Just look at Levi Strauss & Co.Last year the company implemented a full portfolio of enterprise training programsfor all employees at all levelsfocused on data and AI for business applications.For example, there is the Machine Learning Bootcamp, which is an eight-week program for learning Python coding, neural networks and machine learningwith an emphasis on real-world scenarios.

Our goal is to democratize this skill set and embed data scientists and machine learning practitioners throughout the organization, said Louis DeCesari, who is the Global Head of Data, Analytics, and AI at Levi Strauss & Co.In order to achieve our vision of becoming the worlds best digital apparel company, we need to integrate digital into all areas of the enterprise.

Granted, corporate training programs can easily become a waste.This is especially the case when there is not enough buy-in at the senior levels of management.

It is also important to have a training program that is more than just a bunch of lectures.You need to have outcomes-based training, said Kathleen Featheringham, who is the Director of Artificial Intelligence Strategy at Booz Allen.Focus on how AI can be used to push forward the mission of the organization, not just training for the sake of learning about AI. Also, there should be roles-based training.There is no one-size-fits-all approach to training, and different personas within an organization will have different training needs.

AI training can definitely be daunting because of the many topics and the complex concepts.In fact, it might be better to start with basic topics.

A statistics course can be very helpful, said Wilson Pang, who is the Chief Technology Officer at Appen.This will help employees understand how to interpret data and how to make sense of data. It will equip the company to make data driven decisions.

There also should be coverage of how AI can go off the rails.There needs to be training on ethics, said Aswini Thota, who is a Principal Data Scientist at Bose Corporation.Bad and biased data only exacerbate the issues with AI systems.

For the most part, effective AI is a team sport.So it should really involve everyone in an organization.

The acceleration of AI adoption is inescapablemost of us experience AI on a daily basis whether we realize it or not, said Alex Spinelli, who is the Chief Technology Officer at LivePerson.The more companies educate employees about AI, the more opportunities theyll provide to help them stay up-to-date as the economy increasingly depends on AI-inflected roles. At the same time, nurturing a workforce thats ahead of the curve when it comes to understanding and managing AI will be invaluable to driving the companys overall efficiency and productivity.

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL.

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Artificial Intelligence: Should You Teach It To Your Employees? - Forbes

This article was originally published here

PLoS One. 2021 Sep 9;16(9):e0256834. doi: 10.1371/journal.pone.0256834. eCollection 2021.

ABSTRACT

The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.

PMID:34499662 | DOI:10.1371/journal.pone.0256834

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Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface - DocWire News