Category Archives: Machine Learning

GuiltyTargets-COVID-19 web tool

We start by providing a high level overview about the capabilities of the GuiltyTargets-COVID-19 web tool. The web application initially allows the user to browse through a ranked list of potential targets generated using six bulk RNA-Seq and three single cell RNA-Seq datasets applied to a lung specific proteinprotein interaction (PPI) network reconstruction. Our website is also equipped with several filtering options to allow the user to quickly obtain the most relevant results. The candidate targets were ranked using a machine learning algorithm, GuiltyTargets19, which aims to quantify the degree of similarity of a candidate target to other known (candidate) drug targets. Further details about GuiltyTargets are outlined in the Methods section of this paper.

The user can retrieve a consensus ranking of any combination of datasets desired (Fig. 1). For each protein listed, its level of differential gene expression (upregulated, downregulated, no differential expressed) is displayed using a color coding system in addition to its association with COVID-19 as described in the literature. This latter feature is accomplished using an automated web search of scientific articles from PubMed that mention the protein in combination with COVID-19.

Though we provide nine different RNA-Seq datasets to explore, our tool also allows one to upload their own gene expression data. Uploaded data is sent through the GuiltyTargets algorithm and, after a short period of time, a ranking of candidate proteins is made available to the user to download and explore.

To further elucidate their linkage to known disease mechanisms, GuiltyTargets-COVID-19 enables one to explore the neighborhood of any given candidate target within the lung tissue specific PPI network reconstruction (Fig. 2). The network is labeled with information about known disease associations in humans in addition to virus-host interactions.

Importantly, in order to present the user with a list of possible drug candidates for a given protein, we parsed the ChEMBL database to generate a mapping of known ligands for each of the prioritized proteins and included this information in our web application. Direct links to the ligands description pages were added to GuiltyTargets-COVID-19 so that researchers can quickly explore the each compounds profile.

To point out potential target related safety issues, GuiltyTargets-COVID-19 includes a list of adverse effects for each target-linked compound, all of which were derived from the NSIDES database20. By making this information readily available, the user can quickly decide which compounds for a given target are most viable.

Altogether, GuiltyTargets-COVID-19 implements a comprehensive workflow involving computational target prioritization supplemented with annotations from several key databases.

Screenshot of the GuiltyTargets-COVID-19 web application available at https://guiltytargets-covid.eu/.

In the following sections, we demonstrate the utility of GuiltyTargets-COVID-19 based on the analysis of 6 bulk RNA-Seq and 3 single cell RNA-Seq datasets. A detailed overview of the data and workflow can be found in the Differential gene expression section of the Methods. In brief, GuiltyTargets-COVID-19 maps differentially expressed genes in each of these datasets to a lung tissue specific, genome-wide PPI network, which was constructed using data from BioGRID21, IntAct22 and STRING23 (see PPI Network Construction in Methods). Users can choose a combination of these datasets and the tool will present a ranking of each protein for each selected dataset based on its similarity to known drug targets. Additionally, a consensus ranking is also calculated if multiple datasets were selected.

For our analysis, we initially performed a ranking for each individual dataset. This ranking was performed using the GuiltyTargets positive-unlabeled machine learning algorithm19, which combines a PPI network, a differential gene expression (DGE) dataset, and a list of included nodes that are labeled as putative targets. Based on these results, GuiltyTargets then quantifies the probability that a candidate protein could be labeled as a target as well. In order to create a usable model, GuiltyTargets-COVID-19 was trained using a set of 218 proteins targeted by small compounds extracted from ChEMBL. This set of proteins was previously found to be involved in cellular response mechanisms specific to COVID-19 that have been shown to be transcriptionally dysregulated in several bulk RNA-Seq datasets15. The set of 218 proteins may thus be regarded as an extendable set of candidate targets. We chose this approach as there are currently very few approved drugs for COVID-19 (7 as of December 2022 in the European Union), hence making a machine learning model based ranking with respect to only known targets of approved drugs rather questionable.

In order to maximize transparency, GuiltyTargets-COVID-19 also reports the ranking performance of the GuiltyTargets machine learning algorithm that is calculated using the cross-validated area under receiver operator characteristic curve (AUC). As show in Fig. 6, the cross-validated AUCs found for each of the nine datasets used in this work were found to be between 85% and 90%, which align with the results reported in19. Additional details regarding the algorithms performance can be found in the Methods Section.

First degree neighbors of the (a) AKT3 and (b) PIK3CA proteins. Nodes are colored according to their associations: light orange means no virus or human association was found, dark orange indicates only human association, purple signifies viral association, and and dark blue nodes are proteins with associations to both viral mechanisms and human processes. The neighboring proteins and their associations for AKT3 and PIK3CA are outlined in Supplementary Data S1 and S2, respectively.

For our use case, we focused on proteins with a predicted target likelihood higher than 85% in each of the nine datasets. This resulted in 5167 candidate targets for each of the bulk RNA-Seq datasets and 4565 candidate targets for each of the scRNA-Seq datasets. By enabling the filter option novel in our web tool, we can select for those prioritized targets that are not among the original set of 218 proteins labeled as known targets and used for training the model.

Among these prioritized targets, there was a considerable difference between the analyzed bulk RNA-Seq data, with only a single protein target appearing among the top candidates for all 6 datasets: AKT3 (Fig. 3). AKT3 is of great interest in COVID-19 research as the PI3K/AKT signaling pathway plays a central role in cell survival. Moreover, researchers have observed an association between this pathway and coagulopathies in SARS-CoV-2 infected patients24. It has been suggested that the PI3K/AKT signaling pathway can be over-activated in COVID-19 patients either by direct or indirect mechanisms, thus suggesting this pathway may serve as a potential therapeutic target25.

To better understand the relationship of AKT3 with known COVID-19 disease mechanisms, the user can also download a CSV file comprised of the direct (first-degree) neighbors of AKT3 in the lung tissue specific PPI network used for our analysis. Each first-degree neighbor is additionally annotated to indicate whether the corresponding protein is associated with either the disease or with the virus itself. Figure 2a provides a visualization of the AKT3 neighbor network generated using Cytoscape 3.9.126.

Interestingly, a larger number of shared prioritized protein targets can be found among the scRNA-Seq data. Based on the 17 cell types identified in the three datasets, four common target candidates were identified: AKT2, AKT3, MAPK11, and MLKL. The presence of AKT3, as well as its isoform AKT2, in our list of prioritized targets supports the predicted association of the PI3K/AKT signaling pathway with COVID-19 as observed in our analysis of the bulk RNA datasets. Interestingly, our analysis of the single-cell datasets revealed two additional proteins of interest, MAPK11 and MLKL. MAPK11 is targeted by the compound losmapimod, which was tested against COVID-19 in a (terminated) phase III clinical trial (NCT04511819). The trial ended in August 2021 due to the rapidly evolving environment for the treatment of Covid-19 and ongoing challenges to identify and enroll qualified patients to participate (https://clinicaltrials.gov/ct2/show/NCT04511819). MLKL is a pseudokinase that plays a key role in TNF-induced necroptosis, a programmed cell death process. Recent evidence suggests that it can become dysregulated by the inflammatory response due to SARS-CoV-2 infection27. According to the DGldb database28 (which is cross-referenced by GuiltyTargets-COVID-19), the protein is also druggable and thus may serve as a therapeutic target.

Overall, these results demonstrate that GuiltyTargets-COVID-19 has the capability of identifying candidate targets with a clear disease association as well as assessing their potential druggability.

Venn diagram of the number of prioritized targets from the bulk RNA-Seq datasets.

After analyzing the top ranked protein targets shared by each group of RNA-Seq data, we next sought to characterize those candidates found in unique cell types (Table 1). Interestingly, we found that PIK3CA was only ranked among the top therapeutic candidates in goblet cells. Goblet cells are modified epithelial cells that secrete mucus on the surface of mucous membranes of organs, particularly those of the lower digestive tract and airways. Dactolisib is a compound targeting PIK3CA that has been tested in a phase II clinical trial for its ability to reduce COVID-19 disease severity (NCT04409327). The trial was terminated due to an insufficient accrual rate (https://clinicaltrials.gov/ct2/show/NCT04409327). Figure 2b depicts the PIK3CA protein and its first-degree neighbors as defined by the PPI network used in the GuiltyTargets-COVID-19 algorithm.

Another interesting drug we identified during our analysis is the compound varespladib, a compound that is currently being tested in a phase II clinical trial (NCT04969991) and which targets PLA2G2A, a potential protein target that primarily affects NKT cells (Table 1). To better support the user in finding more information about the disease context of such candidate targets, GuiltyTargets-COVID-19 also includes links to PubMed articles in which the protein and its roles in COVID-19 are discussed. Identification of relevant articles is discussed in the the Methods section.

Altogether, these results demonstrate that the tool presented here can be used for cell type specific target prioritization as well as aiding in characterizing the proteins in the context of COVID-19.

GuiltyTargets-COVID-19 also includes a feature for identifying small compound ligands from the ChEMBL database with reported activity (pChEMBL > 5) against candidate targets. In our use case, we were able to identify 186 ligands for AKT3, the top prioritized target across bulk RNA-Seq datasets. Furthermore, 126 ligands were mapped to the four candidate targets that were found among all single cell RNA-Seq datasets. A complete report of the number of ligands mapped to protein targets unique for a given cell type can be found in Table 2. We observed a high imbalance of mapped ligands for different cell types with secretory cells being targeted by the vast majority of compounds.

In total, these results demonstrate the ability of GuiltyTargets-COVID-19 to efficiently identify active ligands against candidate targets, thus supporting researchers in rapidly identifying potential new drugs for therapeutic intervention or repurposing.

An important factor that must be taken into consideration with new target candidates are the adverse events which are associated with the drugs targeting these proteins. To better assess the suggested therapeutics, we mapped significant adverse effects from the NSIDES database (http://tatonettilab.org/offsides) to the extracted ChEMBL compounds. Hence, each protein can be visualized in tandem with the ligands that target it, as well as any side effects found to be associated with the linked compounds. To showcase this feature, Fig. 4 depicts the AKT3 protein as well as its associated ligands and their side effects as shown in the GuiltyTargets-COVID-19 web application.

Screenshot of part of the adverse effect network for the AKT3 protein.

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A machine learning method for the identification and ... - Nature.com

Its happening already. Following Dropboxs move to lay off 500 employees as it shifts its focus to AI, IBM now plans to replace 7,800 jobs with AI technology and pause hiring for roles that could be automated. Company CEO Arvind Krishna stated that most back-office positions, such as HR and accounting, will be replaced.

Layoffs due to AI were inevitable, but amid lingering job losses, new jobs are also being created. A report by the World Economic Forum states that demand for AI and machine learning specialists will grow at the fastest rate in the next five years. The organisation has also listed a number of AI jobs that are expected to see massive growth in the coming years. Lets take a look at them.

AI and machine learning specialists: These are professionals who design, develop, and implement AI and ML systems and applications. They use various tools and techniques to analyse data, build models, and optimise algorithms. The demand for AI and machine learning specialists will grow at the fastest rate in the next five years, the WEF report says.

Big data specialists: They specialise in managing, analysing and interpreting large and complex data sets. They use cutting-edge technologies to organise, store, and retrieve vast amounts of information, turning it into valuable insights that can drive business decisions. They work with a variety of industries such as healthcare, finance, and technology, to help them understand and leverage the power of data.

Data engineers: They are responsible for the design, construction and maintenance of the data infrastructure that supports an organisations data management and analytics needs. They develop and manage data pipelines, work with large datasets, and ensure that data is available and accessible to those who need it. They also work with other data professionals to design and implement data architectures that meet the needs of the organisation.

Data analysts and scientists: These are experts who collect, process, and interpret large and complex datasets to generate insights and solutions for various problems and domains. They use statistical methods, programming languages, and visualisation tools to manipulate and communicate data. Data analysts and scientists are expected to see a 32% growth in demand by 2023.

Apart from the aforementioned jobs listed by the World Economic Forum, heres a list of other jobs AI is expected to create in the near future.

AI trainers: They are responsible for teaching machines to learn from data effectively. They also help to ensure that the AI models accurately interpret the data, providing businesses with valuable insights that can drive informed decisions.

AI ethicists: They use their expertise to ensure that AI systems are developed and deployed responsibly. They also identify potential ethical concerns related to privacy, fairness, and transparency, and work to address them through policy and guidelines.

AI user experience designers: They create interfaces and experiences that are intuitive and user-friendly for AI-driven products and services. They also work to ensure that users can easily interact with AI systems, making their experiences more enjoyable and productive.

AI security analysts: They focus on ensuring the safety and integrity of AI-driven solutions. They also identify potential threats, vulnerabilities, and attacks that could compromise AI systems and develop strategies to mitigate them.

Robotics engineers: They design, build, and program autonomous machines that can perform a wide range of tasks, from assembly line work to surgical procedures. By incorporating AI capabilities such as computer vision and natural language processing, they create intelligent machines that can work alongside humans in new and exciting ways.

Of course, these are just a few examples of the new jobs that AI is expected to create. As AI continues to evolve and become more integrated into various industries, its likely that even more new job opportunities will emerge.

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First published on: 03-05-2023 at 19:39 IST

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From machine learning to robotics: WEF report predicts the most lucrative AI jobs - The Indian Express

Credit providers have grappled with fraudsters since long before mobile banking. In a modern landscape, financial services businesses dedicate ample resources to thwart fraud attempts.

As fraudulent actors get smarter, machine learning can help companies stay one step ahead. But first, organizations need access to those tools.

Capital One is democratizing access to ML tools, encouraging workers to contribute to a common shared ecosystem to provide practitioners with easy access to ML and spur innovation. In the process, Capital One found opportunities for cross-unit collaboration and improved how the company detects fraud.

"The future is here," said Zach Hanif, vice president and head of enterprise machine learning models and platforms at Capital One. "But, historically, it hasn't always been distributed evenly."

ML tools keep humans focused on the tasks that require their attention, prioritizing resources through technology. Artificial intelligence capabilities are finding a role in financial services in particular.

Four in five companies in the sector have up to five AI use cases at work in their organization, according to an NVIDIA reportpublished in February. Nearly one-quarter are using AI to help detect fraud.

Hanif's team worked alongside the card fraud division to build homegrown and open-source ML algorithms and technologies. With ML tools, the company can quickly determine whether a transaction is benign or if it needs further investigation because of potential fraud.

"We were able to get these teams on the same stack and focused on collaboration, which made sure that we were able to bring down some silos," Hanif said. "We were able to prioritize the development of reusable components so when one team would build a component of their pipeline, other teams were able to immediately begin leveraging it and save themselves the time of that initial development."

Machine learning gives the company a way to quickly determine whether something needs to be investigated, Hanifsaid.

Picking a technology and spreading it throughout the organization isn't a turnkey task.

There are several barriers to easing access to ML throughout any organization, according to Arun Chandrasekaran, distinguished VP analyst at Gartner.

The top barriers are security and privacy concerns and the black-box nature of AI systems, as well as the absence of internal AI know-how, AI governance tools and self-service AI and data platforms, Chandrasekaran told CIO Dive in an email.

Despite the advancement of AI tools in the enterprise, activities associated with data and analytics including preparation, transformation, pattern identification, model development and sharing insights with others are still done manually at many organizations.

"Demands for more data-driven and analytics-enabled decision making, and the friction and technical hurdles of this workflow, limit widespread user adoption and achieving better business outcomes," Chandrasekaran said.

But changing how companies operate is a human problem as much as it is a technical one. Cultural factors can determine whether or not a company succeeds at democratizing the use of a technology tool such as ML.

"To be able to drive change across a large organization, you're trying to make a cultural alteration," Hanif said.

Leaders need to encourage employees to imagine what they can do with specific tools, he said. With that mindset, fear of change falls away and employees begin to think about how a new technology can be contextualized within the existing problem space.

"Standardizing a platform allows everyone to have a common operating environment and runbook," Hanif said. "That way, they can start and engage in that process in a standard, well-understood way. That makes so many different things inside of the organization go smoother, go faster, and reduce the overall risk."

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How Capital One is democratizing machine learning to curb fraud - Banking Dive

May 3, 2023 by Diana Ambolis

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Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and

Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help inform investment decisions and maximize returns.

Machine learning algorithms can be trained to analyze a range of data points and variables that are relevant to NFT value. This could include factors such as the artists reputation, the rarity of the NFT, the size of the NFT market, and even social media sentiment around a particular NFT. By analyzing this data, machine learning algorithms can identify patterns and correlations that can be used to predict the future value of a given NFT.

Determining the true value of an NFT can be challenging, with many factors to consider, including the artists reputation, the rarity of the NFT, and social media sentiment around a particular NFT. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help determine the value of NFTs. In this article, well explore the top 10 benefits of using machine learning to determine NFT value.

Machine learning offers a range of benefits for investors seeking to determine NFT value. By providing accurate predictions, improving efficiency, and reducing bias, machine learning can help investors make more informed decisions about NFT investments. As the NFT market continues to evolve, it is likely that machine learning will become an increasingly important tool for investors seeking to capitalize on this emerging market.

Also, read The Top 5 Best NFT Products So Far: A Closer Look

One of the key benefits of using machine learning for NFT investment is that it can help investors make more informed decisions about which NFTs to buy or sell. By providing insights and predictions about future value, machine learning algorithms can help investors identify undervalued NFTs that have strong potential for growth, as well as overvalued NFTs that may be at risk of declining in value.

Another benefit of using machine learning for NFT investment is that it can help investors manage risk. By providing predictive models and insights, machine learning algorithms can help investors understand the potential risks and rewards associated with a given NFT investment, allowing them to make more informed decisions about how to allocate their resources.

There are also potential drawbacks to using machine learning for NFT investment. For example, the accuracy of predictive models can be influenced by a range of factors, including the quality and quantity of data used to train the algorithm. In addition, the NFT market is still relatively new and untested, making it difficult to predict how the market will behave over time.

Despite these potential drawbacks, many investors are turning to machine learning as a way to inform their NFT investment decisions. As the NFT market continues to grow and evolve, machine learning is likely to become an increasingly important tool for investors seeking to capitalize on this emerging market.

Machine learning has the potential to revolutionize the world of NFT investment, providing investors with new insights and predictive models that can inform investment decisions and maximize returns. By analyzing a range of data points and variables, machine learning algorithms can identify patterns and correlations that can be used to predict NFT value and manage risk. While there are potential drawbacks to using machine learning in this context, the benefits are significant, and it is likely that this technology will become an increasingly important tool for investors seeking to capitalize on the emerging NFT market.

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Machine Learning And NFT Investment: Predicting NFT Value And ... - Blockchain Magazine

In episode 105 of the AI/Hyperautomation Minute, Toni Witt provides clarity behind generative AI, its underlying technology the GPT (generative pre-trained transformer) machine learning model and how its evolving.

This episode is sponsored by Acceleration Economys Generative AI Digital Summit, taking place on May 25. Registration for the event, which features practitioner and platform insights on how solutions such as ChatGPT will impact the future of work, customer experience, data strategy, cybersecurity, and more, is free. To reserve your spot, sign up today.

00:26 While there are many conversations about generative AI, those outside of the tech field may still have a misunderstanding of the underlying technology and how its evolving.

01:03 Toni clarifies that ChatGPT is an web-based tool that gives access to GPT-3, which is the underlying machine learning model. GPT-3 is a word predictor. Its a form of deep learning with capabilities that are essentially a subset of what machine learning and AI can do.

01:37 Machine learning started with prediction and classification. Most AI applications that give returns to companies are these classification or predictor models, Toni explains. The Netflix recommender algorithm is an example of this, as it uses data from previous movies and shows that youve liked in the past to recommend what to watch next.

02:12 GPT-3 is a transformer model. Theres a pretty big debate going on whether these transformer models are going to be the ones that reach what you might call AGI, or artificial general intelligence, that basically matches the intelligence level of a human, Toni says.

02:57 Sam Altman, CEO of OpenAI, pointed out a trend that there will be base-level models. The GPT series is already an indication that models will help train other models. Think of it like a tech stack, says Toni.

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How the GPT Machine Learning Model Advances Generative AI - Acceleration Economy

10 best ways to earn money through machine learning in 2023 are enlisted in this article

10 best ways to earn money through machine learning in 2023 take advantage of the early lifespan and its adoption may then leverage this into other apps.

Land Gigs with FlexJobs: FlexJobs is one of the top freelance websites for finding high-quality employment from actual businesses. Whether you are a machine learning novice or a specialist, you may begin communicating with clients to monetize your skills by working on freelancing projects.

Become a Freelancer or List your Company to Hire a Team on Toptal: Toptal is similar to FlexJobs in that it is reserved for top freelancers and top firms wanting to recruit freelance machine learning programmers. This is evident in the hourly pricing given on the site as well as the caliber of the programmers.

Develop a Simple AI App: Creating an app is another excellent approach to generating money using machine learning. You may design a subscription app in which users can pay to access certain premium features. Subscription applications are expected to earn at least 50% more money than other apps with various sorts of in-app sales.

Become an ML Educational Content Creator: You can make money with machine learning online right now if you start teaching people about machine learning and its benefits. To publish and sell your course, use online platforms that provide teaching platforms, such as Udemy and Coursera.

Create and Publish an Online ML Book: You may create a book to provide extraordinary insights on the power of 3D printing, robots, AI, synthetic biology, networks, and sensors. Online book publication is now feasible because of systems such as Kindle Direct Publication, which provides a free publishing service.

Sell Artificial Intelligence Devices: Another profitable enterprise to consider is selling GPS gadgets to automobile owners. GPS navigation services can aid with traffic forecasting. As a result, it can assist car users in saving money if they choose a different route to work. Based on everyday experiences, you may estimate the places likely to be congested with access to the current traffic condition.

Generate Vast Artificial Intelligence Data for Cash: Because machine learning can aid in the generation of massive amounts of data, you can benefit from providing AI solutions to various businesses. AI systems function similarly to humans and have a wide range of auditory and visual experiences. An AI system may learn new things and be motivated by dynamic data and movies.

Create a Product or a Service: AI chatbots are goldmines and a great method to generate money with machine learning. Creating chatbot frameworks for mobile phones in the back endand machine learning engines in the front end is an excellent way to make money quickly. Making services like sentiment analysis or Google Vision where the firm or user may pay after making numerous queries per month is another excellent approach to gaining money using ML.

Participate in ML Challenges: You may earn money using machine learning by participating in and winning ML contests, in addition to teaching it. If you are a guru or have amassed a wealth of knowledge on this subject, you may compete against other real-world machine-learning specialists in tournaments.

Create and License a Machine Learning Tech: If you can develop an AI technology and license it, you can generate money by selling your rights to someone else. As the licensor, you must sign a contract allowing another party, the licensee, to use, re-use, alter, or re-sell it for cash, compensation, or consideration.

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10 Best Ways to Earn Money Through Machine Learning in 2023 - Analytics Insight