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One of the hottest investment trends to jump on at the moment is machine learning stocks to buy. Valued at about $79.3 billion at the moment, its expected to balloon to$503.4 billion by the time 2030rolls around, according to Statista.

All thanks to demand for accurate prediction and better decision making for companies and governments of all sizes. Were also seeing machine learning companies pop up in healthcare, finance, security and retail to name a few industries.

Along the way, machines will learn from historical data, identify patterns, and make logical decisions with little to no need for human interaction. Look at healthcare, for example. Its helping with faster data collection through wearables that machines can learn from. Its helping with accelerated drug discovery and development.

Plus,as noted by BuiltIn.com, By crunching large volumes of data,machine learning technology can help healthcare professionalsgenerate precise medicine solutions customized to individual characteristics. Machine learning models can also predict how patients react to certain drugs, allowing healthcare workers to proactively address patients needs.

We could easily go on. But you can see why were excited about machine learning, and the significant impact it will have on just about everything.So, how can we profit from it all? Here are three machine learning stocks you may want to buy.

Source: Sisacorn / Shutterstock.com

The last time I mentionedLantern Pharma(NASDAQ:LTRN), it traded at $5.25on May 1.

At the time, I noted, An artificial intelligence company, its helping to transform the cost and speed to oncology drug discovery and development with itsAI and machine learning platform, RADR.With the help of machine learning, AI and advanced genomics, its platform can scan billions of data points to help identity compounds that could help cancer patients.

Now trading at $6.46, theres even more to get excited about.

For one,Lantern just received regulatory approval to expand its Harmonic trial,a Phase 2 trial thats evaluating LP-300 fornon-small cell lung cancer, or NSCLC, in people that have never smoked in Japan and Taiwan. About athird of all lung cancer patientsin East Asia have never smoked, with numbers still rising.

With the expanded study, it can accelerate the collection of patient and response data needed for the development of LP-300. And if successful, the treatment may be able to help treat relapsed and inoperable adenocarcinoma of the lung in combination with chemotherapy.

Its also working with French biotech company,Oregon Therapeuticsto developprotein disulfide isomerase, or PDI, inhibitor drug candidate XCE853. Lantern will use its RADR AI platform to uncover biomarkers and efficacy-associated signatures of XCE853 across solid tumors that can aid in precision development,as noted in a company press release.

Source: Gorodenkoff / Shutterstock.com

We can also look atExscientia(NASDAQ:EXAI), an AI-driven precision medical company thats accelerating drug development and reducing the time to get it to market.

In fact,as noted by the company, Exscientia developed the first-ever functional precision oncology platform to successfully guide treatment selection and improve patient outcomes in a prospective interventional clinical study, as well as to progress AI-designed small molecules into the clinical setting.

At the moment, the company in still in Phase 1/2 studies for GTAEXS617, a potential best in class CDK7 inhibitor for the treatment of solid tumors. The company expects to move into a combination phase of the trial by the second half of the year.

In addition,EXS74539 is the companys LSD1 inhibitorand is currently making its way through IND-CTA-enabling studies (investigational new drug-clinical trial application). With it, EXAI plans to submit an IND or CTA by the third quarter of the year. It also has a goal to initiate a Phase 1/2 trial for acute myeloid leukemia (AML) patients by the end of the year.

Source: Shutterstock

Or, if you want to diversify with AI-focused companies that will benefit from AI and machine learning, theres theRoundhill Generative AI & Technology ETF(NYSEARCA:CHAT).

With an expense ratio of 0.75%, the ETF holds 50 related stocks, such asNvidia(NASDAQ:NVDA),Microsoft(NASDAQ:MSFT),Alphabet(NASDAQ:GOOG),Meta Platforms(NASDAQ:META),Advanced Micro Devices(NASDAQ:AMD), andAdobe(NASDAQ:ADBE) to name a few. All of which stand to benefit from the artificial intelligence and machine learning story.

Even better, I can buy 100 shares of CHAT for about $3,500, and gain exposure to those 50 holdings. Thats far better than buying just one of its holdings lets say 100 shares of just NVDA for about $95,000.

With the ETF, youre diversified and all your eggs arent in just one basket.

On the date of publication, Ian Cooper did not hold (either directly or indirectly) any positions in the securities mentioned. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Ian Cooper, a contributor to InvestorPlace.com, has been analyzing stocks and options for web-based advisories since 1999.

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The 3 Best Machine Learning Stocks to Quadruple Your Money by 2035 - InvestorPlace

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Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience - Nature.com

Slack has been using customer data to power its machine learning functions, including search result relevance and ranking, leading to the company being criticized over confusing policy updates that led many to believe that their data was being used to train its AI models.

According to the company's policy, those wishing to opt out must do so through their organizations Slack admin, who must email the company to put a stop to data use.

Slack has confirmed in correspondence to TechRadar Pro that the information it uses to power its ML not its AI is de-identified and does not access message content.

An extract from the companys privacy principles page reads:

To develop non-generative AI/ML models for features such as emoji and channel recommendations, our systems analyze Customer Data (e.g. messages, content, and files) submitted to Slack as well as Other Information (including usage information) as defined in our Privacy Policy and in your customer agreement.

Another passage reads: To opt out, please have your org, workspace owners or primary owner contact our Customer Experience team at feedback@slack.com

The company does not provide a timeframe for processing such requests.

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In response to uproar among the community, the company posted a separate blog post to address concerns arising, adding: We do not build or train these models in such a way that they could learn, memorize, or be able to reproduce any customer data of any kind.

Slack confirmed that user data is not shared with third-party LLM providers for training purposes.

The company added in its correspondence to TechRadar Pro that its "intelligent features (not Slack AI) analyze metadata like user behavior data surrounding messages, content and files but they don't access message content."

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Slack is training its machine learning on your chat behavior unless you opt out via email - TechRadar

The following is a summary of Machine learning study of the extended drugtarget interaction network informed by pain related voltage-gated sodium channels, published in the April 2024 issue of Pain by Chen et al.

Pain is a significant global health issue. However, current treatment options lack in terms of effectiveness, side effects, and potential for addiction, raising the need for improved treatment and the development of new drugs.

Researchers conducted a prospective study identifying potential drug candidates for pain management by targeting Voltage-gated sodium channels (Nav1.3, Nav1.7, Nav1.8, Nav1.9).

They constructed a protein-protein interaction (PPI) network based on pain-related sodium channels and drug-target networks. From over 1,000 targets, 111 inhibitor data sets were selected, and machine learning (ML) was employed to select candidates using a natural language process.

The results showed 150,000 drug candidates for side effects, repurposing potential, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of candidates.

Investigators concluded an innovative platform for the pharmacological development of pain treatments, potentially offering improved efficacy and reduced side effects.

Source: journals.lww.com/pain/abstract/2024/04000/machine_learning_study_of_the_extended_drug_target.17.aspx

Original post:
Pain-related Sodium Channels in Drug-target Interaction Network: A Machine Learning Analysis - Physician's Weekly

The advent of AI has been hailed by many as a societal game-changer, as it opens a universe of possibilities to improve nearly every aspect of our lives.

Astronomers are now using AI, quite literally, to measure the expansion of our universe.

Two recent studies led by Maria Dainotti, a visiting professor with UNLVs Nevada Center for Astrophysics and assistant professor at the National Astronomical Observatory of Japan (NAOJ), incorporated multiple machine learning models to add a new level of precision to distance measurements for gamma-ray bursts (GRBs) the most luminous and violent explosions in the universe.

In just a few seconds, GRBs release the same amount of energy our sun releases in its entire lifetime. Because they are so bright, GRBs can be observed at multiple distances including at the edge of the visible universe and aid astronomers in their quest to chase the oldest and most distant stars. But, due to the limits of current technology, only a small percentage of known GRBs have all of the observational characteristics needed to aid astronomers in calculating how far away they occurred.

Dainotti and her teams combined GRB data from NASAs Neil Gehrels Swift Observatory with multiple machine learning models to overcome the limitations of current observational technology and, more precisely, estimate the proximity of GRBs for which the distance is unknown. Because GRBs can be observed both far away and at relatively close distances, knowing where they occurred can help scientists understand how stars evolve over time and how many GRBs can occur in a given space and time.

This research pushes forward the frontier in both gamma-ray astronomy and machine learning, said Dainotti. Follow-up research and innovation will help us achieve even more reliable results and enable us to answer some of the most pressing cosmological questions, including the earliest processes of our universe and how it has evolved over time.

In one study, Dainotti and Aditya Narendra, a final-year doctoral student at Polands Jagiellonian University, used several machine learning methods to precisely measure the distance of GRBs observed by the space Swift UltraViolet/Optical Telescope (UVOT) and ground-based telescopes, including the Subaru Telescope. The measurements were based solely on other, non distance-related GRB properties. The research was published May 23 in the Astrophysical Journal Letters.

The outcome of this study is so precise that we can determine using predicted distance the number of GRBs in a given volume and time (called the rate), which is very close to the actual observed estimates, said Narendra.

Another study led by Dainotti and international collaborators has been successful in measuring GRB distance with machine learning using data from NASAs Swift X-ray Telescope (XRT) afterglows from what are known as long GRBs. GRBs are believed to occur in different ways. Long GRBs happen when a massive star reaches the end of its life and explodes in a spectacular supernova. Another type, known as short GRBs, happens when the remnants of dead stars, such as neutron stars, merge gravitationally and collide with each other.

Dainotti says the novelty of this approach comes from using several machine-learning methods together to improve their collective predictive power. This method, called Superlearner, assigns each algorithm a weight whose values range from 0 to 1, with each weight corresponding to the predictive power of that singular method.

The advantage of the Superlearner is that the final prediction is always more performant than the singular models, said Dainotti. Superlearner is also used to discard the algorithms which are the least predictive.

This study, which was published Feb. 26 in The Astrophysical Journal, Supplement Series, reliably estimates the distance of 154 long GRBs for which the distance is unknown and significantly boosts the population of known distances among this type of burst.

A third study, published Feb. 21 in the Astrophysical Journal Letters and led by Stanford University astrophysicist Vah Petrosian and Dainotti, used Swift X-ray data to answer puzzling questions by showing that the GRB rate at least at small relative distances does not follow the rate of star formation.

This opens the possibility that long GRBs at small distances may be generated not by a collapse of massive stars but rather by the fusion of very dense objects like neutron stars, said Petrosian.

With support from NASAs Swift Observatory Guest Investigator program (Cycle 19), Dainotti and her colleagues are now working to make the machine learning tools publicly available through an interactive web application.

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Cosmic Leap: NASA Swift Satellite and AI Unravel the Distance of the Farthest Gamma-Ray Bursts - UNLV NewsCenter