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The 2034 Millionaire’s Club: 3 Machine Learning Stocks to Buy Now – InvestorPlace

May 29, 2024 | Author: admin

Machine learning stocks are gaining traction as the interest in artificial intelligence (AI) and machine learning soars, especially after the launch of ChatGPT by OpenAI. This technology has given us a glimpse of its potential, sparking curiosity about its future applications, and has led to my list of machine learning stocks to buy.

Machine learning stocks present a promising opportunity for growth, with the potential to create significant wealth. As per analyst forecasts, I think around a decade from now is when we will see these companies go parabolic and reach their full growth potential.

These companies leverage machine learning for various applications, including diagnosing life-threatening diseases, preventing credit card fraud, developing chatbots and exploring advanced tech like artificial general intelligence. The future will only get better from here.

So if youre looking for machine learning stocks to buy with substantial upside potential, keep reading to discover three top picks.

Source: Lori Butcher / Shutterstock.com

DraftKings (NASDAQ:DKNG) leverages machine learning to enhance its online sports betting and gambling platform. The company has shown significant growth, with recent revenue increases and expansion in legalized betting markets.

DKNG has significantly revised its revenue outlook for 2024 upwards, expecting it to be between $4.65 billion and $4.9 billion, marking an anticipated year-over-year growth of 27% to 34%. This adjustment reflects higher projections compared to their earlier forecast ranging from $4.50 billion to $4.80 billion. Additionally, the company has increased its adjusted EBITDA forecast for 2024, now ranging from $410 million to $510 million, up from the previous estimate of $350 million to $450 million.

DraftKings has also announced plans to acquire the gambling company Jackpocket for $750 million in a cash-and-stock deal. This acquisition is expected to further enhance DraftKings market presence and capabilities in online betting.

I covered DKNG before, and I still think its one of the best meme stocks that investors can get behind. The companys stock price has risen 72.64% over the past year, and it seems theres still plenty of fuel left in the tank to surge higher.

Source: Sundry Photography / Shutterstock

Cloudflare (NYSE:NET) provides a cloud platform that offers a range of network services to businesses worldwide. The company uses machine learning to enhance its cybersecurity solutions.

Cloudflare has outlined a robust strategy for 2024, focusing on advancing its cybersecurity solutions and expanding its network services. The company expects to generate total revenue between $1.648 billion and $1.652 billion for the year. This revenue forecast reflects a significant increase in their operational scale.

NET is another stock that is leveraging machine learning to its full advantage. Ive been bullish on this company for some time and continue to be so. Notably, Cloudflare is expanding its deployment of inference-tuned graphic processing units (GPUs) across its global network. By the end of 2024, these GPUs will be deployed in nearly every city within Cloudflares network.

NET has been silently integrating many parts of its network within the internets fabric for millions of users, such as through its DNS service, Cloudflare WARP; reverse proxy for website owners; and much more. Around 30% of the 10,000 most popular websites globally use Cloudflare. Many of NETs services can be accessed free of charge.

It is following a classic tech stock strategy of expanding its users, influence and reach over reaching immediate profits, and its financials have slowly scaled with this performance.

Source: VDB Photos / Shutterstock.com

CrowdStrike (NASDAQ:CRWD) is a leading cybersecurity company that uses machine learning to detect and prevent cyber threats.

In its latest quarterly report on Mar. 5, CRWD reported a 102% earnings growth to 95 cents per share and a 33% revenue increase to $845.3 million. Analysts expect a 57% earnings growth to 89 cents per share in the next report and a 27% EPS increase for the full fiscal year ending in January.

Adding to the bull case for CRWD is that it has has partnered with Google Cloud by Alphabet (NASDAQ:GOOG, GOOGL) to enhance AI-native cybersecurity solutions, positioning itself strongly against competitors like Palo Alto Networks (NASDAQ:PANW).

Many contributors here at Investorplace have identified CRWD as one of the best cybersecurity stocks for investors to buy, and I am in agreement here. Its aggressive EPS growth and stock price appreciation (140.04% over the past year), make it a very attractive pick for long-term investors.

On the date of publication, Matthew Farley did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

Matthew started writing coverage of the financial markets during the crypto boom of 2017 and was also a team member of several fintech startups. He then started writing about Australian and U.S. equities for various publications. His work has appeared in MarketBeat, FXStreet, Cryptoslate, Seeking Alpha, and the New Scientist magazine, among others.

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

May 29, 2024 | Author: admin

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UK researcher receives NSF CAREER award to develop data-driven, smart technologies for sustainable living – UKNow

May 29, 2024 | Author: admin

LEXINGTON, Ky. (May 28, 2024)Using groundbreaking artificial intelligence (AI) technology, a University of Kentucky researcher is developing a machine learning pipeline with the goal of improving our quality of life.

Hana Khamfroush, Ph.D., associate professor in the Department of Computer Science in the UK Stanley and Karen Pigman College of Engineering,recently received the prestigious National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award. The NSF will support Khamfroush with $624,716 over five years for her research involving pre-processing data, while maintaining privacy, so that it can be trained for use in machine learning models for smart cities applications.

With eco-friendly practices as a priority, smart cities use data and technology to create more livable and sustainable urban environments.

I think we are all used to the internet on computers and smartphones. But when we talk about the internet of things, we are looking at every possible device becoming connected devices to the internet, said Khamfroush. For example, we can have a smart thermometer that can just sense when we are out of the home to reduce the lights. This can help with energy consumption.

The NSF-funded work will serve as a foundation for various emerging AI-based applications including smart traffic light systems. Many of these applications will require a huge amount of data to be automatically processed and some will need to be processed in real time.

There is a lot of noisy data and missing data points, said Khamfroush. A big part of this project is about federated learning and federated data preparation. This means we are preparing data and training machine learning models without losing privacy because we are not sharing the data to a cloud. All the training is done collaboratively and locally on the devices.

Khamfroushs research was previously focused on distributed and edge computing systems. As machine learning becomes more and more developed, she says her research becomes more applicable in the domain of machine learning and distributed machine learning.

I was looking for something that is more of interdisciplinary research. I thought about how I can bring in my previous research and add a flavor of machine learning to it?

Dealing with the unknown is something that I really like. I think doing research, especially in this very exciting field of machine learning and computer science, is something that I really like and appreciate because I can get creative. I can envision things that may be very ambitious. You may fail. But I just like dealing with the unknown and being able to deal with the challenges.

The CAREER Award is one of the most prestigious awards in support of the early career-development activities of those teacher-scholars who most effectively integrate research and education within the context of the mission of their organization, according to NSF.

This material is based upon work supported by the National Science Foundation under Award Number 2340075. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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

May 29, 2024 | Author: admin

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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TikTok moves toward ‘performance automation vision’ with latest machine learning ad tools – Digiday

May 29, 2024 | Author: admin

TikToks latest machine learning ad solutions are proof that the platform wants to automate as much of its advertising as possible.

The product, dubbed Performance Automation, was announced at the platforms fourth annual TikTok World product summit today its first official summit since Biden signed the TikTok divest or sell bill last month, and subsequently the entertainment app took the U.S. government to court to appeal.

Its safe to say TikTok wants advertisers to believe its not entertaining the idea of being booted out of the U.S. anytime soon. If that wasnt already obvious during its NewFront earlier this month, this latest announcement makes it clearer that its business as usual for the platform right now. Or at least trying to make it as clear as possible that advertisers can park their contingency plans and keep spending on TikTok.

TikTok is actively working to keep marketers engaged and on the platform despite the legislative challenges, said Traci Asbury, social investment lead at Goodway Group. They [TikTok] have complete confidence in their upcoming legal appeals and are actively encouraging marketers to keep adopting best practices and usage of the platforms capabilities to make positive impacts on their businesses.

Well, you probably already know about TikToks Smart Performance Campaign, which was launched last year. The campaign uses semi-automation capabilities including auto-targeting, auto-bidding and auto-creative.

But Performance Automation, which is still in early testing, goes one step further, by automating more of the process, including the creative. With this campaign, advertisers input the necessary assets, budget and goals, and TikToks predictive AI and machine learning will select the best creative asset, to ensure the best campaign is put in front of the right customer at the right time. As a TikTok spokesperson confirmed, the platform is moving toward a performance automation vision and this latest product is the next step on that journey.

And thats not all. The platform has also launched a similar capability for its TikTok Shop, dubbed TikTok Shop Marketing Automation. Like Performance Automation, this works by automating bidding, budgeting, ad management and creative for TikTok Shop products. Since TikTok Shop is only available in select regions, this latest product is currently rolled out in South-East Asia, and in testing in the U.S.

Ohio-based health and wellness brand Triquetra Health is one of those early testers. According to Adolfo Fernandez, global product strategy and operations at TikTok, the brand already achieved 4x their return on investment in TikTok Shop within the first month of using this new automation product, and increased sales on the platform by 136%. He did not provide exact figures.

To be clear, Performance Automation and TikTok Shop Marketing Automation arent their official names. These are just temporary identities the platform is using until they roll out the products officially.

Still, all sounds familiar? Thats because it is. Performance Automation is similar to what the other tech giants have been doing for a while now, and what TikTok started to dabble in with its Smart Performance Campaign last year. Think Googles Performance Max, Metas Advantage+ and now even Amazons Performance+ they all play a similar role for their respective platforms. TikTok just joining the pack simply confirms that automation is the direction that advertising as an industry is heading.

In many ways, this was inevitable. Meta, Google et al have amassed billions of ad dollars over the years by making it as easy as possible for marketers to spend on their ads. From programmatic bidders to attribution tools, the platforms have tried to give marketers fewer reasons to spend elsewhere. Machine learning technologies that essentially oversee campaigns are the latest manifestation of this. Sooner or later TikTok was always going to make a move.

Still, there are concerns aplenty over how these technologies work they are, after all, the ultimate set it and forget it type of campaign. Marketers hand over the assets and data they want the platform to work with, and the technology takes it from there. Thats it. Marketers have no way of knowing whether these campaigns are doing what the platform says theyre doing because theyre unable to have them independently verified. It remains to be seen whether TikToks own effort will take a similar stance or break with tradition.

Speaking of measurement, TikTok is also launching unified lift a new product which measures TikTok campaign performance across the entire decision journey, using brand and conversion lift studies. KFC Germany has already tried it out and drove a 25% increase in brand recall and saw an 81% increase in app installs, according to Fernandez, without providing exact figures.

Among the other announcements were:

Well for now, nothing much has changed. Marketers have contingency plans in place, but thats just standard business practice. Beyond that, everything as far as TikTok goes is pretty much business as usual.

Colleen Fielder, group vp of social and partner marketing solutions at Basis Technologies said her team is not actively recommending any of their clients discontinue spending on TikTok. Theyre continuing to include the platform on proposals.

We knew TikTok was going to sue the U.S. government, and that may push this 9-12 month timeline even further back, which gives us a longer lead time to continue running on TikTok and / or identify alternative platforms as needed, she said.

For Markacy, its a similar state of play. We have a loose partnership with digital media company Attn, which is heavily invested in TikTok, said Tucker Matheson, co-CEO of the company. Theyre still getting big proposals for work, which is a positive sign.

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Redox, Snowflake Partner to Streamline Healthcare Data Exchange for AI and Machine Learning – HIT Consultant

May 29, 2024 | Author: admin

What You Should Know:

Redox, a healthcare interoperability company, and Snowflake, the Data Cloud company, have joined forces to simplify the exchange of healthcare data.

This strategic partnership aims to revolutionize how healthcare organizations access and utilize patient data, ultimately leading to improved patient care.

Unifying Legacy Systems for Seamless Data Flow

The collaboration leverages Redoxs expertise in unifying healthcare data from various sources, including legacy systems and disparate formats. This unified data stream is then delivered to Snowflakes Healthcare & Life Sciences Cloud in near real-time. This streamlined approach eliminates data silos and ensures a more comprehensive view of patient health information.

Empowering Providers, Payers, and Digital Health with AI and ML

By making healthcare data readily available in Snowflakes secure and scalable cloud environment, Redox and Snowflake empower various healthcare stakeholders. Providers, payers, and digital health organizations can leverage this data for advanced analytics powered by Artificial Intelligence (AI) and Machine Learning (ML).

The ability to quickly, easily, and securely access health data from a variety of systems is essential for uncovering meaningful insights that are required for better precision-based care and better member outcomes, said Joe Warbington, Industry Principal for Healthcare at Snowflake. Together, the Snowflake Healthcare and Life Sciences Data Cloud and Redox accelerate interoperability to centralize live healthcare data from often dozens to hundreds of data system silos, equipping our customers to garner deeper data insights, construct comprehensive Patient 360 data products, and push insights back into EHRs and health tech apps. We look forward to seeing how Snowflakes and Redoxs technologies drive the future of connected healthcare.

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Five industries undergoing transformative change due to ongoing Artificial Intelligence research Intelligent CIO … – Intelligent CIO

May 29, 2024 | Author: admin

Around the world, major industries are undergoing transformative changes thanks to the power and adaptability of Artificial Intelligence (AI).

From AI algorithms that enhance the efficiency of flightpaths, to AI assisted drones that monitor crops, and metaverse-based immersive learning, here are five key sectors that are at the heart of the AI revolution.

1)Healthcare:AI will have large-scale impact in the health industry. The university has more than 20 ongoing or upcoming health-related research projects with partners such as the Malaria No More, Quris-AI, Aspire, Infinite Brain Technologies (IBT), Abu Dhabi Health Co and Sheikh Shakhbout Medical City. To combine these efforts, the university launched itsInstitute of Digital Public Health (IDHP)this year to provide a pathway to transformative AI advancements to solidify the UAEs visionto become a global hub for AI and a centre for life sciences.

AI is already revolutionising healthcare by streamlining administrative tasks, enhancing diagnostics, and personalising patient care. However, machine learning tools will have the biggest impact and can analyse vast amounts of medical data from patient records to CT scans to identify patterns and predict diseases, leading to earlier detection and more effective treatments. Most recently, MBZUAI partnered with The Department of Health Abu Dhabi (DoH) and Core42 to launch the Global AI Healthcare Academy center and provide AI training and upskilling to the Emirates healthcare workforce.

2) Aviation:In the aviation industry, AI is optimising operations, improving safety and enhancing the passenger experience. Airlines are also using AI algorithms to predict maintenance needs, optimising aircraft use and reducing delays.

Last year, MBZUAI andEtihad Airways, the national airline of the UAE, signed a Memorandum of Understanding (MoU) to jointly develop initiatives and conduct research into how AI could transform key aspects of the aviation sector. As part of the agreement, both organisations will establish joint training programs and explore research opportunities. Etihad Airways was also a launch partner of Jais, the worlds most advanced Arabic large language model (LLM).

3)Agriculture:AI technologies are being used to increase crop yields, enhance food security and optimise resources used in agriculture. Drones equipped with AI-powered sensors can monitor crops and detect diseases and nutrient deficiencies early, enabling targeted interventions, while Machine Learning algorithms can analyse weather patterns and soil data to provide insights for precision farming.

MBZUAI will work withSilal, an agri-food company based in Abu Dhabi, to bring AI innovation to agriculture and food production. The agreement will support the creation of a joint AI Center of Excellence with the potential to enable the UAE to develop and expand its food production sector and increase sustainable practices.

4) Education:AI is reshaping education by personalising learning experiences, automating administrative tasks and creating new types of learning experiences.Adaptive learning platformsuse AI algorithms to tailor curriculum and learning materials to each students individual needs and learning pace, while virtual tutors and chatbots provide instant assistance and feedback to students.

MBZUAIsMetaverse Center(MMC) is conducting research on AI-enabled metaverse virtual teleportation solutions that could help expand education by giving children in remote areas the ability to attend school virtually, in immersive 3D environments. The university is also exploring ways to create bespoke avatars and 3D content.

5) Energy:In the energy sector, AI is optimising operations, increasing efficiency and promoting sustainability. AI also requires a lot of power to operate. MBZUAI is championing sustainable AI at scale and researching ways to reduce AIs energy consumption. The university pioneered theAI Operating System (AIOS),a technology designed to substantially reduce the three big costs of AI computing energy, time and talent. MBZUAIs AIOS reduces AI computing energy costs by making models smaller, faster, more efficient and less reliant on expensive hardware for AI creation. It directly speeds up the computing operations involved in training and serving AI models, which further reduces the time needed for training. On top of this, researchers have been working on an array of open-source, on-device or efficiently trained LLMs.

Smart grids leverage AI algorithms to monitor and manage energy distribution in real-time, optimising loads and reducing energy wastage. A team at MBZUAI is working on AI solutions forsmart energy gridsby applying a technique called federated learning to train a machine learning model, enabling it to learn about the energy usage habits of millions of users without compromising data privacy. This enables energy providers to massively increase the efficiency and reliability of energy distribution.

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AI Startup Says California AI Bill Will Hamper Innovation – BroadbandBreakfast.com

May 29, 2024 | Author: admin

AI

The bill increases regulatory requirements for machine learning systems in California.

May 24, 2024 In a Tuesday press release, Haltia AI, an artificial intelligence startup based in Dubai, warned leaders in machine learning that Californias new AI bill will cripple innovation with overly burdensome regulations.

Haltia said that the bill throws a wrench into the growth of AI startups with its unrealistic requirements and stifling compliance costs.

The legislation, titled the Understanding the Safe and Secure Innovation for Frontier Artificial Intelligence Act, was introduced in February and passed the California State Senate on Tuesday. The act mandates that developers of AI tools comply with various safety requirements and report any safety concerns.

AI systems are defined by the act as machine-based systems that can make predictions, recommendations, decisions, and formulate options. Safety tests include ensuring that an AI model does not have the capability to enable harms, such as creation of chemical and biological weapons or cyberattacks on critical infrastructure. Third party testers will be required to determine the safety of these systems.

Haltia said that on the surface, the act aims for responsible AI development. However, its implementation creates a labyrinth of red tape that disproportionately impacts startups. Because the bill requires ongoing annual reviews, Haltia argues that it adds significant technical and financial burdens.

Arto Bendiken, co-founder and CTO at Haltia, said that the act is a prime example of how well-intentioned regulations can morph into a bureaucratic nightmare. He added that the financial penalties for non-compliance only exacerbate the issue, potentially deterring groundbreaking ideas before they even take flight.

Haltia called for other AI startups to follow its lead and move operations to the United Arab Emirates where its thriving ecosystem, coupled with its commitment to the future of AI, makes it the ideal launchpad for the next generation of groundbreaking AI technologies in the Silicon Valley of the East.

In 2023, California Governor Gavin Newson signed an executive order that announced new directives aimed at understanding the risks of machine learning technologies in order to ensure equitable outcomes when used and to prepare the states workforce for its use.

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Scientists leverage machine learning to decode gene regulation in the developing human brain – EurekAlert

May 29, 2024 | Author: admin

image:

The study is part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development. (From left: first authors Sean Whalen and Chengyu Deng, and senior authors Katie Pollard and Nadav Ahituv.)

Credit: Gladstone Institutes / Michael Short

SAN FRANCISCOMay 24, 2024In a scientific feat that broadens our knowledge of genetic changes that shape brain development or lead to psychiatric disorders, a team of researchers combined high-throughput experiments and machine learning to analyze more than 100,000 sequences in human brain cellsand identify over 150 variants that likely cause disease.

The study, from scientists at Gladstone Institutes and University of California, San Francisco (UCSF), establishes a comprehensive catalog of genetic sequences involved in brain development and opens the door to new diagnostics or treatments for neurological conditions such as schizophrenia and autism spectrum disorder. Findings appear in the journal Science.

We collected a massive amount of data from sequences in noncoding regions of DNA that were already suspected to play a big role in brain development or disease, says Senior Investigator Katie Pollard, PhD, who also serves as director of the Gladstone Institute for Data Science and Biotechnology. We were able to functionally test more than 100,000 of them to find out whether they affect gene activity, and then pinpoint sequence changes that could alter their activity in disease.

Pollard co-led the sweeping study with Nadav Ahituv, PhD, professor in the Department of Bioengineering and Therapeutic Sciences at UCSF and director of the UCSF Institute for Human Genetics. Much of the experimental work on brain tissue was led by Tomasz Nowakowski, PhD, associate professor of neurological surgery in the UCSF Department of Medicine.

In all, the team found 164 variants associated with psychiatric disorders and 46,802 sequences with enhancer activity in developing neurons, meaning they control the function of a given gene.

These enhancers could be leveraged to treat psychiatric diseases in which one copy of a gene is not fully functional, Ahituv says: Hundreds of diseases result from one gene not working properly, and it may be possible to take advantage of these enhancers to make them do more.

Organoids and Machine Learning Take the Spotlight

Beyond identifying enhancers and disease-linked sequences, the study holds significance in two other key areas.

First, the scientists repeated parts of their experiment using a brain organoid developed from human stem cells and found that the organoid was an effective stand-in for the real thing. Notably, most of the genetic variants detected in the human brain tissue replicated in the cerebral organoid.

Our organoid compared very well against the human brain, Ahituv says. As we expand our work to test more sequences for other neurodevelopmental diseases, we now know that the organoid is a good model for understanding gene regulatory activity.

Second, by feeding massive amounts of DNA sequence data and gene regulatory activity to a machine learning model, the team was able to train the computer to successfully predict the activity of a given sequence. This type of program can enable in-silico experiments that allow researchers to predict the outcomes of experiments before doing them in the lab. This strategy enables scientists to make discoveries faster and using fewer resources, especially when large quantities of biological data are involved.

Sean Whalen, PhD, a senior research scientist in the Pollard Lab at Gladstone and a co-first author of the study, says the team tested the machine learning model using sequences held out from model training to see if it could predict the results already gathered on gene expression activity.

The model had never seen this data before and was able to make predictions with great accuracy, showing it had learned the general principles for how genes are impacted by noncoding regions of DNA in developing brain cells, Whalen says. You can imagine how this could open up a lot of new possibilities in research, even predicting how combinations of variants might function together.

A New Chapter for Brain Discoveries

The study was completed as part of the PsychENCODE Consortium, which brings together multidisciplinary teams to generate large-scale gene expression and regulatory data from human brains across several major psychiatric disorders and stages of brain development.

Through the consortiums publication of multiple studies, it seeks to shed light on poorly understood psychiatric conditions, from autism to bipolar disorder, and ultimately jumpstart new treatment approaches.

Our study contributes to this growing body of knowledge, showing the utility of using human cells, organoids, functional screening methods, and deep learning to investigate regulatory elements and variants involved in human brain development, says Chengyu Deng, PhD, a postdoctoral researcher at UCSF and a co-first author of the study.

About the Study

The study, Massively Parallel Characterization of Regulatory Elements in the Developing Human Cortex, appears in the May 24, 2024 issue of Science. Authors include: Chengyu Deng, Sean Whalen, Marilyn Steyert, Ryan Ziffra, Pawel Przytycki, Fumitaka Inoue, Daniela Pereira, Davide Capauto, Scott Norton, Flora Vaccarino, PsychENCODE Consortium, Alex Pollen, Tomasz Nowakowski, Nadav Ahituv, and Katherine Pollard.

The work was funded in part by the National Institute of Mental Health, the New York Stem Cell Foundation, the National Human Genome Research Institute, and Coordination for the Improvement of Higher Education Personnel. The data generated was part of thePsychENCODE Consortium.

About Gladstone Institutes

Gladstone Institutesis an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. Established in 1979, it is located in the epicenter of biomedical and technological innovation, in the Mission Bay neighborhood of San Francisco. Gladstone has created a research model that disrupts how science is done, funds big ideas, and attracts the brightest minds.

Massively parallel characterization of regulatory elements in the developing human cortex

24-May-2024

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Collaboration to drive artificial intelligence and machine learning market growth | TheBusinessDesk.com – The Business Desk

May 29, 2024 | Author: admin

Liverpool-based SysGroup, a technology partner specialising in the delivery and management of cloud, data and security services, has signed a strategic partnership with IT solutions and services provider, Softcat.

The deal with Softcat, which has a base in Leeds, is designed to open up new avenues for SysGroup in the artificial intelligence and machine learning markets. (AI and ML)

Andrew Hermsen, Softcat chief technologist Data, AI & Automation, said: We are pleased to announce our strategic partnership with SysGroup, which represents a significant step forward in addressing the evolving needs of our customers in the dynamic and rapidly growing machine learning market.

This collaboration leverages the unique strengths of both companies, combining SysGroups innovative AI and ML solutions with Softcats deep expertise in the IT market.

Together, we are poised to deliver unparalleled value, helping our clients harness the transformative potential of machine learning to drive business growth and innovation.

As the opportunities in this space continue to expand, we are committed to providing cutting-edge solutions that empower our customers to stay ahead of the curve.

Heejae Chae, SysGroups executive chairman, added: We are thrilled to have achieved Preferred Partner status with Softcat plc, marking a significant milestone in our journey to become a leading force in the AI and ML markets.

This partnership not only validates our innovative approach to AI and ML solutions but also opens up new possibilities for us to deliver exceptional value to our clients.

By combining our strengths with Softcats industry-leading expertise, we can offer comprehensive and cutting-edge solutions that drive efficiency and innovation for our customers.

We look forward to the tremendous opportunities this collaboration will bring and are excited about the positive impact it will have on our clients success.

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