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The new application, TargetSelector, streamlines target-protein discovery and enables researchers in various industries to identify novel targets for innovative products

REHOVOT, Israel, July 25, 2023 /PRNewswire/ --Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN), a leading computational biology company targeting to revolutionize life-science product discovery and development across multiple market segments, is proud to announce the latest addition to its ChemPass AI tech-engine a breakthrough technology for target-protein discovery. The integration of TargetSelector, a new application that streamlines target-protein discovery for active molecule identification, assists researchers in finding suitable target proteins for new products while reducing development time, resources and most importantly, increasing the probability of success.

Proteins play a fundamental role in a wide array of biological processes and serve as the primary targets for developing innovative therapeutics, ag-chemical, ag-biological, and other life science solutions. The precise identification of these protein targets is pivotal in advancing research and discovery across various domains, including pharmaceuticals, agriculture, and environmental applications.

The challenge of finding a target-protein that is novel, safe, and druggable from the thousands of proteins in a relevant organism is enormous. Leveraging predictive machine learning algorithms and genomic data, users gain valuable insights into product requirements such as homology, druggability, essentiality, and biological pathways, efficiently narrowing down the list of potential target-protein, thus optimizing the discovery process.

"ChemPass AI tech-engine is a cutting-edge platform for the identification of small molecules. The addition of the TargetSelector application now enables a broader scope of finding the optimal target-protein for these molecules," said Dr. Nir Arbel, CPO at Evogene. "Our subsidiary AgPlenus, which focuses on developing ag chemicals, will be the first to benefit from this new improvement, applying it to identify novel mechanismsof action for pesticides. I believe that this significant advancement in Evogene's ChemPass AI tech-engine, positions us to forge strategic partnerships with industry leaders, unlocking innovation, expediting product development, and delivering groundbreaking solutions that tackle pressing global challenges."

About ChemPass AI:

ChemPass AI tech engine is a cutting-edge computational platform for discovering and optimizing small molecules for various life-science products, such as therapeutics and ag-chemicals. Developed at the intersection of docking techniques and machine learning, ChemPass AI brings together the power of artificial intelligence, predictive biology, and molecular interactions to accelerate target-protein and active molecule discovery processes like never before.

ChemPass AIhas been trained on vast repositories of molecular data encompassing diverse chemical structures and biological targets. This wealth of knowledge empowers the platform to recognize intricate patterns, subtle interactions, and complex relationships between small molecules and their target-proteins. As a result, ChemPass AI can rapidly evaluate an organism's protein set (proteome) as well as billions of potential candidates, ranking them according to their likelihood of success and shortening the time needed to identify promising target-proteins and leads (small molecules).

About Evogene:

Evogene Ltd. (Nasdaq: EVGN) (TASE: EVGN) is a computational biology company leveraging big data and artificial intelligence,aiming to revolutionize the development of life-science based products by utilizing cutting-edge technologies to increase the probability of success while reducing development time and cost.

Evogene established three unique tech-engines - MicroBoostAI,ChemPass AIandGeneRator AI. Each tech-engineis focused on the discovery and development of products based on one of the following core components: microbes (MicroBoost AI), small molecules (ChemPass AI), and genetic elements (GeneRator AI).

Evogene uses its tech-engines to develop products through strategic partnerships and collaborations, and its five subsidiaries including:

For more information, please visit: http://www.evogene.com.

Forward-Looking Statements: This press release contains "forward-looking statements" relating to future events. These statements may be identified by words such as "may", "could", "expects", "hopes" "intends", "anticipates", "plans", "believes", "scheduled", "estimates", "demonstrates" or words of similar meaning. For example, Evogene and its subsidiaries are using forward-looking statement in this press release when it discusses TargetSelector's ability to assist researchers in finding suitable target proteins for new products while reducing development time, resources and increasing the probability of success, TargetSelector's ability to enable a broader scope of finding the optimal protein target for hit small molecules, AgPlenus' success in identifying novel mechanism of action pesticides, and ChemPass AI's ability to accelerate drug discovery processes by reducing the time and resources required. Such statements are based on current expectations, estimates, projections and assumptions, describe opinions about future events, involve certain risks and uncertainties which are difficult to predict and are not guarantees of future performance. Therefore, actual future results, performance or achievements of Evogene and its subsidiaries may differ materially from what is expressed or implied by such forward-looking statements due to a variety of factors, many of which are beyond the control of Evogene and its subsidiaries, including, without limitation, those risk factors contained in Evogene's reports filed with the applicable securities authority. In addition, Evogene and its subsidiaries rely, and expect to continue to rely, on third parties to conduct certain activities, such as their field-trials and pre-clinical studies, and if these third parties do not successfully carry out their contractual duties, comply with regulatory requirements or meet expected deadlines, Evogene and its subsidiaries may experience significant delays in the conduct of their activities. Evogene and its subsidiaries disclaim any obligation or commitment to update these forward-looking statements to reflect future events or developments or changes in expectations, estimates, projections, and assumptions.

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Contact: Rachel Pomerantz Gerber Head of Investor Relations at Evogene [emailprotected] +972-8-9311901

SOURCE Evogene

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Evogene's ChemPass AI Tech-Engine is Introduced with New ... - PR Newswire

Exploring the Future of Semiconductor Testing: A Comprehensive Analysis of Machine Learning Applications

The future of semiconductor testing is poised for a significant transformation, thanks to the advent of machine learning applications. As the semiconductor industry continues to evolve, the need for more efficient and accurate testing methods has become increasingly apparent. Machine learning, a subset of artificial intelligence, is emerging as a promising solution to meet these demands.

Semiconductor testing is a critical process in the manufacturing cycle, ensuring the functionality and reliability of semiconductor devices. However, traditional testing methods are time-consuming, costly, and often unable to detect subtle defects that could lead to device failure. Machine learning, with its ability to learn from data and make predictions, offers a new approach to semiconductor testing that could overcome these challenges.

Machine learning algorithms can be trained to recognize patterns in data, enabling them to predict outcomes with high accuracy. In the context of semiconductor testing, these algorithms could be used to analyze data from the manufacturing process and predict potential defects in the devices. This predictive capability could significantly reduce the time and cost associated with testing, as well as improve the overall quality of the devices.

Moreover, machine learning can also be used to optimize the testing process itself. By analyzing data from previous tests, machine learning algorithms can identify the most effective testing strategies and adapt them to new devices. This adaptive testing approach could further enhance the efficiency and accuracy of semiconductor testing.

The application of machine learning in semiconductor testing is not without its challenges. One of the main hurdles is the need for large amounts of high-quality data to train the machine learning algorithms. This data is often difficult to obtain due to the proprietary nature of semiconductor manufacturing processes. However, collaborations between semiconductor manufacturers and machine learning researchers are starting to address this issue, paving the way for more widespread adoption of machine learning in semiconductor testing.

Another challenge is the complexity of the machine learning algorithms themselves. These algorithms require significant computational resources and expertise to develop and implement, which may be beyond the capabilities of many semiconductor manufacturers. However, advances in cloud computing and the development of user-friendly machine learning platforms are making these technologies more accessible.

Despite these challenges, the potential benefits of machine learning in semiconductor testing are too significant to ignore. The ability to predict defects and optimize testing strategies could revolutionize the semiconductor industry, leading to more reliable devices and lower manufacturing costs. Furthermore, the use of machine learning in semiconductor testing could also have broader implications for the electronics industry, potentially leading to more efficient production processes and higher-quality electronic devices.

In conclusion, the future of semiconductor testing is likely to be shaped by the application of machine learning. While there are challenges to overcome, the potential benefits of this technology are substantial. As the semiconductor industry continues to evolve, the adoption of machine learning in semiconductor testing could play a crucial role in driving this evolution, leading to significant improvements in device quality and manufacturing efficiency.

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There are many methods for better night-vision, but often these rely on enhancing light, which may not be present, or using devices which can interfere with one another. One alternative solution is to use heat, but such infrared sensors struggle to distinguish between different objects. To overcome this, researchers have now combined such sensors with machine learning algorithms to make a system that grants day-like night-vision. They hope it will be useful in technologies such as self-driving cars.

Research article: Bao et al.

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We discuss some highlights from the Nature Briefing. This time, the cost to scientists of English not being their native language, and the mysterious link between COVID-19 and type 1 diabetes.

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Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.

Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. An RSS feed for the Nature Podcast is available too.

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Last Updated on June 12, 2023

Are you eager to know more about the differences between machine learning and Deep learning? If so, then this article is for you. Well provide you with everything you need to know about the two types of AI models and the key differences that differentiate them.

In recent years, theres been a lot of buzz on the internet concerning machine learning and deep learning. However, its not common knowledge as to what these terms actually mean. This brings us to the question, what exactly are machine learning and deep learning?

Before we dive into that, its best to give you a broad overview of artificial intelligence (AI) since machine learning and deep learning are both subsets of artificial intelligence.

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In its simplest form, artificial intelligence utilizes computer science and data to solve problems in machines. It enables machines to act and think like humans. At the moment, artificial intelligence is yet to match human intelligence. But in the future, artificial intelligence may eventually match or even surpass human intelligence due to the exponential rate of its development.

Currently, when compared to humans, AI excels in certain areas. For example, AI can complete a select number of tasks much more efficiently than humans, excelling especially in repetitive tasks.A great example of a service powered by Machine Learning is OpenAIs ChatGPT.

However, despite AIs proficiency in this area, it is still limited in its ability to perform a great number of functions and often requires some sort of human input or moderation.This is where machine learning and deep learning come into the picture. They can help AI refine their systems to become more discerning and more efficient at carrying out tasks.

Machine learning is a subset of artificial intelligence that focuses on computers that are able to learn from experience without being programmed. Machine learning artificial intelligence enables scientists to train machines on large amounts of data. The machine learning model is made to use an algorithm in analyzing and drawing inferences from the available data. And as the machine parses more data, the better it becomes at completing a task.

Machine learning is of 3 different types; supervised learning, unsupervised learning, andreinforcement learning.

Today, machine learning is used for a broad range of things, such as automated recommendations, malware threat detection, fraud detection, spam filtering, generalized trend-based predictions, and more.

Deep learning is a subset of machine learning that is modeled on the workings of the human brain. It can be considered to be an advanced version or evolution of machine learning. A deep learning model works similarly to human brains, in that it layers algorithms and computing units, also known as neurons, into a large web of interconnected systems. This web of data is known artificial neural network. These deep neural networkscontinually analyze datasets in a logical fashion to draw conclusions and predictions based on them.

A great example of deep learning artificial intelligence is Googles AlphaGo, which can beat professional human players at the board game Go, the oldest board game known to be continually played.

There are different types of deep learning algorithms. Some of which include convolutional neural networks (CNNs), recurrent neural networks (RNNs),generative adversarial networks(GANs), long short-term memory networks (LSTMs), multilayer perceptrons (MLPs), radial basis function networks (RBFNs), and more.

Deep learning is used for a broad range of things today, such as automated driving, the military, consumer electronics, speech recognition, image recognition, and more.

Lets take a look at some of the key differences between machine learning and deep learning.

Getting results from machine learning algorithms requires a fair amount of human intervention, more so than with a Deep Learning Model. On the other hand, the setup process for deep learning is vastly more complex. But after that, only very little human intervention is required.

Machine learning systems are very easy and fast to set up. However, the results they produce are often limited. While deep learning systems take a longer time to set up, their results are usually instantaneous.

Machine learning uses traditional algorithms and usually relies on structured data. Deep learning uses neural networks and is designed to accommodate huge amounts of unstructured data.

As we have seen, machine learning and deep learning are quite similar but also differ in many ways. As we have seen with technologies such as Siri and Alexa, these types of machine learning have the potential to make great leaps forward in the advancement of the tech we have today.For generations to come, machine learning deep learning will impact our lives in so many ways and will become an increasingly important part of almost every industry.

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One of the first community-based population health studies in the US, the Healthy Nevada Project launched in 2016 with three straightforward goals: conduct sound science, improve health, and save lives. Now among the nations largest such studies, the ground-breaking health and genetics project is three for three.

Developed by theDesert Research Institute Center for Genomic Medicine, the Healthy Nevada Project offers genetic testing at no cost to Nevada residents who want to learn more about their health and genetic profile.

By combining genetic data, environmental data and individual health information, researchers and physicians are gaining new insights into population health, enabling personalised healthcare while improving the health and wellbeing of entire communities in the state.

Painting an accurate portrait of an individual or population to help understand and anticipate health outcomes requires data representing many life factors, including genetics, socioeconomic backgrounds, physical environments, lifestyle behaviours and quality of healthcare.

One of medicines most complicated questions is, how do you predict what someones health outcome is going to be? says Joseph Grzymski, PhD, who serves as principal investigator of the Healthy Nevada Project, chief scientific officer of Renown Health, and research professor of computational biology and genetics at theDesert Research Institute. Its not just genetics, or your blood pressure or where you live, its trying to model all the impacting factors for diseases. The massive challenge of population health studies is to build better predictive models to understand why some people get sick and others dont, why some live to be 90 and above, and determine what that magical equation is.

Working in tandem with experts in environmental data at the Desert Research Institute, the Center for Genomic Medicine fuels the project with de-identified electronic health records. Researchers supplement this with data from the Environmental Protection Agency (EPA), the US Census Bureau, birth and death records, and other data sources to build a population health portrait.

To form connections between participant genetic information and other health factors, data scientists applymachine learningandartificial intelligencecapabilities to DNA results generated byHelix, a partner specialising in population genomics.

Were working to understand how environmental and other factors can help predict who may be at risk, allow for quicker diagnoses and encourage the development of more precise treatments, says Jim Metcalf, chief data scientist of the Healthy Nevada Project. Statistical and machine learning methods, along with the intuitive data visualisations made possible by SAS, have been critical elements.

In addition to using analyticsto identify populations and subpopulations of people who already have a disease in common, project researchers also apply analytics to get in front of diseases before they manifest in individuals.

After a participants voluntary genetic testing, the team checks for risks for many serious genomic conditions, including the top three identified by the Centers for Disease Control and Prevention as medically actionable (CDC Tier 1):

Most individuals affected by these genetic risks arent aware they have them. The project has genetic counsellors who will call our participants if they have a particular mutation and inform them, so they can talk with their physician and make important health decisions, Metcalf says.

Healthy Nevada Project participant Jordan Stiteler says the unexpected phone call saved her life.

Stiteler, a young mother, had family members who had suffered heart attacks and strokes at early ages. When she learned she carried the FH marker, she received guidance and support to help her make healthy lifestyle and medication choices. Soon several other family members joined the study to learn about their genetic risks.

Genetic screening also makes it possible to get in front of a cancer diagnosis. The ideal is to detect these mutations prior to any kind of a tumour becoming untreatable, Metcalf says. We have cases where people have told us, Thank you so much, you saved my life, because they were able to have preventive surgery, or they found a treatable Stage I tumour because of the results of genetic testing. Those are the things we live for in this project.

Since its initial 10,000 adult participants, the Healthy Nevada Project has grown to more than 52,000 individuals and expanded from northern Nevada to Las Vegas and its outlying areas in the southern part of the state.

According to Grzymski, more genome data from more people equates to greater statistical power and accuracy in understanding the links between who you are and your health outcomes. The underpinning of a population genetics study is access to data and then the ability to extract, transform and study the data for any of the myriad health outcomes we want to focus on, he says.

Providing the foundation for those efforts is a SAS platform, which the project runs in an on-premises computing environment.

The strength of the language, the depth, everything that SAS brings has been rock solid, Metcalf says. We have used SAS to comb through, manipulate and extract 200 terabytes of genetics and health records data. Setting the right parameters, we can look through a billion-record table of physician notes with no problem.

A data collection endeavour of this magnitude required cooperation between organisations, care protection of privacy, and a means to gain consent from participants. When executive leadership at Renown realised Desert Research Institute had a cadre of skilled data scientists on staff able to independently ingest and analyse Renowns electronic health records (EHR) data, they made the decision to begin sharing EHR data with the Center for Genomic Medicine at Desert Research Institute. Consequently, Desert Research Institute became a Health Insurance Portability and Accountability Act (HIPAA) business associate of Renowns.

Implementing and supporting processes to ensure patient privacy while facilitating research is a technically challenging and mentally taxing effort.The very real overprint of adhering to HIPAA requirements should not be underestimated in terms of project cost structure and staffing effort. The Healthy Nevada Project team works closely with Renowns compliance department and the Institutional Review Board at the University of Nevada, Reno, to ensure it adheres to the highest standards and practices of maintaining participant privacy. Healthy Nevada Project cohorts typically number in the tens of thousands of participants, if not more. The team is not looking at individuals in the EHR and would have great difficulty doing so as nearly all personally identifiable information is removed from the EHR to create a HIPAA-defined limited dataset as the first step of data ingestion.

Collecting genetic data requires receiving consent from participants via documents approved by the University of Nevada Institutional Review Board. Participants agree to be in the study knowing their genetic information and medical record will be used for medical research. Participating in the study is not mandatory and participants can withdraw at any time for any reason. The consent documents are written at an 8th grade level and are heavily vetted and tested for participant understanding.

The Healthy Nevada Project continues to bring a variety of data sources to the table for insights into population health, including:

The team uses SAS statistical models and analyses to report results to hospital administrators and research to the teams scientific peers for review.

The SAS platform has been the foundation bedrock of the Healthy Nevada Project, Metcalf says. We have immersed ourselves in the machine learning and AI procedures that SAS has and use those on a continual basis.

For example, a hospital wanted to reduce the time patients spend in the post-anesthesia care unit or stepdown room after surgery. To understand why some patients required more time there, the Healthy Nevada Project used a variety of SAS procedures, such as variable selection in the analytic process, to facilitate machine learning, allowing researchers to identify and eliminate possible causes as key factors.

The researchers found that the top factors most directly contributing to time spent in the stepdown room were the anesthesia type used, the patients age and the patients relative health.

The Healthy Nevada Project has elevated Nevadas profile in doing cutting-edge research, using data to deliver evidence-based, publishable results in peer-reviewed scientific journals and databases, says Grzymski. The entire team is proud of the work weve delivered and its impact as we continue to understand what makes people sick or well and enable preventive care.

Using Data and Analytics Across the Research Lifecycle to Improve Population Health read the whitepaper here.

About the author Sarah Newton Sarah is the manager of public sector health policy at SAS, helping governments leverage data and analytics to improve the health and wellbeing of their citizens.Sarah has a masters in public health, as well as extensive experience working on health policy at the federal and state level.Sarah can be contacted at [emailprotected].

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Improving US citizens' health though machine learning and AI - Global Government Forum