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ByCatherine Graham

Though almost every cell in your body contains a copy of each of your genes, only a small fraction of these genes will be expressed, or turned on. These activations are controlled by specialized snippets of DNA called enhancers, which act like skillful on-off switches. This selective activation allows cells to adopt specific functions in the body, determining whether they becomefor exampleheart cells, muscle cells, or brain cells.

However, these enhancers don't always turn on the right genes at the right time, contributing to the development of genetic diseases like cancer and diabetes.A team of Johns Hopkins biomedical engineers has developed a machine-learning model that can predict which enhancers play a role in normal development and diseasean innovation that could someday power the development of enhancer-targeted therapies to treat diseases by turning genes on and off at will. The study results appeared in Nature Genetics.

Michael Beer

Professor, Whiting School of Engineering

"We've known that enhancers control transitions between cell types for a long time, but what is exciting about this work is that mathematical modeling is showing us how they might be controlled," said study leader Michael Beer, a professor of biomedical engineering and genetic medicine at Johns Hopkins University.

Human cells are highly dynamic and change over the course of our development or in response to our environment. Beer's team was specifically interested in understanding how enhancers influence "cell fate decisions," or the process when one cell transitions into another cell type during development. Errors in cell fate decisions are a major factor in disease development.

First, the team built a machine-learning model to simulate how genes regulate each other in a cell. From there, they used large-scale selection experiments, known as genetic screens, to identify several key genes that control cell fate decisions, as well as enhancers that turn the expression of these genes on and off. Next, they used the CRISPR gene-editing system to disrupt or stimulate potential enhancers and observe the effects on gene expression. This also allowed the researchers to test which enhancers accelerated the transition of embryonic stem cells to endodermal cells, which is the first step in forming the stomach or pancreas. Finally, the team used the data from their genetic screens to model the DNA features, such as physical structure or modifying marks, that are best at predicting which enhancers will have the biggest impact on cell fate.

Using this new computational approach, the team uncovered two surprising properties about the interplay between enhancer activity and cell fates. First, enhancers that have a strong impact on gene expression were all in DNA loops enclosing the target genea discovery that reveals more precise information about how the genomic location of an enhancer helps to activate a target gene. Second, stimulating enhancers only influenced gene activation while the cells were transitioning from one type to another, and the effect disappeared once the transition was complete.

"This may explain why historically it has been so difficult to connect enhancer variants with the associated disease. Many of these disease-associated enhancers identified by genetics may only change gene expression significantly when the cell is transitioning to a new cell type," said Beer.

Their results suggest that CRISPR screens designed to detect enhancers during a cell state transition will have greater sensitivity. The team believes that their work could help other researchers study enhancer mutations by using models based on DNA features to predict which are most likely to impact cell fate, and design experiments to identify harder-to-detect enhancers by stimulating cell transitions.

According to Beer, the study results indicate that a cell's fate may not be set in stone and that, with more research, scientists will be able to determine which enhancers are connected to certain genetic diseases, allowing them to alter enhancer function to prevent or cure genetic maladies.

"This is a new tool to study the interactions between genes and regulatory elements such as enhancers, and that will enable insights into how to correct abnormal cellular behaviors during disease," said Beer. "We expect that our work could someday spur the development of therapies for cancer or other genomic diseases by targeting combinations of enhancers with CRISPR."

Additional study co-authors include Jin Woo Oh, Wang Xi, Dustin Shigaki of Johns Hopkins. Other authors are from Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, Weill Cornell Medicine, and the Albert Einstein College of Medicine.

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After achieving the elite AI and Machine Learning in Microsoft Azure specialization, Protiviti has launched new Microsoft Artificial Intelligence (AI) Center of Excellence and AI Solutions to help clients adopt responsible AI

MENLO PARK, Calif., Aug. 9, 2023 /PRNewswire/ -- Global consulting firm Protiviti has achieved the Artificial Intelligence (AI) and Machine Learning (ML) in Microsoft Azure specialization. Microsoft solution specializations recognize partners for deep expertise in the designated capability areas and illustrate the partner has met demanding requirements to demonstrate solution competency, all validated by a third-party audit. With the AI and ML in Microsoft Azure specialization, Protiviti has shown proven capabilities enabling customer adoption of Al and implementing Azure-based solutions for Al-powered applications and machine learning.

As part of this Microsoft specialization achievement, Protiviti has launched a Microsoft AI Center of Excellence (MSAI COE) and new Microsoft-certified AI solutions. The MSAI COE is focused on researching and developing AI solutions that drive impactful change to businesses, and how they operate and serve their customers. It also brings together a high concentration of Microsoft MVPs, world-class data scientists, award-winning AI solutions, and Protiviti's risk heritage focusing on delivering trusted and secure AI solutions. Protiviti's MSAI COE will operate and deliver solutions that embed Microsoft's responsible AI principles: fairness, inclusiveness, reliability and safety, transparency, security and privacy, and accountability.

Within Protiviti's suite of 50+ AI solutions and accelerators, the company has developed Microsoft-certified offerings to help clients get started with generative AI, including: the Generative AI Roadmap and Generative AI Proof of Concept. The Generative AI Accelerated Roadmap is a four-week assessment to define objectives and develop a strategic plan for deploying Microsoft Azure-based Open AI-LLM applications across business functions. Generative AI Proof of Concept provides a six-week solution utilizing a proven methodology and a knowledgeable team to accelerate ideation to functional prototype.

"Protiviti is proud to collaborate with Microsoft to be a leading provider of AI enabled capabilities," said Christine Livingston, global leader of Protiviti's AI services. "Together with Microsoft, we look forward to helping organizations utilize AI solutions responsibly to drive impactful transformation in their businesses."

Microsoft's partnership with OpenAI and the acceleration of large language models have unlocked new ability to deliver transformational AI-enabled capabilities and experiences, while maintaining the security and privacy of organizational data. Protiviti's MSAI COE will enhance its 50+ proprietary models with the latest advancements in Microsoft and OpenAI technologies.

"We're excited to be a leader for Microsoft-enabled AI solutions and look forward to driving business value to our clients," said Tom Andreesen, Protiviti's Microsoft global alliance leader. "The MSAI COE will focus on leveraging the latest Microsoft AI capabilities and driving research and resulting solutions that consider the broad risk considerations in today's business environment and is well aligned with Protiviti's rich risk management and governance history."

To learn more about Microsoft AI, Protiviti and Microsoft will host a webinar on August 17 to discuss "Are You Ready for (Responsible) AI? How to Embrace the Future of Productivity and Limitless Innovation with Microsoft AI", register here.

About Protiviti

Protiviti (www.protiviti.com) is a global consulting firm that delivers deep expertise, objective insights, a tailored approach and unparalleled collaboration to help leaders confidently face the future. Protiviti and its independent and locally owned Member Firms provide clients with consulting and managed solutions in finance, technology, operations, data, digital, legal, governance, risk and internal audit through its network of more than 85 offices in over 25 countries.

Named to the 2022 Fortune 100 Best Companies to Work For list, Protiviti has served more than 80 percent of Fortune 100 and nearly 80 percent of Fortune 500 companies. The firm also works with smaller, growing companies, including those looking to go public, as well as with government agencies. Protiviti is a wholly owned subsidiary of Robert Half Inc. (NYSE: RHI). Founded in 1948, Robert Half is a member of the S&P 500 index.

SOURCE Protiviti

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In the dynamic landscape of modern technology, where innovation is the norm and data flows like an ever-expanding river, machine learning stands as the beacon of transformation. At its core, machine learning empowers computers to unravel intricate patterns, glean insights from vast datasets, and ultimately make intelligent decisions. Among the arsenal of tools that machine learning provides, two techniques shine particularly brightly: classification and regression. These twin pillars of predictive modeling are the architects of smart decision-making, enabling machines to not only comprehend the nuances of diverse data but also predict future outcomes with uncanny accuracy.

In the vast and intricate tapestry of machine learning, one captivating technique takes center stage, orchestrating a symphony of data like a virtuoso conductor leading a mesmerizing musical performance. This technique, known as classification, is the enchanting art of imbuing machines with the ability to unravel patterns, categorize information, and make decisions that resonate with unerring accuracy.

Imagine a realm where computers, akin to perceptive artists, possess the dexterity to discern the intricate strokes of a digital canvas. Classification empowers them to sort, classify, and label data points with an intuitive finesse that mirrors the human minds capacity to differentiate between shapes, colors, and textures. Its as though the machine is not merely analyzing numbers and variables, but is gracefully dancing with the essence of the data itself.

From identifying fraudulent transactions in the financial world to diagnosing diseases from medical images, classification stretches its imaginative wings across a diverse array of applications. Its the ingenious algorithm behind email filters that deftly segregate spam from legitimate messages, just as a seasoned sommelier distinguishes the subtle notes of fine wine.

As we delve deeper into the captivating realm of classification, we unravel the threads of a narrative where machines don the mantle of interpreters, translating the language of data into a harmonious melody of understanding.

In the realm of machine learning, where algorithms unfold like pages of a captivating novel, there exists a technique that resembles the crystal ball of ancient fortune-tellers, offering glimpses into the mysteries of the future. This technique, known as regression, is a masterful blend of art and science that empowers machines to peer into historical data, decipher intricate relationships, and forecast forthcoming outcomes with a precision that borders on the magical.

Imagine a virtual oracle that harnesses the echoes of the past, infusing them with the wisdom of algorithms to unveil a world yet to unfold. Regression is the compass guiding us through the terrain of continuous variables, enabling us to navigate complexities ranging from economic predictions to ecological phenomena. Its as though machines have been bestowed with the power to unveil a tantalizing glimpse of the future, just as a seasoned astronomer deciphers the movements of celestial bodies to predict cosmic events.

Consider a scenario where industries pivot and strategize based on insights that transcend time. A business, for instance, could employ regression to illuminate the path ahead by analyzing historical sales data, market trends, and external influences. Armed with this predictive prowess, the business might effortlessly adapt to changing conditions, pre-emptively altering its course to thrive in an ever-shifting landscape.

In our exploration of the captivating universe of regression, we embark on a voyage that stretches beyond numbers and equations. Its a journey where algorithms wield the brushstrokes of insight, painting a canvas of future possibilities and enabling us to chart our course with a blend of scientific acumen and imaginative foresight.

In the intricate labyrinth of machine learning, where algorithms weave a tapestry of intelligence, there exists a remarkable crossroads where two distinct techniques converge, creating a symphony of insight and foresight. This juncture, where classification and regression intersect, is a realm of boundless potential a place where machines harmoniously blend the art of categorization with the science of prediction, transcending boundaries and unlocking new dimensions of decision-making mastery.

Imagine a realm where the analytical mind meets the visionary eye, where data is not just dissected but also projected forward in time. Here, classification the ability to discern patterns and categorize information joins hands with regression the technique of forecasting continuous outcomes to form a dynamic partnership. Its akin to a skilled conductor leading an orchestra, seamlessly blending diverse instruments into a crescendo of harmonious melodies.

This fusion finds its resonance in fields as diverse as healthcare and finance. For instance, envision a scenario where a medical professional seeks to chart a patients trajectory. Classification steps in to diagnose and categorize the ailment based on symptoms, medical history, and tests. But the tale doesnt conclude there; regression then steps onto the stage, forecasting the patients recovery time and potential outcomes. Its an intricate pas de deux between understanding the present and predicting the future, resulting in a comprehensive narrative that guides medical decisions with a profound blend of accuracy and foresight.

As we venture into this captivating convergence of minds, we embark on a voyage that unveils the synergy of classification and regression. This harmonious merger offers a glimpse into the future where data, once disparate, becomes a unified source of wisdom, empowering us to make decisions that are not only informed by the past but also shaped by a visionary outlook. Together, classification and regression dance to a rhythm of discovery, illuminating the path toward a horizon where intelligence is a tapestry woven with threads of both understanding and prophecy.

In the grand tapestry of technological evolution, where the threads of innovation weave a complex narrative, we find ourselves at a crossroads where intelligence and possibility converge. The journey through the landscapes of classification and regression in machine learning has unveiled a panorama of potential, where data transforms into discernment, and predictions blossom into strategic prowess.

As we step back to contemplate the significance of this expedition, we are met with a resounding revelation: the fusion of classification and regression holds the key to empowering the future. It is not merely a journey through algorithms and equations; it is a voyage into a world where decisions, once rooted in uncertainty, are now fortified by the pillars of insight and foresight.

Classification, with its meticulous dance of patterns, teaches machines to decipher the language of data, categorizing and labeling with a precision that mirrors human intuition. Regression, the art of gazing into the crystal ball of historical information, equips us with the ability to predict and optimize, transforming the future from an enigma into a realm of calculated possibilities.

This synergy isnt limited to a single domain it extends its tendrils into every facet of human endeavor. From medicine to finance, agriculture to energy, the marriage of classification and regression reshapes industries, informs strategies, and guides choices that resonate with efficacy and ingenuity.

In a world propelled by intelligent algorithms, we embark on a future where decisions are not just made, but strategically crafted. The capabilities forged by classification and regression offer a tapestry of insights, inviting us to step into a landscape where innovation and informed choices are the cornerstones of progress.

So, as we stand on the precipice of this transformative era, let us embrace the elegance of these techniques, for they are the conduits through which the realm of machines transcends into one of partners, collaborators, and co-creators. The journey has been exhilarating, but the path ahead is even more luminous an ascent towards a horizon where intelligence is not just a tool, but a beacon guiding humanity toward a future of limitless potential.

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