In recent years, with the appearance of the COVID-19 pandemic, numerous publications relevant to this disease have been issued. Because of the massive volume of publications, an efficient retrieval system is necessary to provide researchers with useful information if an unexpected pandemic happens so suddenly, like COVID-19. In this work, we present a method to help the retrieval system, the Covrelex-SE system, to provide more high-quality search results. We exploited the power of the large language models (LLMs) to extract the hidden relationships inside the unlabeled publication that cannot be found by the current parsing tools that the system is using. Since then, help the system to have more useful information during retrieval progress.

At the National Heart Lung and Blood Institute (NHLBI), CIO Alastair Thomson says machine learning (ML) played a major role in COVID-related research. The National COVID Cohort Collaborative (NC3) clearly demonstrates the value of securely bringing together data in one place, where it can be analyzed by thousands of researchers. Through collaboration with various healthcare and cloud service providers, the NC3 and ML helped NIH identify potential participants for the RECOVER initiative. "The utility of this really became clear when NIH launched the RECOVER initiative, which is dealing with post-acute COVID syndrome, or long COVID," Thomson said at the annual AFCEA's Health IT Summit last week. "They were able to use machine learning with that data to identify the key characteristics, what we call a phenotype, for long COVID."

Members of the GenSLMs team received the Gordon Bell Special Prize for HPC-Based COVID-19 Research at the SC22 conference. Scientists from Argonne National Laboratory and a team of collaborators have won the 2022 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research for their method of quickly identifying how a virus evolves. Their work in training large language models (LLMs) to discover variants of SARS-CoV-2 has implications to biology beyond COVID-19. The researchers leveraged Argonne's supercomputing and AI resources to develop and apply LLMs toward tracking how a virus can mutate into more dangerous or more transmissible variants, or a variant of concern (VOC). Existing methods to track VOCs can be slow.

A recent study published in IEEE Intelligent Systems discussed the role of artificial intelligence (AI) in combating the coronavirus disease 2019 (COVID-19) pandemic. The COVID-19 pandemic has reshaped the world in an unprecedented way, resulting in more than 583 million cases and six million deaths to date. Yet, there is no clear sign of an end to the ongoing crisis. AI has been instrumental during the pandemic in supporting telemedicine, communications, automated, virtual, and economic activities. AI has been at the center of the fight against COVID-19 from detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent, identifying COVID-19 symptoms to saving lives and curtailing the spread of the virus.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Can artificial intelligence (AI) be legally listed as an inventor? After all, if AI can legally invent products, the number of patents on drug-discovery tools will shoot up fast. The issue is currently before a United States court. The U.S. Court of Appeals heard arguments on that question again last week, and the ruling could affect the pace of AI technology development, particularly within the pharmaceutical and life science industries.

An artificial intelligence-enabled synthetic data generator that converts clinical data of any kind into equivalent, mock versions that don't expose sensitive patient-identifying details is being put to use as a component of the National Institutes of Health-steered National COVID Cohort Collaborative, or N3C effort. "The NIH's N3C initiative is a result of the urgent need for understanding of COVID both to develop better patient care and understand the impacts on individuals and the health system as a whole," Dr. Michael D. Lesh told Nextgov this week. Lesh--the co-founder and CEO of Syntegra, the company behind the synthetic data engine--shed light on how the tool works, and a new partnership between the business, NIH and the Bill and Melinda Gates Foundation that underpins this fresh endeavor. In June 2020, not long after the novel coronavirus pandemic disrupted nearly every aspect of American life, NIH launched N3C to accelerate COVID-19 research and new medical breakthroughs. The collaborative pursuit, according to a June press release, intends to systematically capture relevant data from participating health care providers across the country, aggregate that data into accessible formats, and in-turn help approved users harness research insights from that harmonized information, via the NCATS N3C Data Enclave.

Medical researchers since March have been pivoting projects to focus on COVID-19, driving the critical need for machine learning and imaging analysis tools to support big data initiatives, according to a Nov. 13 Wall Street Journal report. At the Center for Clinical Data Science, which is part of Boston-based Massachusetts General Hospital and Brigham and Women's Hospital, multidisciplinary teams with artificial intelligence skills have been vital for organizing and sifting through COVID-19 data sets. "Many of us dropped all other research and tried to focus entirely on doing COVID modeling," Jayashree Kalpathy-Cramer, PhD, scientific director of the Center for Clinical Data Science, told the publication. The work required large amounts of data storage, easy access to data and enough computer power to build complex AI models. Over the past several months, researchers from various MGH task forces have collaborated on AI algorithms in numerous ways, including using the models to predict which COVID-19 patients will require more advanced treatments and to identify how many intensive care unit beds could be needed at a particular time.

Each week, 51 processes (one for each state and one for the U.S.) are run in parallel (at the same time). "Running all 51 analyses on our own computers would take an extremely long time. The analysis places heavy demands on the hardware running the computations, which makes crashes somewhat likely on a typical laptop. We get all 51 done in the time it would take to do 1," said Corsetti. "It is our goal to provide accurate data that helps our country."

Over the past six months the world has been focused on the singular goal of developing treatments, vaccines, and containment strategies but what no one expected was how the tech world would rise to the challenge presented by Covid-19. While front line responders and essential workers put their lives on the line, researchers and scientists turned to artificial intelligence (AI) for answers. In mere months, emerging technologies have been rolled out and leveraged to full effect to vastly boost computing power, dramatically increase access to high-performance computing (HPC), and accelerate research by orders of magnitude. AI and HPC are being leveraged to not only assess and develop treatments but also to create potential vaccines, manage shutdowns and reopenings, analyze and enable access to digital medical records, and even to help develop better face masks. Samsung Semiconductor technology has played a particularly essential role in the fight against Covid-19.

In a short period, many research publications that report sets of experimentally validated drugs as potential COVID-19 therapies have emerged. To organize this accumulating knowledge, we developed the COVID-19 Drug and Gene Set Library (https://amp.pharm.mssm.edu/covid19/), a collection of drug and gene sets related to COVID-19 research from multiple sources. The platform enables users to view, download, analyze, visualize, and contribute drug and gene sets related to COVID-19 research. To evaluate the content of the library, we compared the results from the six in-vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe low overlap across screens while highlighting overlapping candidates that should receive more attention as potential therapeutics for COVID-19.