Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challenging because of the vast size of the human genome, and because its consequences manifest across a wide range of cells, tissues and scales -- spanning from molecular to whole organism level. Here, we present Phenformer, a multi-scale genetic language model that learns to generate mechanistic hypotheses as to how differences in genome sequence lead to disease-relevant changes in expression across cell types and tissues directly from DNA sequences of up to 88 million base pairs. Using whole genome sequencing data from more than 150 000 individuals, we show that Phenformer generates mechanistic hypotheses about disease-relevant cell and tissue types that match literature better than existing state-of-the-art methods, while using only sequence data. Furthermore, disease risk predictors enriched by Phenformer show improved prediction performance and generalisation to diverse populations. Accurate multi-megabase scale interpretation of whole genomes without additional experimental data enables both a deeper understanding of molecular mechanisms involved in disease and improved disease risk prediction at the level of individuals.

Addressing the unavoidable bias inherent in supervised aging clocks, we introduce Sundial, a novel framework that models molecular dynamics through a diffusion field, capturing both the population-level aging process and the individual-level relative aging order. Sundial enables unbiasedestimation of biological age and the forecast of aging roadmap. Fasteraging individuals from Sundial exhibit a higher disease risk compared to those identified from supervised aging clocks. This framework opens new avenues for exploring key topics, including age- and sex-specific aging dynamics and faster yet healthy aging paths.

The most common cause of dementia worldwide is Alzheimer's disease (AD), a neurodegenerative disorder with no known cure. A new study published in Scientific Reports uses artificial intelligence (AI) machine learning (ML) and data from electronic health records (EHRs) to identify the important predictors for Alzheimer's disease and finds that a person's genetics outperforms age as a predictor for individuals who are 65 years of age and older. "Machine learning (ML) methods provide an attractive and effective alternative to traditional statistical regression models, especially in situations where one has a large number of features or predictors," wrote the authors of the National Institutes of Health (NIH) funded study led by Xiaoyi Raymond Gao at The Ohio State University College of Medicine, with Ohio State researchers Marion Chiariglione, Ke Qin and Douglas Scharre; the University of Miami researchers Karen Nuytemans and Eden Martin; and Yi-Ju Li at Duke University. Globally, Alzheimer's disease accounts for an estimated 60-70 percent of the over 55 million people with dementia and affects women disproportionately according to the World Health Organization (WHO). In the U.S., there are currently 6.7 million people aged 65 and older with living AD, of which almost two-thirds are women and that figure will increase significantly to an estimated 12.7 million Americans by 2050 according to the Alzheimer's Association.

The study found that machine learning techniques improved diet prediction by 10-20%.

Electronic health records (EHRs) contain patients' heterogeneous data that are collected from medical providers involved in the patient's care, including medical notes, clinical events, laboratory test results, symptoms, and diagnoses. In the field of modern healthcare, predicting whether patients would experience any risks based on their EHRs has emerged as a promising research area, in which artificial intelligence (AI) plays a key role. To make AI models practically applicable, it is required that the prediction results should be both accurate and interpretable. To achieve this goal, this paper proposed a label-dependent and event-guided risk prediction model (LERP) to predict the presence of multiple disease risks by mainly extracting information from unstructured medical notes. Our model is featured in the following aspects. First, we adopt a label-dependent mechanism that gives greater attention to words from medical notes that are semantically similar to the names of risk labels. Secondly, as the clinical events (e.g., treatments and drugs) can also indicate the health status of patients, our model utilizes the information from events and uses them to generate an event-guided representation of medical notes. Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes. To demonstrate the applicability of the proposed method, we apply it to the MIMIC-III dataset, which contains real-world EHRs collected from hospitals. Our method is evaluated in both quantitative and qualitative ways.

For quite some time now, fat accumulation around the heart has been linked with cardiovascular and metabolic disease. However, until now, there hasn't been a simple way to measure it. A team from the Queen Mary University of London has developed a new artificial intelligence (AI) tool that can automatically quantify these fat deposits from regular MRI scan images. There is a particular collection of fat tissue surrounding the surface of the heart called Pericardial adipose tissue (PAT). High levels of PAT (separate from body mass index and body weight) have been associated with a more significant risk of coronary heart disease and diabetes.

Altoida Inc. today announced a $6.3 million round of venture capital financing to bring its FDA-cleared and CE Mark-approved medical device and brain health data platform to patients, physicians and researchers around the globe. Led by a team of esteemed neuroscientists, physicians and computer scientists, Altoida uses digital biomarkers to drive better clinical outcomes for brain disease. The Series A round was led by M Ventures, the corporate venture capital arm of the science and technology company Merck KGaA, Darmstadt, Germany, with participation from Grey Sky Venture Partners, VI Partners AG, Alpana Ventures, and FYRFLY Venture Partners. The new capital will be used to further expand Altoida's global presence with an immediate focus on commercialization activities in the US and EU markets. "Altoida is at the forefront of a new era to leverage Artificial Intelligence and Machine Learning to assess brain health," said Alexander Hoffmann, Principal, New Businesses at M Ventures.

Accumulation of fat specifically around the heart has long been linked to cardiovascular and metabolic disease but until now there hasn't been a simple way to measure this. A new artificial intelligence tool has been developed that can quantify these fat deposits from regular MRI images. Pericardial adipose tissue (PAT) is a particular collection of fat tissue surrounding the surface of the heart. High levels of PAT, separate to body weight or body mass index, have been linked to increased risk of diabetes and coronary heart disease but the association has remained a hypothesis due to measurement challenges. The best way we can currently measure PAT levels is using a computed tomography (CT) scan.

A combination of different healthy lifestyle habits such as being physically active, not smoking and a high-quality diet can reduce the risk of developing Alzheimer's. Researchers from the Rush University Medical Center examined data on nearly 3,000 people from two longitudinal studies run by the National Institute for Aging. They found that people in the dataset who adhered to four or five'healthy behaviour' types had a 60 per cent lower chance of developing Alzheimer's disease. These included being physically active, not smoking, light-to-moderate alcohol consumption, eating a high-quality diet, and performing cognitive activities. They found that people in the dataset who adhered to four or five'healthy behaviour' types had a 60 per cent lower chance of developing Alzheimer's disease The National Institute on Aging (NIA) funded research adds to existing evidence that lifestyle factors play a part in mitigating Alzheimer's disease risk, the team said.

The deep learning algorithm, developed by researchers at the Boston University School of Medicine, uses a combination of brain magnetic resonance imaging (MRI) testing to measure cognitive impairment, along with data on age and gender, which helps to accurately predict the risk of Alzheimer's Disease. Alzheimer's disease is the primary cause of dementia worldwide. One in 10 people age 65 and older has Alzheimer's dementia and it is the primary cause of dementia worldwide. The study has been published in the journal Brain. The researchers used MRI scans of the brain, demographics, and clinical information of individuals with Alzheimer's disease as well as ones with normal cognition.