The advent of artificial intelligence, especially the progress of deep neural networks, is expected to revolutionize genetic research and offer unprecedented potential to decode the complex relationships between genetic variants and disease phenotypes, which could mark a significant step toward improving our understanding of the disease etiology. While deep neural networks hold great promise for genetic association analysis, limited research has been focused on developing neural-network-based tests to dissect complex genotype-phenotype associations. This complexity arises from the opaque nature of neural networks and the absence of defined limiting distributions. We have previously developed a kernel-based neural network model (KNN) that synergizes the strengths of linear mixed models with conventional neural networks. KNN adopts a computationally efficient minimum norm quadratic unbiased estimator (MINQUE) algorithm and uses KNN structure to capture the complex relationship between large-scale sequencing data and a disease phenotype of interest. In the KNN framework, we introduce a MINQUE-based test to assess the joint association of genetic variants with the phenotype, which considers non-linear and non-additive effects and follows a mixture of chi-square distributions. We also construct two additional tests to evaluate and interpret linear and non-linear/non-additive genetic effects, including interaction effects. Our simulations show that our method consistently controls the type I error rate under various conditions and achieves greater power than a commonly used sequence kernel association test (SKAT), especially when involving non-linear and interaction effects. When applied to real data from the UK Biobank, our approach identified genes associated with hippocampal volume, which can be further replicated and evaluated for their role in the pathogenesis of Alzheimer's disease.

We're looking for an awesome Experienced Data Scientist to join our team on our journey to revolutionize personalized medicine About your Role / What will you be doing? BSc/MSc in Biomedical Engineering, Physics, Mathematics, Telecommunication Engineering, or equivalent degree with 5 years of relevant experience (PhD in a relevant field will be considered) in data science and artificial intelligence, ideally applied to healthcare and signal processing. As personalized medicine becomes the norm, scientists, patients, and healthcare systems worldwide face massive barriers in providing the right treatment to the right patient. Recognized by CB Insights and Financial Times as one of the most promising Digital Health companies in the world, iLoF is building an AI-powered breakthrough platform capable of transforming precision medicine. Our goal is to become the platform that democratizes access to personalized medicine for millions of people living with complex diseases around the globe.

Finding new ways to repurpose or combine existing drugs has proved to be a powerful tool to treat complex diseases. Drugs used to treat one type of cancer, for instance, have effectively strengthened treatments for other cancer cells. Complex malignant tumors often require a combination of drugs, or "drug cocktails," to formulate a concerted attack on multiple cell types. Drug cocktails can not only help stave off drug resistance but also minimize harmful side effects. But finding an effective combination of existing drugs at the right dose is extremely challenging, partly because there are near-infinite possibilities.

A healthcare professional uses innovative technology to monitor patient data. For those with complex, chronic diseases such as diabetes, gastrointestinal problems, autism, cancer, coronavirus and more, a solution for managing the multiple aspects of treatment may be available--as early as today. Open Health Network--a healthcare IT data solutions company--launched Constant Care, an integrative data system that allows for a robust level of patient care and monitoring, on Tuesday. The development helps to diagnose, treat and manage complex diseases in multiple areas of medicine and has been tested and adopted by Mount Sinai Hospital, Cleveland Clinic, New York University and Children's Hospital of Philadelphia. Constant Care incorporates artificial intelligence and machine learning to track patients' symptoms, progress and medication, and offer holistic health planning all in one portal.

The genetic etiologies of common diseases are highly complex and heterogeneous. Classic statistical methods, such as linear regression, have successfully identified numerous genetic variants associated with complex diseases. Nonetheless, for most complex diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides completed information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties and holds great promise for genetic data analyses (e.g., investigating genetic variants predisposing to a high-risk population), it has been rarely used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes and can be used to discover genetic variants predisposing to sub-populations (e.g., high-risk groups). We further integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex relationships between genetic variants and disease phenotypes. We also applied the proposed method to the genetic data from the Study of Addiction: Genetics and Environment(SAGE), investigating the relationships of candidate genes with smoking quantity.

RALEIGH – A startup in Raleigh backed by investor and Sprout Pharmaceuticals CEO Cindy Eckert, is unveiling Thursday a different approach to dealing with Alzheimer's that is powered by artificial intelligence, not drugs. And its solution is part of a new effort launched by the Women's Alzheimer's Movement Prevention Center at Cleveland Clinic, an internationally respected medical institution. ExtND is among the offerings from the Women's Alzheimer's center, which also was unveiled today in Las Vegas, according to a spokesperson for uMETHOD. Several medical institutions already are deploying the method. More than 5 million people are currently afflicted with Alzheimer's and the disease is the sixth leading cause of death in the US, according to the Alzheimer's Assocation.

New York: Scientists have used artificial intelligence (AI) to detect a new class of mutations behind autism spectrum disorder. Many mutations in DNA that contribute to disease are not in actual genes but instead lie in the 99 per cent of the genome once considered "junk." Even though scientists have recently come to understand that these vast stretches of DNA do in fact play critical roles, deciphering these effects on a wide scale has been impossible until now. Using AI, a research team led by Princeton University in the US has decoded the functional impact of such mutations in people with autism. The researchers believe this powerful method is generally applicable to discovering such genetic contributions to any disease.

You may have heard of dark matter, a mysterious form of lesser-studied matter that is thought to be composed of some as-yet undiscovered subatomic particles, but which makes up an astonishing 85% of the matter in the universe. These unexplored molecules and matter surrounding our genes make up more than half of the human genome -- but are a total conundrum in terms of what they encode and, more importantly, affect. The good folks at IBM and the Munich Leukemia Laboratory think they can help come up with some answers -- and they've used some groundbreaking A.I. algorithms to help. "Despite it making up a large portion of our genome, dark matter DNA has been ignored, as most scientists believe it plays no role," Laxmi Parida, IBM Research Fellow in Computational Genomics, told Digital Trends. "At IBM Research, we thought there might be more to dark matter DNA than we have been led to believe."

"One man's trash is another man's treasure," is a familiar expression. When it comes to health and genomics, "junk" DNA may turn out to be a goldmine. In a recent study, Princeton University-led researchers used whole-genome sequencing and artificial intelligence (AI) deep learning to identify the contribution of noncoding mutations to autism risk--demonstrating that mutations in "junk" DNA can contribute to a complex disease. The study was led by Princeton professor Olga Troyanskaya, who is also deputy director for genomics at the Flatiron Institute's Center for Computational Biology (CCB) in New York City, along with professor Robert Darnell of The Rockefeller University, also an investigator at the Howard Hughes Medical Institute. Published on May 27 in Nature Genetics, the study presented an AI deep learning framework that "predicts the specific regulatory effects and the deleterious impact of genetic variants," and used it on autism spectrum disorder (ASD).

2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic determinants remain largely elusive, with only a handful of identified candidate genes. Genome wide association studies (GWAS) promised to significantly enhance our understanding of genetic based determinants of common complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2 diabetes have been identified using GWAS. Standard statistical tests for single and multi-locus analysis such as logistic regression, have demonstrated little effect in understanding the genetic architecture of complex human diseases. Logistic regression is modelled to capture linear interactions but neglects the non-linear epistatic interactions present within genetic data. There is an urgent need to detect epistatic interactions in complex diseases as this may explain the remaining missing heritability in such diseases. In this paper, we present a novel framework based on deep learning algorithms that deal with non-linear epistatic interactions that exist in genome wide association data. Logistic association analysis under an additive genetic model, adjusted for genomic control inflation factor, is conducted to remove statistically improbable SNPs to minimize computational overheads.