alzheimer s disease


New machine learning algorithm can help search new drugs

#artificialintelligence

LONDON, Feb 12: Researchers say they have developed a machine learning algorithm for drug discovery which is twice as efficient as the industry standard, and could accelerate the process of developing new treatments for diseases such as Alzheimer's. The team led by researchers at the University of Cambridge in the UK used the algorithm to identify four new molecules that activate a protein thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process, according to the study published in the journal PNAS. It is possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed "Machine learning has made significant progress in areas such as computer vision where data is abundant," said Alpha Lee from Cambridge's Cavendish Laboratory.


Machine Learning Algorithm Helps In The Search For New Drugs

#artificialintelligence

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease. The researchers, led by the University of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. The results are reported in the journal PNAS.


Machine Learning Helps Researchers in Hot Pursuit of New Drugs

#artificialintelligence

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease. The researchers, led by the University of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. The results are reported in the journal PNAS. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It's possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.


Cognetivity Advancing AI Platform to Detect Mental Health Disorders INN

#artificialintelligence

Sina Habibi, CEO of Cognetivity Neurosciences, spoke with INN about the company's partnership with DPUK and additional plans for 2019. At the recent Cantech Investment Conference, Sina Habibi, CEO of Cognetivity Neurosciences (CSE:CGN,OTCQB:CGNSF) spoke with the Investing News Network (INN) about the company's partnership with the Dementia Platform UK (DPUK) and additional plans for 2019. Habibi said the company will be putting more efforts into its artificial intelligence (AI) platform and collecting more data as it seeks to train its solutions to detect mental health disorders, like attention deficit hyperactivity disorder (ADHD). As it currently stands, Cognetivity is using AI and machine learning to aid in the early detection of dementia and Alzheimer's disease. On that note, in addition to the DPUK partnership, Habibi spoke to INN about a health application the company has that could be launched by the end of 2019.


AI That Understands Your Body Language

#artificialintelligence

Ubiquitous sensing, paired with machine learning, can amalgamate all of the signals we give off--from the timbre of our voice to the dilation of our pupils--to detect signs of conditions, such as Alzheimer's disease, years before a traditional diagnosis. Emerging "empathetic technology" sounds a bit scary to people who don't like the idea of machines reading our feelings. We want to think we can mask how we're feeling by controlling our faces, voices, and body language--and we don't entirely trust what machines do with this data. True, this is a thorny topic. But the potential for empathetic technology to improve our health and lives makes dealing with the uncomfortable questions worthwhile.


Machine learning algorithm helps in the search for new drugs

#artificialintelligence

Researchers have designed a machine learning algorithm for drug discovery which has been shown to be twice as efficient as the industry standard, which could accelerate the process of developing new treatments for disease. The researchers, led by the University of Cambridge, used their algorithm to identify four new molecules that activate a protein which is thought to be relevant for symptoms of Alzheimer's disease and schizophrenia. The results are reported in the journal PNAS. A key problem in drug discovery is predicting whether a molecule will activate a particular physiological process. It's possible to build a statistical model by searching for chemical patterns shared among molecules known to activate that process, but the data to build these models is limited because experiments are costly and it is unclear which chemical patterns are statistically significant.


Machine learning helps to identify early signs of Alzheimer's

#artificialintelligence

Researchers at the University of Southern California have discovered "hidden" indicators of Alzheimer's in medical data that could result in earlier diagnosis of the disease and better prognosis for patients. Using machine learning, USC researchers identified potential blood-based markers of Alzheimer's disease that could be detected with a routine blood test. "This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease," said Paul Thompson, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in USC's Keck School of Medicine. "In a very large database of health measures, it helped us discover predictive features of Alzheimer's disease that nobody suspected were there." Also See: MRI brain scans better ID people likely to develop Alzheimer's In their study, published in Frontiers in Aging Neuroscience, the USC research team analyzed medical data in the Alzheimer's Disease Neuroimaging Initiative database--collected from 829 older adults--to identify predictors of cognitive decline and brain atrophy during a one-year period.


USC Researchers Use AI to Detect Early Signs of Alzheimer's - USC Viterbi School of Engineering

#artificialintelligence

Neuroscientist Paul Thompson (left) with computer scientist Greg Ver Steeg. Nearly 50 million people worldwide have Alzheimer's disease or another form of dementia. While age is the greatest risk factor for developing the disease, researchers believe most Alzheimer's cases occur as a result of complex interactions among genes and other factors. But those factors and the role they play are not known--yet. In a new study, USC researchers used machine learning to identify potential blood-based markers of Alzheimer's disease that could help with earlier diagnosis and lead to non-invasive ways of tracking the progress of the disease in patients.


Using AI to Detect Early Signs of Alzheimer's

#artificialintelligence

Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress toward identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique – Correlation Explanation (CorEx) – to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer's disease (AD). We examined 829 participants (Mage: 75.3 6.9 years; 350 women and 479 men) from the Alzheimer's Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a 1-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis.


Curing diseases and delivering effective treatments with the cloud - Cloud Perspectives Blog

#artificialintelligence

Researchers trying to cure some of the world's least-understood diseases, such as Alzheimer's and Parkinson's, are discovering new and exciting opportunities in the cloud. With the ability to instantly access vast amounts of computing power, and without the burden of large initial investments or ongoing costs, cloud technology is making it easier for healthcare organizations to study complex disorders and develop innovative new treatments. This is helping lead to an era of more precise and effective medicine. Although the healthcare industry has used distributed computing networks to tackle large-scale health challenges before--such as the Folding@Home project, which allows individual PC users to contribute unused computing cycles to study how protein misfolding can lead to disease--there are a number of benefits to using a modern cloud computing solution. Cloud-based artificial intelligence (AI) and machine learning (ML) tools, for example, are helping healthcare organizations become more efficient and medical researchers develop better treatments for diseases.