When it comes to neurodegenerative diseases, Alzheimer's disease is considered one of the worst. It promotes the onset of dementia--an irreversible decline in thinking, memory, and ability to perform simple everyday tasks--in around 60 to 70 percent of patients. Now, researchers have suggested that the disease can be divided into four distinct subtypes; potentially opening doors for individualized treatment among sufferers. In an international study, scientists illustrated that tau--proteins found in neurons that are associated with neurodegenerative conditions--spread through the brain in four distinct patterns. This leads to varied symptoms and outcomes among affected individuals. Machine learning (ML) was leveraged by the authors to distinguish between the different subtypes.
Verge Genomics is taking a novel approach to speed drug discovery for devastating neurodegenerative diseases such as Parkinson's, Alzheimer's and ALS. Rather than spending exhaustive time with animal testing, the San Francisco-based company goes straight to the source. "To succeed in humans, you need to start with humans," says CEO Alice Zhang, whose drug discovery combines artificial intelligence with human genomics. Animal testing can be successful in predicting success for some drugs, but when it comes to neurodegenerative diseases, human brains are far more complex. To keep human patients at the center of the drug discovery process, Zhang's company has built one of the largest databases of brain tissue sequences in the world, with tissue from more than 1,000 human brains.
Going to college or university and getting a degree may help prepare you for the working world -- but it won't stop age-related brain shrinkage, a study found. Education has long been associated with health benefits -- including a decreased risk of heart disease, a delayed peak in cognitive abilities and lower risk of dementia. It has been contended that greater levels of education in childhood and early adulthood can slow the rate of brain aging in late adulthood. To put this to the test, an international team of researchers analysed the brain structure of some 2,000 people at different stages of their lives. They found that while higher education can lead to larger brain volumes, it does nothing significant to stave off the ravages of age.
But the earlier it's diagnosed, the more chances there are to delay its progression. Our joint team of researchers from IBM and University of Tsukuba has developed an AI model that could help detect the onset of the mild cognitive impairment (MCI), the transitional stage between normal aging and dementia -- by asking older people typical daily questions. In a new paper published in Frontiers in Digital Health journal, we present the first empirical evidence of tablet-based automatic assessments of patients using speech analysis -- successfully detecting mild cognitive impairment (MCI), the transitional stage between normal aging and dementia. Unlike previous studies, our AI-based model uses speech responses to daily life questions using a smartphone or a tablet app. Such questions could be as simple as inquiring someone about their mood, plans for the day, physical condition or yesterday's dinner. Earlier studies mostly focused on analyzing speech responses during cognitive tests, such as asking a patient to "count down from 925 by threes" or "describe this picture in as much detail as possible."
Algorithms similar to those used by Netflix, Amazon and Facebook have shown the ability to decipher the'biological language' of cancer, Alzheimer's and other neurodegenerative diseases. Researchers trained a large-scale language model with a recommendation AI to look at what happens when something goes wrong with proteins that leads to the development of a disease. The work, conducted by St. John's College and the University of Cambridge, programed the algorithm to learn the language of shapeshifting droplets of proteins found in cells in order to understand their function and malfunction. By learning these protein droplets' language, the team can then'correct the grammatical mistakes inside cells that cause disease.'' Professor Tuomas Knowles, a Fellow at St John's College, said: 'Any defects connected with these protein droplets can lead to diseases such as cancer. 'This is why bringing natural language processing technology into research into the molecular origins of protein malfunction is vital if we want to be able to correct the grammatical mistakes inside cells that cause disease.' Machine learning technology has made waves in the tech industry – Netflix uses it to recommend series, Facebook's suggest someone to friend and Amazon's Alexa has an algorithm to recognize people based on their voice.
Powerful algorithms used by Netflix, Amazon and Facebook can'predict' the biological language of cancer and neurodegenerative diseases like Alzheimer's, scientists have found. Big data produced during decades of research was fed into a computer language model to see if artificial intelligence can make more advanced discoveries than humans. Academics based at St John's College, University of Cambridge, found the machine-learning technology could decipher the'biological language' of cancer, Alzheimer's, and other neurodegenerative diseases. Their ground-breaking study has been published in the scientific journal PNAS today and could be used in the future to'correct the grammatical mistakes inside cells that cause disease'. Professor Tuomas Knowles, lead author of the paper and a Fellow at St John's College, said: "Bringing machine-learning technology into research into neurodegenerative diseases and cancer is an absolute game-changer. Ultimately, the aim will be to use artificial intelligence to develop targeted drugs to dramatically ease symptoms or to prevent dementia happening at all."
We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.
The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases. The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer's disease. In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 "significant" genetic differences among Alzheimer's and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer's symptoms develop and a patient's brain is irreversibly damaged.
For media inquiries, please contact Gina DiGravio: 617-224-8962, firstname.lastname@example.org Warning signs for Alzheimer's disease (AD) can begin in the brain years before the first symptoms appear. Spotting these clues may allow for lifestyle changes that could possibly delay the disease's destruction of the brain. "Improving the diagnostic accuracy of Alzheimer's disease is an important clinical goal. If we are able to increase the diagnostic accuracy of the models in ways that can leverage existing data such as MRI scans, then that can be hugely beneficial," explained corresponding author Vijaya B. Kolachalama, PhD, assistant professor of medicine at Boston University School of Medicine (BUSM).