Tag: Machine-Learning
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Computer models to analyze Huntington disease pathology
Rice University scientists have uncovered new details about how a repeating nucleotide sequence in the gene for a mutant protein may trigger Huntington’s and other neurological diseases. Researchers used computer models to analyze proteins suspected of misfolding and forming plaques in the brains of patients with neurological diseases. Their simulations confirmed experimental results by other…
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New tool to discover bio-markers for aging using In-silico Pathway Activation Network Decomposition Analysis (iPANDA)
Today the Biogerontology Research Foundation announced the international collaboration on signaling pathway perturbation-based transcriptomic biomarkers of aging. On November 16th scientists at the Biogerontology Research Foundation alongside collaborators from Insilico Medicine, Inc, the Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle and BioTime Inc. announced the publication of their proof of…
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Virtual clinical trials use mathematical modelling to predict melanoma response
Researchers from Moffitt Cancer Center’s Integrated Mathematical Oncology (IMO) Department are overcoming the limitations of common preclinical experiments and clinical trials by studying cancer through mathematical modeling. A study led by Alexander “Sandy” Anderson, Ph.D., chair of IMO, and Eunjung Kim, Ph.D., an applied research scientist, shows how mathematical modeling can accurately predict patient responses…
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Computer based model InFlo predicts cell signals and network activity
A multi-institution academic-industrial partnership of researchers led by Case Western Reserve University School of Medicine has developed a new method to broadly assess cell communication networks and identify disease-specific network anomalies. The computer-based method, called InFlo, was developed in collaboration with researchers at Philips and Princeton University and predicts how cells send signals across networks…
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Researchers use multi-task deep neural networks to automatically extract data from cancer pathology reports
Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs–a national network of organizations that systematically collect demographic and…
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Researchers identify suicidal behavior using machine learning algorithm on patients verbal and non-verbal data
A new study shows that machine learning is up to 93 percent accurate in correctly classifying a suicidal person and 85 percent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither. These results provide strong evidence for using advanced technology as a decision-support tool to help…
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Machine learning to intuitively predict chemical properties of molecules
Scientists from MIPT’s Research Center for Molecular Mechanisms of Aging and Age-Related Diseases together with Inria research center, Grenoble, France have developed a software package called Knodle to determine an atom’s hybridization, bond orders and functional groups’ annotation in molecules. The program streamlines one of the stages of developing new drugs. A paper on the…
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Researchers use machine learning to identify different cancer cell types
National Institutes of Health, COBRE Center for Cancer Research Development at Rhode Island Hospital, Rhode Island Foundation Medical Research Grant, Jason and Donna McGraw Weiss Brown University researchers have developed a new image analysis technique to distinguish two key cancer cell types associated with tumor progression. The approach could help in pre-clinical screening of cancer…
Medical News Observer 