Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here
Prediction of heart disease is most complicated and challenging task in the field of medical science. Heart disease is the most threatening one among various diseases as it can not be detected easily. Bad clinical decisions would cause death of a patient. In our project the hea...
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1–3. In principle, computational
Additionally, genetic disease databases, e.g., ClinVar [4], OMIM [5], and HGMD [6], have also amassed a large amount of information on known pathogenic or benign genetic variations. These databases have been widely used as references in the genetic diagnosis of Mendelian diseases. Pathogenic...
From the results, it has been seen that neural network predict heart disease with nearly 100% accuracy.doi:10.1038/467494aChaitrali S. DangareSulabha S. ApteSocial Science Electronic PublishingChaitrali S. Dangare et. al., "A Data Mining Approach for Prediction of Heart Disease using Neural...
The 2019 novel coronavirus (COVID-19) has spread quickly among people living in different countries and is impending 26,27,630 cases worldwide according to the statistics of European Center for Disease Prevention and Control. To control the spread of COVID-19, testing large numbers of alleged ...
In general the statistical conclusions here may not apply to preclinical compounds. These risk factors may best apply to drugs that have advanced to at least phase II clinical trials. The lack of data on the degree of the reported events is also a limitation of this analysis. A given ...
Because of the relative infrequency of the outcome (ie, 6-month incident AF) across the data set, there was an imbalance between the cases and controls. In the presence of imbalance, many classification algorithms, which often base classification on a probability of disease of greater than 50...
Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational...
Sufficient data is vital for proper modeling and hence further investigation is necessary to explore the possible trade-off between disease severity and data availability on new patient populations. Thirdly, though achieving favorable results on population CMI, the modeling method of clinical notes ...