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Through the use of machine learning, artificial intelligence can be able to substantially aid doctors in patient diagnosis through the analysis of mass electronic health records (EHRs). [22] AI can help early prediction, for example, of Alzheimer's disease and dementias, by looking through large numbers of similar cases and possible treatments ...
The QLattice mainly targets scientists, and integrates well with the scientific workflow. [2] [6] It has been used in research into many different areas, such as energy consumption in buildings, [3] water potability, [7] heart failure, [8] pre-eclampsia, [4] Alzheimer's disease, [9] hepatocellular carcinoma, [9] and breast cancer.
Deep learning applications have been used for regulatory genomics and cellular imaging. [33] Other applications include medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. [34] Deep learning has been applied to regulatory genomics, variant calling and pathogenicity scores. [35]
AI is revolutionizing the drug delivery systems. AI technology can assist in identifying biological targets for pharmaceuticals, evaluating the pharmacological profiles of potential drugs, and analyzing genetic information; in the future, this could lead to drugs personalized to an individual, targeted cancer treatments, and edible vaccines.
Disease Informatics (also known as infectious disease informatics) studies the knowledge production, sharing, modeling, and management of infectious diseases. [1] It became a more studied field as a by-product of the rapid increases in the amount of biomedical and clinical data widely available, and to meet the demands for useful data analyses of such data.
A case study involving 5 use cases of genomic prediction demonstrate that SNPs with extremely small p-values, and by implication extreme OR do not give extreme differences in discrimination. [16] They point out that use of significantly associated genetic variants does not necessarily lead to better classification.
Owkin’s research on AI/ML has led to a number of publications that focus on machine learning methodologies and the development of predictive models for different disease areas, mainly oncology. Courtiol, Pierre et al. “Deep learning-based classification of mesothelioma improves prediction of patient outcome”, Nat Med 25, 1519–1525 (2019 ...
The fingerprint is predicted from the given spectrum and its corresponding fragmentation tree using deep kernel learning, [26] [10] which is a combination of kernel methods and deep neural networks. Not only the top scoring molecular formula but multiple high-scoring molecular formula candidates are considered.