2022
DOI: 10.3390/biomedicines10010116
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Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

Abstract: An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed.… Show more

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Cited by 6 publications
(5 citation statements)
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“…The application of the study results, which were obtained using Caucasian populations, to other ethnic populations is questionable. Asian populations have a higher level of clopidogrel resistance [ 50 , 51 , 52 ]. In our pooled studies, most of the combinations used for the DAPTs involve clopidogrel plus aspirin.…”
Section: Discussionmentioning
confidence: 99%
“…The application of the study results, which were obtained using Caucasian populations, to other ethnic populations is questionable. Asian populations have a higher level of clopidogrel resistance [ 50 , 51 , 52 ]. In our pooled studies, most of the combinations used for the DAPTs involve clopidogrel plus aspirin.…”
Section: Discussionmentioning
confidence: 99%
“…They are involved in mitochondrial autophagy and DNA repair pathways, accelerating the healing process of CAD. However, when the BTBD7 gene acts alone, it is engaged in clopidogrel resistance, leading to treatment failure in peripheral arterial disease 57 (Figure 1). Thus, BTBD7 may be an independent predictor of CAD.…”
Section: Effects Of Btbd7 In Normal Tissuesmentioning
confidence: 99%
“…Machine learning has been a powerful paradigm since it uses a knowledge-based model for building the training system. Recently, there have been attempts to design ML systems covering several applications such as diabetes [59,60,165], neonatology [166], gene [167,168], coronary artery disease risk stratification [169,170], carotid plaque classification [171], cancer imaging such as thyroid [61,172,173], breast [174], ovarian [64,175], prostate [63,176], etc. The second key benefit of ML is its ability to handle the nonlinearity between the combination of risk factors (or covariates) and the gold standard.…”
Section: Machine Learning-based System For Cvd/stroke Risk Assessment...mentioning
confidence: 99%