Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0015674 (chronic fatigue syndrome)
 2,978 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Carnitine is a well-known cofactor for the beta-oxidation of long-chain fatty acid. It also plays a role in transport of acetyl moity for fatty acid and cholesterol synthesis, excretion of organic acid and xenobiotic acid as carnitine ester, and control of ratio of acetylCoA to CoA. Therapeutic effect of acetylcarnitine on Alzheimer disease and HIV-infection, and aberrant incorporation acetylcarnitine into brain under chronic fatigue syndrome have been reported. Carnitine deficiency causes hyperammonemia through suppression of gene expression of urea cycle enzymes. On the other hand, a large amount of carnitine has a therapeutic effect on hyperammonemia by still unclear mechanism. These suggest carnitine as a multifunctional biofactor.
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PMID:[Carnitine as a vitamin-like biofactor]. 1054 Aug 73

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.
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PMID:Causality on longitudinal data: Stable specification search in constrained structural equation modeling. 2865 54