Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals

Abstract

Folate metabolism, also known as one-carbon metabolism, is required for several cellular processes including DNA synthesis, repair and methylation. Impairments of this pathway have been often linked to Alzheimer’s disease (AD). In addition, increasing evidence from large scale case-control studies, genome-wide association studies, and meta-analyses of the literature suggest that polymorphisms of genes involved in one-carbon metabolism influence the levels of folate, homocysteine and vitamin B12, and might be among AD risk factors. We analyzed a dataset of 30 genetic and biochemical variables (folate, homocysteine, vitamin B12, and 27 genotypes generated by nine common biallelic polymorphisms of genes involved in folate metabolism) obtained from 40 late-onset AD patients and 40 matched controls to assess the predictive capacity of Artificial Neural Networks (ANNs) in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being affected by dementia of Alzheimer’s type. Moreover, we constructed a semantic connectivity map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (being AD or control). TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 16 variables that allowed specialized ANNs to discriminate between AD and control subjects with over 90% accuracy. The semantic connectivity map provided important information on the complex biological connections among one-carbon metabolic variables highlighting those most closely linked to the AD condition.

Artificial Neural Networks Link One-Carbon Metabolism to Gene-Promoter Methylation in Alzheimer’s Disease

“…Auto-CM has been successfully applied to AD datasets, for example to link serum folate and hcy levels to brain atrophy [9], to unravel genetic polymorphisms linked to impaired one-carbon metabolism [10], to detect the connections among studied variables in the nun study [8], to differentiate among various forms of dementia [10], or to detect the predictors of response to cholinesterase inhibitors [12].…”

Deep Neural Network Classification method to Alzheimer?s Disease Detection

“…3. Architecture of DNN [15] The cost function, C (W) of the DNN can be used for the supervisedfine-tuning byusing the mean squared error (MSE), L1-norm, and L2-norm terms and it can be represented as follows [17].…”

MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms

“…AutoCM provides a bottomup approximation of the matrix of tensors among the variables or among the patterns in the dataset [11,34]. AutoCM has been applied with success in many fields [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]; in this recent but ample literature, partial comparisons with other algorithms such as PCA, EU or LC have already been carried out, depending on the nature of the problem under analysis, and in addition, in each case, the performance of AutoCM has also been evaluated by field experts against the benchmark of their available toolbox to date. In all cases, AutoCM was reported to provide fresh, as yet unavailable insights due to its superior capacity to systematically capture previously hidden information in the datasets.…”