Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer's, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.

PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets

The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. The syntax is simple, and the tools show the exploration progress in real-time. In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment. In addition, any user can perform further selections based on the results printed in the console. The paper contains examples of using these tools for large datasets such as the one belonging to the World Values Survey and based on a simple variable naming practice. This article includes many recorded simulations and presents performance results. They depend on different resources and hardware configurations used, including cloud vs. on-premises, large vs. small amounts of RAM and processing cores, and in-memory vs. traditional storage.

Editorial for the Genetics of Alzheimer's Disease Special Issue: October 2021

Alzheimer's disease is a complex and multifactorial condition regulated by both genetics and lifestyle, which ultimately results in the accumulation of β-amyloid (Aβ) and tau proteins in the brain, loss of gray matter, and neuronal death [...].