Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Mahendran, N.; Vincent, Pascal; Srinivasan, Kathiravan; Chang, Chuan‐Yu

doi:10.3389/fgene.2021.784814

Cited by 26 publications

(11 citation statements)

References 64 publications

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“…Similarly, another method used for features selection in case of binary class problems was proposed in [20], where the ordinary bat procedure was extended by using more suitable and refined formulations, multi-objective operators to improve the performance, and an advanced local search mechanism. Other examples can be found in [21] and [22].…”

Section: Related Workmentioning

confidence: 99%

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

Hamraz¹,

Khan²,

Gul³

et al. 2023

Computers, Materials &Amp; Continua

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The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the "Median" as well as the measures of variation i.e., "Median absolute deviation (MAD) and Interquartile range (IQR)" in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR). The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e., classification error rate. A total of 5 gene expression datasets have been used in this study. Different subsets of informative genes are selected by the proposed and all the other methods included in the study, and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine (SVM), Random forest (RF) and k-nearest neighbors (k-NN). The results of the analysis reveal that the proposed method (RSNR) produces minimum error rates than all the other competing feature selection methods in majority of the cases. For further assessment of the method, a detailed simulation study is also conducted.

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Section: Related Workmentioning

confidence: 99%

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

Hamraz¹,

Khan²,

Gul³

et al. 2023

Computers, Materials &Amp; Continua

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“…Different technologies can be used to detect AD, such as molecular biomarkers combined with deep learning on gene expression datasets ( 11 ). However, we used transcripts combined with deep learning on speech datasets instead.…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing

Liu

Luo

Yuan

et al. 2022

Front. Public Health

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Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.

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“…However, machine learning algorithms suffer from the problem of high dimension low sample size (HDLSS), also known as, the “curse of dimensionality”, and this is also the case for gene expression datasets where the number of genes is usually in the tens of thousands, obtained from a few hundreds of samples. Similarly, this range of sampling is often found in datasets collected in AD studies [ 22 , 23 ]. Due to the need for a larger number of samples for machine learning, researchers usually combine several datasets to obtain a bigger dataset with more samples [ 21 ].…”

Section: Introductionmentioning

confidence: 95%

Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression

Kalkan

Akkaya

Inal-Gultekin

et al. 2022

Genes

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Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes’ expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection.

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Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Cited by 26 publications

References 64 publications

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing

Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression

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