Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods

Abstract

Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease, and many other disease types, cause cognitive dysfunctions such as dementia via the progressive loss of structure or function of the body’s neurons. However, the etiology of these diseases remains unknown, and diagnosing less common cognitive disorders such as vascular dementia (VaD) remains a challenge. In this work, we developed a machine-leaning-based technique to distinguish between normal control (NC), AD, VaD, dementia with Lewy bodies, and mild cognitive impairment at the microRNA (miRNA) expression level. First, unnecessary miRNA features in the miRNA expression profiles were removed using the Boruta feature selection method, and the retained feature sets were sorted using minimum redundancy maximum relevance and Monte Carlo feature selection to provide two ranking feature lists. The incremental feature selection method was used to construct a series of feature subsets from these feature lists, and the random forest and PART classifiers were trained on the sample data consisting of these feature subsets. On the basis of the model performance of these classifiers with different number of features, the best feature subsets and classifiers were identified, and the classification rules were retrieved from the optimal PART classifiers. Finally, the link between candidate miRNA features, including hsa-miR-3184-5p, has-miR-6088, and has-miR-4649, and neurodegenerative diseases was confirmed using recently published research, laying the groundwork for more research on miRNAs in neurodegenerative diseases for the diagnosis of cognitive impairment and the understanding of potential pathogenic mechanisms.

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features

“…Therefore, selecting optimal features is regarded as one of the most influential steps in ML-based prediction [ 53 , 54 , 55 , 56 ]. Recognizing the potential of feature selection, we applied the Boruta feature selection method, which has been widely applied effectively in several biological applications [ 57 , 58 , 59 ], and consequently identified optimal features. Among these, the major contribution was from AutoC (~27%), followed by CTD, DPC, QSO, CTriad, AAC, and SOCN.…”

Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods

“…The five ranking methods were LASSO [ 11 ], LightGBM [ 12 ], MCFS [ 13 ], mRMR [ 14 ], and RF [ 15 ]. These methods have been successfully applied in machine learning applications in the life sciences [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Salivary biomarkers: novel noninvasive tools to diagnose chronic inflammation

“…Currently, machine learning methods including Random Forests or Gradient Boosted Trees to deep learning have been increasingly applied to identify biomarkers from body fluids for non-invasive disease diagnosis. [101][102][103][104] Despite the scientific and technological advances, an early and non-invasive biomarker detection is still limited by current biosensors. In particular, the co-detection of different biomarkers that characterize a specific disease, by a single biosensor, is still unripe.…”