Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method

Ding, Shi‐Jian; Wang, Deling; Zhou, Xianchao; Lei, Chen; Kong, Feng; Xu, Xianling; Huang, Tao; Li, Zhandong; Cai, Yu-Dong

doi:10.3390/life12020228

Cited by 40 publications

(27 citation statements)

References 72 publications

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“…Random forest is widely applied in analyzing biological and biomedical data. Several previous studies indicate the satisfactory performance of RF ( Pan et al, 2010 ; Zhao et al, 2018 ; Jia et al, 2020 ; Chen et al, 2021 , 2022 ; Ding et al, 2022 ; Li Z. et al, 2022 ; Wu and Chen, 2022 ; Zhou et al, 2022 ). RF is a meta-classifier because it consists of numerous decision trees.…”

Section: Methodsmentioning

confidence: 93%

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Guo

Zeng

et al. 2022

Front. Neurosci.

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Alzheimer’s disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.

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Section: Methodsmentioning

confidence: 93%

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Guo

Zeng

et al. 2022

Front. Neurosci.

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“…Here, two classic base classifiers, namely, SVM ( Cortes and Vapnik, 1995 ) and RF ( Breiman, 2001 ), were used, which were widely applied in tackling many biological problems ( Kandaswamy et al, 2011 ; Nguyen et al, 2015 ; Chen et al, 2017 ; Zhou JP. et al, 2020 ; Zhou J.-P. et al, 2020 ; Liang et al, 2020 ; Liu et al, 2021 ; Onesime et al, 2021 ; Wang et al, 2021 ; Zhu et al, 2021 ; Chen et al, 2022 ; Ding et al, 2022 ; Li et al, 2022 ; Wu and Chen, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Identifying Functions of Proteins in Mice With Functional Embedding Features

Zhang

Chen

et al. 2022

Front. Genet.

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In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods.

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“…Lastly, the final result is determined by aggregating the voting results of many tree classifiers. As RF is powerful, it is always an important candidate for constructing efficient classifiers ( Chen et al, 2017 ; Zhao et al, 2018 ; Chen et al, 2021 ; Li X. et al, 2022 ; Li Z. et al, 2022 ; Chen et al, 2022 ; Ding et al, 2022 ). In this study, the RF program in Weka ( Frank et al, 2004 ) was employed with default parameters.…”

Section: Methodsmentioning

confidence: 99%

Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods

Guo

Ding

et al. 2022

Front. Genet.

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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.

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Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method

Cited by 40 publications

References 72 publications

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Identifying Functions of Proteins in Mice With Functional Embedding Features

Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods

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