Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

Onesime, Mbulayi; Yang, Zhenyu; Dai, Qi

doi:10.1155/2021/9969751

Cited by 34 publications

(17 citation statements)

References 48 publications

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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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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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“…RF integrates the predictions of all decision trees with majority voting. RF is deemed as a powerful classification algorithm and has wide applications in tackling many biological problems ( Kandaswamy et al, 2011 ; Casanova et al, 2014 ; Marques et al, 2016 ; Jia et al, 2020 ; Liang et al, 2020 ; Zhang et al, 2021b ; Chen et al, 2021 ; Onesime et al, 2021 ; Chen et al, 2022 ; Ding et al, 2022 ; Yang and Chen, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods

Pan

Cai

2022

Front. Bioeng. Biotechnol.

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Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.

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“…In order to further study the arrangement of different structural elements, some important structural fragments or patterns have been proposed one after another [ 51 ]. The length of the longest segment (MaxSeg SE ) is defined as the following formula:

where MaxLen represents the function of the maximal segment length, and SEG SE is the composed of each segment of the structure element SE [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…In order to further study the arrangement of different structural elements, some important structural fragments or patterns have been proposed one after another [51]. The length of the longest segment ðMaxSeg SE Þ is defined as the following formula:…”

Section: Evolutionary Profilementioning

confidence: 99%

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Zhang

Wang

2022

Computational and Mathematical Methods in Medicine

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Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

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Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

Cited by 34 publications

References 48 publications

Identifying Functions of Proteins in Mice With Functional Embedding Features

Identifying Functions of Proteins in Mice With Functional Embedding Features

Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

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