A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

Sun, Shuang; Zhang, Xiongpan; Peng, Qinke

doi:10.1016/j.artmed.2016.11.004

Cited by 4 publications

(2 citation statements)

References 55 publications

(62 reference statements)

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“…Nevertheless, such performances can be altered by modifying the network hyperparameters (such as the number of layers or neuron units), often on the cost of overfitting the data. Other works have applied distance-based methods such as KNN (Ahmad et al, 2017), kernel-driven spatial transforms as SVM (Shi et al, 2013;Xiang et al, 2017), and variations of Partial Least Squares PLS (Sun et al, 2017), all after performing a specially tailored data pretreatment. This non-standard pretreatment results in the loss of generality of such approaches.…”

Section: Introductionmentioning

confidence: 99%

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

Medina-Ortiz

Contreras

Quiroz

et al. 2020

Front. Mol. Biosci.

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Section: Introductionmentioning

confidence: 99%

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

Medina-Ortiz

Contreras

Quiroz

et al. 2020

Front. Mol. Biosci.

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“…More recent studies ([ 24 ], Khan SA, Ammad-ud-din M. tensorBF: an R package for Bayesian tensor factorization. 2016; bioRxiv 097048, unpublished) leveraged tensor representation to integrate different omics, environmental, and phenotypic data sets to uncover unclear biological problems; Also, our previous work [ 25 ] used tensor representation to identify transcription factor binding sites. All results from these applications are demonstrated that tensor representation enable to achieve a powerful performance.…”

Section: Introductionmentioning

confidence: 99%

Higher-order partial least squares for predicting gene expression levels from chromatin states

Sun

Zheng

2018

BMC Bioinformatics

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BackgroundExtensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels.ResultsThe prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively.ConclusionsThe overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species.

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PLS-based gene subset augmentation and tumor-specific gene identification

You,

Yang,

2024

Computers in Biology and Medicine

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A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli

Cited by 4 publications

References 55 publications

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

Higher-order partial least squares for predicting gene expression levels from chromatin states

PLS-based gene subset augmentation and tumor-specific gene identification

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