Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method

Liao, Zhijun; Wang, Xinrui; Chen, Xingyong; Zou, Quan

doi:10.2174/1386207320666170314094951

Cited by 8 publications

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“…Moreover, Charos et al utilized ChIP-seq to obtain PPARGC1A binding sites across the genome in hepatoma cells HepG2 [ 10 ]. Conserved motif analysis [ 39 , 40 , 41 ] showed that the majority of PPARGC1A binding sites are located in multiple regulatory factor binding regions including RNAPII. Additionally, these regions are frequently located at the promoter of target genes, such as genes CEBPB [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

Wang

Liao

Bai

et al. 2018

Genes

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Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A, formerly known as PGC-1a) is a transcriptional coactivator and metabolic regulator. Previous studies are mainly focused on the association between PPARGC1A and hepatoma. However, the regulatory mechanism remains unknown. A microRNA associated with cancer (oncomiR), miR-93-5p, has recently been found to play an essential role in tumorigenesis and progression of various carcinomas, including liver cancer. Therefore, this paper aims to explore the regulatory mechanism underlying these two proteins in hepatoma cells. Firstly, an integrative analysis was performed with miRNA–mRNA modules on microarray and The Cancer Genome Atlas (TCGA) data and obtained the core regulatory network and miR-93-5p/PPARGC1A pair. Then, a series of experiments were conducted in hepatoma cells with the results including miR-93-5p upregulated and promoted cell proliferation. Thirdly, the inverse correlation between miR-93-5p and PPARGC1A expression was validated. Finally, we inferred that miR-93-5p plays an essential role in inhibiting PPARGC1A expression by directly targeting the 3′-untranslated region (UTR) of its mRNA. In conclusion, these results suggested that miR-93-5p overexpression contributes to hepatoma development by inhibiting PPARGC1A. It is anticipated to be a promising therapeutic strategy for patients with liver cancer in the future.

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Section: Discussionmentioning

confidence: 99%

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

Wang

Liao

Bai

et al. 2018

Genes

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“…For example, in Table 1 , the number of sites contributing to the construction of motif 1 only 47, which may result in a great deal of false positive results. Therefore, it would be practicable to turn to the machine learning-based methods and has been proved to be effective in many fields [ 26 , 31 – 46 ].…”

Section: Methodsmentioning

confidence: 99%

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

Jia

Duan

et al. 2018

BMC Syst Biol

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BackgroundPromoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It’s also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.ResultsIn order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.ConclusionThe jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0570-1) contains supplementary material, which is available to authorized users.

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Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method

Fang

Luan

et al. 2023

Computers in Biology and Medicine

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Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method

Abstract: Two classification models for KLFs prediction have been built by novel machine learning methods.

Cited by 8 publications

References 0 publications

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

MiR-93-5p Promotes Cell Proliferation through Down-Regulating PPARGC1A in Hepatocellular Carcinoma Cells by Bioinformatics Analysis and Experimental Verification

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method

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