mirMark: a site-level and UTR-level classifier for miRNA target prediction

Menor, Mark; Ching, Travers; Zhu, Xun; Garmire, David; Garmire, Lana X.

doi:10.1186/s13059-014-0500-5

Cited by 45 publications

(21 citation statements)

References 41 publications

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“…There are currently over 1000 genes encoding microRNAs in the human genome, and the microRNAs play a role in regulating the expression of about 60% of the human protein coding gene. 39 Not only the expression of miR-200c regulated by many factors but also the expression of VEGF-A mRNA. The miR200b expression in cancer cells is also known as the VEGF-A regulator by targeting VEGF-A directly and its receptor Flt1 and KDR.…”

Section: Discussionmentioning

confidence: 99%

Expression of circulating miR-200c and vascular endothelial growth factor-A (VEGF-A) mRNA as potential biomarker in human hepatocellular carcinoma

et al. 2018

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Hepatocellular carcinoma (HCC) is a common liver disease that causes significant public health problems throughout the world, including in Indonesia. The HCC is the six most common cancers and second cancer-related deaths among men in the world. Recently it was reported that the microRNA is an important player in hepatocarcinogenesis. The expression of MiRNA-200c is often regulated in primary HCC and HCC cell lines. Vascular endothelial growth factor-A (VEGF-A) is a regulator of angiogenesis that has been reported as miR-200c target gene. This study was conducted to measure expression levels in miR-200c and mRNAVEGF-A and their potential role as biomarkers at HCC. A total of 36 HCC patients and 36 healthy subjects were included in this study. The relative expression of miRNA-200c and mRNA VEGF-A was quantified using reverse transcription real time quantitative PCR (qRT PCR). Relative expression was calculated using. Unpaired t-test was used to compare the expression levels of circulating miRNA-200c and mRNA VEGF-A in HCC patients and healthy subjects. Pearson test was used to determine correlation between circulating miR-200c expression and mRNA VEGF-A expression levels. The expression levels of circulating miR-200c in HCC patients were lower compared to healthy subjects although it was not significant (p = 0.258). Conversely, the expression levels of circulating mRNA VEGF-A in HCC patients were significantly higher compared to healthy subjects (p = 0.001). The relative expression levels of circulating miR-200c were negatively correlated with mRNA VEGF-A in HCC patients. In conclusion, the expression levels of mRNA VEGF-A in HCC patients are significantly deregulated in compared to that in healthy subjects. Negative correlation between circulating miRN-200c and mRNA VEGF-A expression levels are reported in HCC patients. ABSTRAK Hepatocellular carcinoma (HCC) adalah penyakit hati yang umum dan menyebabkan masalah kesehatan masyarakat yang signifikan di seluruh dunia, termasuk di Indonesia. Karsinoma hepatoseluler adalah penyebab kematian keenam akibat kanker umumnya dan kedua pada laki-laki di seluruh dunia. Saat ini microRNA dilaporkan berperan penting dalam hepatokarsinogenesis. Ekspresi MiRNA-200c mengalami deregulasi pada jaringan HCC primer dan sel HVCC. Vascular endothelial growth factor-A (VEGF-A) adalah regulator angiogenesis yang dilaporkan sebagai gen target miR-200c. Penelitian ini dilakukan untuk mengkaji ekspresi miR-200c dan mRNA VEGF-A serta peran

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Section: Discussionmentioning

confidence: 99%

Expression of circulating miR-200c and vascular endothelial growth factor-A (VEGF-A) mRNA as potential biomarker in human hepatocellular carcinoma

et al. 2018

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“…An n-fold (default n=10; flexible depending on different sample sizes) cross-validation is applied on the training dataset to avoid overfitting. Metrics to measure prediction accuracy, including Area Under the Curve (AUC), F1-statistic, balanced accuracy, sensitivity and specificity are reported to the user as barplots, similar to others [19].…”

Section: Classification and Predictionmentioning

confidence: 99%

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Alakwaa

Huang

Garmire

2018

Preprint

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Lilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi

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“…This problem also relates to the challenges associated with accurately modeling miRNA data and mRNA data on a system level. To this end, several approaches predict miRNA targets based on binding sites, where the most commonly used features for predicting miRNA targets include sequence complementarity between the "seed" region of a miRNA and the "seed match" region of a putative target mRNA, species conservation, thermodynamic stability and site accessibility [9]. These methods can be classified in two categories.…”

Section: Introductionmentioning

confidence: 99%

“…The other category comprises machine-learning techniques (e.g. decision trees, support vector machine and artificial neural networks) such as mirMark [9], TarPmiR [13], TargetMiner [14], TargetSpy [15] and MiRANN [16]. More sophisticated algorithms in this category of methods include deep learning methods such as for example Deep-MirTar [17].…”

Section: Introductionmentioning

confidence: 99%

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington’s disease mice

et al. 2020

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Background: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington's disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice. Results: Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels. Several of these pairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs were not detected in cortex. Conclusions: These data suggest that miRNA regulation has a limited global role in HD while providing accuratelyselected miRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data also provide a methodological framework for researchers to explore how shape analysis can enhance multidimensional data analytics in biology and disease.

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mirMark: a site-level and UTR-level classifier for miRNA target prediction

Cited by 45 publications

References 41 publications

Expression of circulating miR-200c and vascular endothelial growth factor-A (VEGF-A) mRNA as potential biomarker in human hepatocellular carcinoma

Expression of circulating miR-200c and vascular endothelial growth factor-A (VEGF-A) mRNA as potential biomarker in human hepatocellular carcinoma

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington’s disease mice

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