XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm

Hui, Min; Xin, Xu; Gao, Chu-Qiao; Wang, Likun; Du, Pu-Feng

doi:10.3389/fgene.2022.877409

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…Since long non‐coding RNAs (lncRNAs) are involved in very diverse biological processes and diseases, their essentiality has been studied, first thanks to knockout by CRISPR/Cas9 and knockdown by RNAi and then thanks to new computational methods, databases, and scores. [ 89 ] Various methods for predicting the essentiality of microRNAs (miRNAs) with machine learning also exist, [ 90 ] showing their critical role in health and disease. Some studies show the link between the essentiality score obtained thanks to algorithms and miRNA conservation, [ 91 ] but we did not find any relevant reference on the essentiality of non‐coding RNA and their variability within species, nor on their implication in the possible variability of sequences 3′‐ or 5′‐UTR or even essential gene enhancers.…”

Section: In Regulatory Sequencesmentioning

confidence: 99%

Gene essentiality and variability: What is the link? A within‐ and between‐species perspective

Dubois-Mignon

Monget

2022

BioEssays

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A gene is considered essential when its loss of function results in a deleterious phenotype, hugely reducing the organism's viability or fitness. However, the link between the essentiality of a gene and its degree of polymorphism is unclear. In this review, we show that there is a place for a certain degree of variability, even for essential genes.We first study the role of infectious diseases in the prevalence of genetic disorders among humans: balancing selection has selected harmful variants due to the selective pressure of pathogens because the heterozygous carrier can resist them. Then we show that the environment can induce adaptation of species by rapidly evolving genes.We also study the role of positive selection on speciation, particularly upon genes of the immune, reproductive and nervous systems. Finally, we highlight the role of regulatory sequences in changes in morphology between species and adaptation to the environment within species.

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Section: In Regulatory Sequencesmentioning

confidence: 99%

Gene essentiality and variability: What is the link? A within‐ and between‐species perspective

Dubois-Mignon

Monget

2022

BioEssays

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“…PESM was also an essential miRNA prediction method and improved the prediction performance by adding new features and gradient boosting machines model ( 34 ). In addition, XGEM was also an essential miRNAs prediction method by applying the XGBoost framework with Classification and Regression Trees (CART) on various types of sequence-based features ( 35 ).…”

Section: Introductionmentioning

confidence: 99%

PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences

2022

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Increasing evidence has proved that miRNA plays a significant role in biological progress. In order to understand the etiology and mechanisms of various diseases, it is necessary to identify the essential miRNAs. However, it is time-consuming and expensive to identify essential miRNAs by using traditional biological experiments. It is critical to develop computational methods to predict potential essential miRNAs. In this study, we provided a new computational method (called PMMS) to identify essential miRNAs by using multi-head self-attention and sequences. First, PMMS computes the statistic and structure features and extracts the static feature by concatenating them. Second, PMMS extracts the deep learning original feature (BiLSTM-based feature) by using bi-directional long short-term memory (BiLSTM) and pre-miRNA sequences. In addition, we further obtained the multi-head self-attention feature (MS-based feature) based on BiLSTM-based feature and multi-head self-attention mechanism. By considering the importance of the subsequence of pre-miRNA to the static feature of miRNA, we obtained the deep learning final feature (WA-based feature) based on the weighted attention mechanism. Finally, we concatenated WA-based feature and static feature as an input to the multilayer perceptron) model to predict essential miRNAs. We conducted five-fold cross-validation to evaluate the prediction performance of PMMS. The areas under the ROC curves (AUC), the F1-score, and accuracy (ACC) are used as performance metrics. From the experimental results, PMMS obtained best prediction performances (AUC: 0.9556, F1-score: 0.9030, and ACC: 0.9097). It also outperformed other compared methods. The experimental results also illustrated that PMMS is an effective method to identify essential miRNA.

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Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm

Chen,

Mao,

2024

Comput Econ

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XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm

Cited by 7 publications

References 36 publications

Gene essentiality and variability: What is the link? A within‐ and between‐species perspective

Gene essentiality and variability: What is the link? A within‐ and between‐species perspective

PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences

Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm

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