PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees

Deng, Lei; Yang, Wenyi; Liu, Hui

doi:10.3389/fgene.2019.00637

Cited by 32 publications

(23 citation statements)

References 55 publications

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“…GBDT can generate highly robust, interpretable and competitive classification procedures, especially for exploiting less than clean data [ 29 , 40 , 41 ]. For an lncRNA–protein pair , an estimator denotes an approximate function response to the label , the GBDT model iteratively builds K different individual decision tree using the training data .…”

Section: Methodsmentioning

confidence: 99%

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LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification

et al. 2021

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Background Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins. Results Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637. Conclusions Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

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Section: Methodsmentioning

confidence: 99%

“…Fan and Zhang [ 28 ] explored a stacked ensemble-based LPI classification model via logistical regression (LPI-BLS). Deng et al [ 29 ] proposed a gradient boosted regression tree for finding possible LPIs. Wekesa et al [ 30 ] designed a categorical boosting-based LPI discovery framework (LPI-CatBoost).…”

Section: Introductionmentioning

confidence: 99%

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification

et al. 2021

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“…Random forest is a generally acknowledged ensemble classifier for machine learning and can exploit large data repositories for the analysis of risk predictors and their intimate interactions and advancement risk prediction capability ( Yang et al, 2021 ). It was used in evaluating cervical cancer candidate genes as the main objects of our research ( Nunn et al, 2012 ; Wang et al, 2018 ; Deng et al, 2019b ; Ru et al, 2019 ; Lv et al, 2020 ; Yang et al, 2020 ). In the random forest setting, we divided the expression profile data of 103 genes according to the properties of the samples and considered the samples before radiotherapy as “good” and the samples during radiotherapy “poor,” respectively.…”

Section: Methodsmentioning

confidence: 99%

Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Feng

Wang

Yang

et al. 2021

Front. Cell Dev. Biol.

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Cervical cancer as a common gynecological malignancy threatens the health and lives of women. Resistance to radiotherapy is the primary cause of treatment failure and is mainly related to difference in the inherent vulnerability of tumors after radiotherapy. Here, we investigated signature genes associated with poor response to radiotherapy by analyzing an independent cervical cancer dataset from the Gene Expression Omnibus, including pre-irradiation and mid-irradiation information. A total of 316 differentially expressed genes were significantly identified. The correlations between these genes were investigated through the Pearson correlation analysis. Subsequently, random forest model was used in determining cancer-related genes, and all genes were ranked by random forest scoring. The top 30 candidate genes were selected for uncovering their biological functions. Functional enrichment analysis revealed that the biological functions chiefly enriched in tumor immune responses, such as cellular defense response, negative regulation of immune system process, T cell activation, neutrophil activation involved in immune response, regulation of antigen processing and presentation, and peptidyl-tyrosine autophosphorylation. Finally, the top 30 genes were screened and analyzed through literature verification. After validation, 10 genes (KLRK1, LCK, KIF20A, CD247, FASLG, CD163, ZAP70, CD8B, ZNF683, and F10) were to our objective. Overall, the present research confirmed that integrated bioinformatics methods can contribute to the understanding of the molecular mechanisms and potential therapeutic targets underlying radiotherapy resistance in cervical cancer.

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“…In this study, we incorporated ANOVA with incremental feature selection (IFS) [54]- [56] to pick out the optimal feature subset. The principle of ANOVA is to calculate the score (F value) of each feature in feature subset, which indicates the contribution of each feature to classification.…”

Section: Analysis Of Variancementioning

confidence: 99%

iPhoPred: A Predictor for Identifying Phosphorylation Sites in Human Protein

Zhang

Zhao

et al. 2019

IEEE Access

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Protein phosphorylation is an important type of post-translational modification that regulates various activities of cell life inside human body. The accurate identification of phosphorylation sites can provide new insights for revealing the specific function of protein. However, it is time-consuming and inefficient to apply the experiment-based techniques in investigating the phosphorylation sites in proteins. Additionally, computational approaches are regarded as an ideal choice in such a big data era. Therefore, in this work, we designed a new computational method to identify phosphorylation sites. At first, phosphorylation data was collected from human proteins to construct an objective and strict benchmark dataset. By a series of feature analysis, we found that the distributions of conservation scores and nine physicochemical properties surrounding the phosphorylation sites in positive samples are significantly different from those surrounding non-phosphorylation sites in negative samples. Based on these features, a novel sequence-based method for predicting the phosphorylation sites in human proteomics was proposed, which incorporated the conservation scores with position-associated attributes that reflect the correlation of physicochemical characteristics among amino acid residues. Furthermore, the analysis of variance (ANOVA) was utilized to obtain the optimal feature subset which could produce the highest accuracy. Comparison with the published predictor demonstrated the superiority of our predictor. Finally, a user-friendly online tool called iPhoPred was established and can be freely available at http://lin-group.cn/server/iPhoPred/. We hope the tool will provide important guide for the study of protein phosphorylation.

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PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees

Cited by 32 publications

References 55 publications

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification

Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

iPhoPred: A Predictor for Identifying Phosphorylation Sites in Human Protein

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