Feature Importance Analysis of Non-coding DNA/RNA Sequences Based on Machine Learning Approaches

Almeida, Breno Lívio Silva de; Queiroz, Alvaro Pedroso; Avila-Santos, Anderson P.; Bonidia, Robson Parmezan; Rocha, Ulisses Nunes da; Sanches, Danilo Sipoli; Carvalho, André Carlos de Leon Ferreira de Ponce

doi:10.1007/978-3-030-91814-9_8

Cited by 2 publications

(2 citation statements)

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“…Ghandi et al [19] proposed Kgap sequence encoder that captures comprehensive distributional information of amino acids, by considering different gaps among bi-mers in the protein sequences. This encoder has been widely used in diverse types of DNA, RNA and protein sequence analysis tasks such as DNA regulatory sequence identification [20], RNA Pseudouridine sites detection [21], DNA Methylcytosine prediction [22, 24], and small non-coding RNA classification [23].…”

Section: Methodsmentioning

confidence: 99%

NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

Datta

Asim

Dengel

et al. 2023

Preprint

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Post-translational modifications (PTMs) either enhance a protein activity in various sub-cellular processes, or degrade their activity which leads towards failure of intracellular processes. Tyrosine nitration (NT) modification degrades proteins activity that initiate and propagate various diseases including Neurodegenerative, Cardiovascular, Autoimmune diseases, and Carcinogenesis. Identification of NT modification support development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming, and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches remain fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. The paper in hand presents NTpred framework competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, that is utilized to train a Logistic Regression classifier. On BD1 benchmark dataset, the proposed framework outperform existing best performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on BD2 benchmark dataset, the proposed framework outperform existing best performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC.

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Section: Methodsmentioning

confidence: 99%

NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

Datta

Asim

Dengel

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Many ML-based techniques have been proposed to identify ncRNAs in bacteria (EPPEN-HOF;PEÑA-CASTILLO, 2019;ALMEIDA et al, 2021;HE et al, 2018;XIAO, 2020;BARIK;DAS, 2018;BAR et al, 2021). In Barik and Das (2018), the authors compare the predictive performance of different techniques for RNAs classes, such as tRNAs, rRNAs, and mRNAs.…”

Section: Prediction Techniques Of Ncrnas In Bacteriamentioning

confidence: 99%

BioAutoML: Democratizing Machine Learning in Life Sciences

Bonidia

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Feature Importance Analysis of Non-coding DNA/RNA Sequences Based on Machine Learning Approaches

Cited by 2 publications

References 30 publications

NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

NTpred: A robust and precise machine learning framework for in-silico identification of Tyrosine nitration sites in protein sequences

BioAutoML: Democratizing Machine Learning in Life Sciences

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