Journal ArticleDOI
BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
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TLDR
Experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods and will become a useful tool for biological sequence analysis.Abstract:
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user's convenience, its stand-alone program was also released, which can be downloaded from http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.read more
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Journal ArticleDOI
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.
Zhen Chen,Pei Zhao,Fuyi Li,Tatiana T. Marquez-Lago,André Leier,Jerico Revote,Yan Zhu,David R. Powell,Tatsuya Akutsu,Geoffrey I. Webb,Kuo-Chen Chou,A. Ian Smith,Roger J. Daly,Jian Li,Jiangning Song +14 more
TL;DR: iLearn is a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences.
Journal ArticleDOI
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.
Bin Liu,Xin Gao,Hanyu Zhang +2 more
TL;DR: The experimental results indicated that the predictors developed by the BioSeq-Analysis2.0 can achieve comparable or even better performance than the existing state-of-the-art predictors.
Journal ArticleDOI
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
Jiangning Song,Yanan Wang,Fuyi Li,Tatsuya Akutsu,Neil D. Rawlings,Geoffrey I. Webb,Kuo-Chen Chou +6 more
TL;DR: iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of prote enzyme-specific substrate cleavage and proteolytic events.
Journal ArticleDOI
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.
TL;DR: This study utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF), and support vector machine (SVM) using 51 feature sets derived from eight different feature encodings for the prediction of AHTPs to show superior performance and model robustness.
Journal ArticleDOI
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach.
TL;DR: A new predictor called ‘iEnhancer‐EL’ was proposed that contains two layer predictors and Rigorous cross‐validations have indicated that the proposed predictor is remarkably superior to the existing state‐of‐the‐art one in this area.
References
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