An efficient and flexible scanning of databases of protein secondary structures

Mrozek, Dariusz; Socha, Bartek; Kozielski, Stanisław; Małysiak-Mrozek, Bożena

doi:10.1007/s10844-014-0353-0

Cited by 27 publications

(8 citation statements)

References 32 publications

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“…Feature representation, fusion, 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 and selection 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 are the key steps in the machine learning process. In this paper, we propose and employ three feature representation algorithms, including PS(k-mer)NP, PCPs, 10 and RFHC-GACs.…”

Section: Methodsmentioning

confidence: 99%

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Wei

Chen

2018

Molecular Therapy - Nucleic Acids

166

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N6-methyladenosine (m6A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m6A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m6A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m6A sites. To predict m6A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m6A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m6A functional mechanisms.

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Section: Methodsmentioning

confidence: 99%

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Wei

Chen

2018

Molecular Therapy - Nucleic Acids

166

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“…By combining multiple weak classifiers, the final results can be voted or averaged to obtain an overall model with higher accuracy, better general performance, and resistance to overfitting. This algorithm has been extensively used in bioinformatics and other areas, and has been confirmed to be an effective modeling technique in various domains (Ding et al, 2016a,b;Mrozek et al, 2016;Qiu et al, 2016;Wang et al, 2017;Wei et al, 2017a,b,c;Yu et al, 2017a;Zheng et al, 2017;Tang et al, 2018Tang et al, , 2019aXue et al, 2018;Degenhardt et al, 2019;Xu et al, 2019). In this study, the scikit-learn toolkit, available at https://scikit-learn.org, was used to establish the models.…”

Section: Algorithmmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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“…In [33] and [34], we reported the PSS-SQL (Protein Secondary Structure -Structured Query Language) for searching protein similarities on the basis of their secondary structures in the Microsoft SQL Server relational database. The newest version of the PSS-SQL search engine [35] utilizes a multi-threaded alignment procedure, which allows effective querying on DBMSs hosted on computers with multi-core CPUs. However, capabilities of the PSS-SQL are limited to processing only the secondary structures of proteins.…”

Section: B Related Workmentioning

confidence: 99%