CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

Niu, Mengting; Zhang, Jun; Li, Yanjuan; Wang, Cankun; Liu, Zhao‐Qian; Ding, Hui; Zou, Quan; Ma, Qin

doi:10.1016/j.csbj.2020.03.028

Cited by 44 publications

(29 citation statements)

References 59 publications

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“…The max-relevance-max-distance (MRMD) is a dimensionality reduction algorithm, which was developed by Zou et al [ 62 , 63 ] in 2015 that can be downloaded at https://github.com/heshida01/mrmd/tree/master/mrmdjar . It is based on a series of distance functions to judge the feature independence.…”

Section: Methodsmentioning

confidence: 99%

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Tao

Teng

et al. 2020

Computational and Mathematical Methods in Medicine

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With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

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Section: Methodsmentioning

confidence: 99%

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Tao

Teng

et al. 2020

Computational and Mathematical Methods in Medicine

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“…To discard irrelevant and redundant features from the original feature descriptor, feature optimization using sequential forward search (SFS) or other approaches is generally performed. 47 , 48 , 49 In the third step, the prediction model is constructed based on the exploration of different classifiers and different approaches. Specifically, the optimal features from each descriptor (from the second step) are input to several ML classifiers (SVM, extreme gradient boosting, deep learning, and random forest) to develop a prediction model.…”

Section: Methodsmentioning

confidence: 99%

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

Manavalan

Hasan

Basith

et al. 2020

Molecular Therapy - Nucleic Acids

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DNA N 4 -methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN ( Escherichia coli ), DNA4mC-LIP ( Arabidopsis thaliana ), iDNA-MS ( Fragaria vesca ), DNA4mC-LIP and 4mCCNN ( Drosophila melanogaster ), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus , Geobacter pickeringii , and Mus musculus , thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

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“…Testing parameters also play a significant role in prediction performance. Moreover, two articles (Wu et al, 2018;Niu et al, 2020) also point out that a swarm intelligence algorithm can optimize parameters and the ABC algorithm (Karaboga and Akay, 2009) is utilized to get the more suitable parameters α, β, and γ in this article. ABC, which is proposed by Karaboga, is inspired by bee colony behavior.…”

Section: Parameter Analysis Based On Abcmentioning

confidence: 99%

Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm

Lei

Zhang

Wang

2020

Front. Mol. Biosci.

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In recent years, latent metabolite-disease associations have been a significant focus in the biomedical domain. And more and more experimental evidence has been adduced that metabolites correlate with the diagnosis of complex human diseases. Several computational methods have been developed to detect potential metabolite-disease associations. In this article, we propose a novel method based on the spy strategy and an artificial bee colony (ABC) algorithm for metabolite-disease association prediction (SSABCMDA). Due to the fact that there are large parts of missing associations in unconfirmed metabolite-disease pairs, spy strategy is adopted to extract reliable negative samples from unconfirmed pairs. Considering the effects of parameters, the ABC algorithm is utilized to optimize parameters. In relevant cross-validation experiments, our method achieves excellent predictive performance. Moreover, three types of case studies are conducted on three common diseases to demonstrate the validity and utility of SSABCMDA method. Relevant experimental results indicate that our method can predict potential associations between metabolites and diseases effectively.

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CirRNAPL: A web server for the identification of circRNA based on extreme learning machine

Cited by 44 publications

References 59 publications

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools

Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm

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