Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection

Abstract The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods perform well on distinguishing majority long noncoding RNAs (lncRNAs) and coding RNAs (mRNAs) but poorly on RNAs with small open reading frames (sORFs). Here, we present DeepCPP (deep neural network for coding potential prediction), a deep learning method for RNA coding potential prediction. Extensive evaluations on four previous datasets and six new datasets constructed in different species show that DeepCPP outperforms other state-of-the-art methods, especially on sORF type data, which overcomes the bottleneck of sORF mRNA identification by improving more than 4.31, 37.24 and 5.89% on its accuracy for newly discovered human, vertebrate and insect data, respectively. Additionally, we also revealed that discontinuous k-mer, and our newly proposed nucleotide bias and minimal distribution similarity feature selection method play crucial roles in this classification problem. Taken together, DeepCPP is an effective method for RNA coding potential prediction.

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Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BMC Genomics ◽

10.1186/s12864-020-07033-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Zhixun Zhao ◽

Xiaocai Zhang ◽

Fang Chen ◽

Liang Fang ◽

Jinyan Li

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Case Studies ◽

Learning Algorithm ◽

State Of The Art ◽

Learning Algorithms ◽

Feature Space ◽

Sequence Features ◽

Independent Test ◽

Benchmark Datasets

Abstract Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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Enhanced Prediction of plant virus-encoded RNA silencing suppressors by incorporating Reduced Set of Sequence Features using SMOTE followed by Fuzzy-Rough Feature Selection Technique

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt45670.2019.8944442 ◽

2019 ◽

Author(s):

Pankhuri Jain ◽

Anoop Kumar Tiwari ◽

Tanmoy Som

Keyword(s):

Feature Selection ◽

Rna Silencing ◽

Plant Virus ◽

Feature Selection Technique ◽

Selection Technique ◽

Sequence Features

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CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine

Nucleic Acids Research ◽

10.1093/nar/gkm391 ◽

2007 ◽

Vol 35 (suppl_2) ◽

pp. W345-W349 ◽

Cited By ~ 1262

Author(s):

Lei Kong ◽

Yong Zhang ◽

Zhi-Qiang Ye ◽

Xiao-Qiao Liu ◽

Shu-Qi Zhao ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Protein Coding ◽

Sequence Features ◽

Coding Potential

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Research on WebShell Detection Method Based on Regularized Neighborhood Component Analysis (RNCA)

Symmetry ◽

10.3390/sym13071202 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1202

Author(s):

Aijun Zhou ◽

Nurbol Luktarhan ◽

Zhuang Ai

Keyword(s):

Feature Selection ◽

Detection Method ◽

Recognition Rate ◽

Reference Value ◽

Component Analysis ◽

Main Research ◽

Sequence Features ◽

Single Feature ◽

Opcode Sequence ◽

Detection Effect

The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. The RNCA algorithm can effectively reduce the dimension of data while ensuring the accuracy of classification. In this paper, it is innovatively applied to a WebShell detection neighborhood, taking opcode behavior sequence features as the main research object, constructing vocabulary by using opcode sequence features with variable length, and effectively reducing the dimension of WebShell features from the perspective of feature selection. The opcode sequence selected by the algorithm is symmetrical with the source code file, which has great reference value for WebShell classification. On the issue of the single feature, this paper uses the fusion of behavior sequence features and text static features to construct a feature combination with stronger representation ability, which effectively improves the recognition rate of WebShell to a certain extent.

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