Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model

Wang, Yan; Yang, Sen; Zhao, Jing; Du, Wei; Liang, Yanchun; Wang, Cankun; Zhou, Fengfeng; Tian, Yuan; Ma, Qin

doi:10.1038/s41598-019-40780-7

Cited by 25 publications

(28 citation statements)

References 85 publications

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“…Feature encoding has a vital role in the construction of the model [38]. A DNA sequence is represented as a fixed length of feature vectors which can be classified by deep learning algorithms.…”

Section: Feature Encoding Methodsmentioning

confidence: 99%

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

Wahab

Mahmoudi

Kim³

et al. 2020

Cells

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N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.

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Section: Feature Encoding Methodsmentioning

confidence: 99%

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

Wahab

Mahmoudi

Kim³

et al. 2020

Cells

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“…Feature extraction plays a crucial role in the construction of the model (Wang et al, 2019). Four feature extraction algorithms were adopted to formulate 6mA samples.…”

Section: Model Architecturementioning

confidence: 99%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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“…Non-coding RNA (ncRNA) cannot translate protein, but it is involved in many crucial and essentially biological processes, such as gene expression (Wang et al, 2019), gene regulation (Deaton and Bird, 2011;Dykes and Emanueli, 2017), gene silencing (Singh et al, 2018), etc. Furthermore, ncRNA plays a key role in the development of diverse cancers, including pancreatic cancer (Peng et al, 2016;Xiong et al, 2017), lung cancer (Anastasiadou et al, 2017), and so on.…”

Section: Introductionmentioning

confidence: 99%

NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences

et al. 2020

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Noncoding RNA (ncRNA) is a kind of RNA that plays an important role in many biological processes, diseases, and cancers, while cannot translate into proteins. With the development of next-generation sequence technology, thousands of novel RNAs with long open reading frames (ORFs, longest ORF length > 303 nt) and short ORFs (longest ORF length ≤ 303 nt) have been discovered in a short time. How to identify ncRNAs more precisely from novel unannotated RNAs is an important step for RNA functional analysis, RNA regulation, etc. However, most previous methods only utilize the information of sequence features. Meanwhile, most of them have focused on long-ORF RNA sequences, but not adapted to short-ORF RNA sequences. In this paper, we propose a new reliable method called NCResNet. NCResNet employs 57 hybrid features of four categories as inputs, including sequence, protein, RNA structure, and RNA physicochemical properties, and introduces feature enhancement and deep feature learning policies in a neural net model to adapt to this problem. The experiments on benchmark datasets of 8 species shows NCResNet has higher accuracy and higher Matthews correlation coefficient (MCC) compared with other state-of-the-art methods. Particularly, on four short-ORF RNA sequence datasets, specifically mouse, Saccharomyces cerevisiae, zebrafish, and cow, NCResNet achieves greater than 10 and 15% improvements over other state-of-the-art methods in terms of accuracy and MCC. Meanwhile, for long-ORF RNA sequence datasets, NCResNet also has better accuracy and MCC than other state-of-the-art methods on most test datasets. Codes and data are available at https://github.com/abcair/NCResNet.

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Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model

Cited by 25 publications

References 85 publications

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences

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