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PCPI: Prediction of circRNA and Protein Interaction Using Machine Learning Method

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Bioinformatics Research and Applications (ISBRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14248))

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Abstract

Circular RNA (circRNA) is an RNA molecule different from linear RNA with covalently closed loop structure. CircRNAs can act as sponging miRNAs and can interact with RNA binding protein. Previous studies have revealed that circRNAs play important role in the development of different diseases. The biological functions of circRNAs can be investigated with the help of circRNA-protein interaction. Due to scarce circRNA data, long circRNA sequences and the sparsely distributed binding sites on circRNAs, much fewer endeavors are found in studying the circRNA-protein interaction compared to interaction between linear RNA and protein. With the increase in experimental data on circRNA, machine learning methods are widely used in recent times for predicting the circRNA-protein interaction. The existing methods either use RNA sequence or protein sequence for predicting the binding sites. In this paper, we present a new method PCPI (Predicting CircRNA and Protein Interaction) to predict the interaction between circRNA and protein using support vector machine (SVM) classifier. We have used both the RNA and protein sequences to predict their interaction. The circRNA sequences were converted in pseudo peptide sequences based on codon translation. The pseudo peptide and the protein sequences were classified based on dipole moments and the volume of the side chains. The 3-mers of the classified sequences were used as features for training the model. Several machine learning model were used for classification. Comparing the performances, we selected SVM classifier for predicting circRNA-protein interaction. Our method achieved 93% prediction accuracy.

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Funding

This work was partly supported by the Key Research and Development Project of Guangdong Province under grant No. 2021B0101310002, National Key Research and Development Program of China Grant No. 2021YFF1200100, Strategic Priority CAS Project XDB38050100, National Science Foundation of China under grant No. 62272449, the Shenzhen Basic Research Fund under grant, No. RCYX20200714114734194, KQTD20200820113106007 and ZDSYS20220422103800001. We would also like to thank the funding support by the Youth Innovation Promotion Association (Y2021101), CAS to Yanjie Wei.

Author information

Authors and Affiliations

  1. Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, 8100, Bangladesh

    Md. Tofazzal Hossain

  2. Bioinformatics Lab, Department of Statistics, Rajshahi University, Rajshahi, Bangladesh

    Md. Selim Reza

  3. Department of Pathology, The Shenzhen University School of Medicine, Shenzhen, 518060, Guangdong, People’s Republic of China

    Yin Peng

  4. Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Xuelei Li, Shengzhong Feng & Yanjie Wei

Authors
  1. Md. Tofazzal Hossain
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  2. Md. Selim Reza
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  3. Xuelei Li
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  4. Yin Peng
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  5. Shengzhong Feng
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  6. Yanjie Wei
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Corresponding author

Correspondence to Yanjie Wei .

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Editors and Affiliations

  1. University of North Texas, Denton, TX, USA

    Xuan Guo

  2. University of Southern California, Los Angeles, CA, USA

    Serghei Mangul

  3. Georgia State University, Atlanta, GA, USA

    Murray Patterson

  4. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Hossain, M., Reza, M., Li, X., Peng, Y., Feng, S., Wei, Y. (2023). PCPI: Prediction of circRNA and Protein Interaction Using Machine Learning Method. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_8

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  • DOI: https://doi.org/10.1007/978-981-99-7074-2_8

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  • Online ISBN: 978-981-99-7074-2

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