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An efficient gene bigdata analysis using machine learning algorithms

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Abstract

Bioinformatics is one of the emerging and rapidly developing research areas that is predominantly used for genetic data analysis and processing. Bioinformatics is characterized by its huge and voluminous data that is growing in nature which in turn complicates data analysis. In most cases, Bioinformatics data analysis and processing involve big data analytics due to the complex nature of the data. Previous research works handled data analytics using traditional tools and conventional big data analytical methods. However, it can be proved that machine learning algorithms and approaches can be effectively deployed to perform parallel, distributed and incremental processing of complex big data analytics especially in the case of gene big data analytics to enhance the efficiency in processing this large chunk of Bioinformatics-based gene big data. This paper provides a Machine Learning algorithm-based Convolution Neural Network (ML-CNN) approach for the process of identifying potential target genes, predicting miRNAs, visualizing the unique miRNA patterns, and validating genomes. The proposed approach has experimented with MATLAB software using deep learning toolbox on the pre - miRNA dataset. Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. The mean performance of ML-CNN is improved 7% high than the existing system.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (Grant: 91746104). The author would like to thank all the students and teachers for their efforts. We are also appreciating the reviewers and editors for their valuable suggestions and comments to improve this work.

Author information

Authors and Affiliations

  1. Department of Information Engineering, Shandong University of Science and Technology, Taian, China

    Ge Wang & Pengbo Pu

  2. Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, 100853, China

    Tingyan Shen

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  1. Ge Wang
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  2. Pengbo Pu
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Correspondence to Ge Wang.

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Appendix-1

Appendix-1

1.1 List of Abbreviations

A:

Adrenal Gland

Ap:

Adipose

B:

Brain

Bl:

Bladder

BM:

Bone Marrow

Br:

Breast

Ce:

Cervix

Co:

Colon

DCo:

Distal Colon

Du:

Duodenum

ES, EBD3, EBD28, E11, E15, E17:

Embryonic Stages

E:

Esophagus

F:

Fallopian Tube

FC:

Frontal Cortex

H:

Heart

HLS3:

Hela3s

I:

Intestine

Ile:

Ileum

Je:

Jejunum

K:

Kidney

LAt:

Left Atrium

Li:

Liver

Lu:

Lung

LVe:

Left Ventricle

Ly:

Lymph Node

O:

Ovary

Pa:

Pancreas

PBMC:

Peripheral Blood Mononuclear Cells

PCo:

Proximal Colon

Pe:

Pericardium

Pl:

Placenta

Pr:

Prostate

RAt:

Right Atrium

RVe:

Right Ventricle

SI:

Small Intestine

SM:

Skeletal Muscle

Sp:

Spleen

St:

Stomach

Te:

Testicle

Th:

Thymus

Tr:

Trachea

Ty:

Thyroid

U:

Uterus

VC:

Vena Cava

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Wang, G., Pu, P. & Shen, T. An efficient gene bigdata analysis using machine learning algorithms. Multimed Tools Appl 79, 9847–9870 (2020). https://doi.org/10.1007/s11042-019-08358-7

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  • DOI: https://doi.org/10.1007/s11042-019-08358-7

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