CDLGP: A novel unsupervised classifier using deep learning for gene prediction

Sree, Pokkuluri Kiran; Rao, P. Srinivasa; Devi, N. S. S. S. N. Usha

doi:10.1109/icpcsi.2017.8392232

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…This technique presents valuable results in various medical fields such as thyroid diagnosis using images [ 37 ], heart disease detection [ 38 ], breast cancer diagnosis [ 39 , 40 ], molecular properties identification of drugs damaging the liver [ 41 ], orally disintegrating tablets (ODT) formulation prediction using an artificial neural network [ 42 ], and predicting water solubility of medication using other machine learning methods [ 43 ]. In addition, it has been exploited in modeling the sequence specificity of DNA–protein binding [ 44 ], genes prediction [ 45 ], motif identification, binding classification [ 46 ], protein binding [ 47 ], predicting genomic sequence, and the effects of non-coding variants [ 48 ]. Finding genes is the most crucial research problem in bioinformatics.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning for Acute Myeloid Leukemia Diagnosis

et al. 2020

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By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions. So, this study aims to use new techniques to diagnose acute myeloid leukemia. In this study, the leukemia microarray gene data, contenting 22283 genes, was extracted from the Gene Expression Omnibus repository. Initial preprocessing was applied by using a normalization test and principal component analysis in Python. Then DNNs neural network designed and implemented to the data and finally results cross-validated by classifiers. The normalization test was significant (P>0.05) and the results show the PCA gene segregation potential and independence of cancer and healthy cells. The results accuracy for single-layer neural network and DNNs deep learning network with three hidden layers are 63.33 and 96.67, respectively. Using new methods such as deep learning can improve diagnosis accuracy and performance compared to the old methods. It is recommended to use these methods in cancer diagnosis and effective gene selection in various types of cancer.

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Section: Discussionmentioning

confidence: 99%

“…Finding genes is the most crucial research problem in bioinformatics. Researchers have proposed different models for finding genes in the DNA sequence, but sometimes it does not work because of DNA sequence length variety and low accuracy [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Acute Myeloid Leukemia Diagnosis

et al. 2020

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“…This method helped to improve the prediction accuracy of short exons. Hung and Tang [14] reviewed the GPU-based deep learning-based algorithms on biological data. The convolution neural network and recurrent neural network have been adopted in gene expression analysis, enhancer and regulatory region prediction, and methylation prediction.…”

Section: Related Workmentioning

confidence: 99%

A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

Daş¹,

Toraman²,

Türkoğlu³

2020

Turk J Elec Eng & Comp Sci

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The identification of DNA sequences as exon and intron is a common problem in genome analysis. The methods used for feature extraction and mapping techniques for the digitization of sequences affect directly the solution of this problem. The existing mapping techniques are not enough to detect coding and noncoding regions in some genomes because the digital representation of each base in a DNA sequence with an integer does not fully reflect the structure of an original DNA sequence. In the entropy-based mapping technique, we could overcome this problem because the technique deepens distinction rates of exon regions, and better reflects the complexity of DNA sequences. Moreover, in the literature, features are extracted by using various statistical techniques. The statistical features to be extracted are chosen by a system designer's experience. The other proposed approach in this study is to carry out the feature extraction using the transfer learning method. Transfer learning and feature extraction are performed automatically by convolutional neural network models as independent of the data set. In this study, we propose a new method to classify DNA sequences as exon and intron using two approaches. In the first approach, the entropy-based numerical technique was used for the numerical representation of DNA sequences. In the second approach, transfer learning was used to extract features. Then, the obtained features were classified by support vector machine and k -nearest neighbors algorithm. As a result of the classification, accurate performance with 97.8% was achieved. The performance of the current method was compared with the other numerical mapping techniques and feature extraction methods. The results showed that the developed method was much more successful than other methods.

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