DNA numerical representation and neural network based human promoter prediction system

Arniker, Swarna Bai; Kwan, Hon Keung; Law, Ngai-Fong; Lun, Daniel Pak-Kong

doi:10.1109/indcon.2011.6139326

Cited by 7 publications

(16 citation statements)

References 12 publications

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“…Random Forest [19] Interval Position Vector Matrix -0.53 -SVM [55] Particle Swarm Optimization -0.62 0.62 SVM [5] GP 7-cross validation 0.86 0.90 FFNN [35] Mapping function 3-cross validation 0.90 0.90 PSVM [56] Markov model 5-cross validation 0.93 0.98 SVM [57] Sliding Window -0.69 -SVM [58] Markov Model ---SVM [59] Physicochemical properties 10-cross validation 0.96 0.98 * Under this column, the numbers appearing in the square brackets are reference numbers.…”

Section: Other Methodsmentioning

confidence: 99%

“…The output of a node is a result of applying an activation and summation function. [33] Different types of neural network are explored in [34][35][36][37][38] and frequently used are feed-forward and recurrent neural networks. [39,40] Although various types of neural network are available nowadays, design of neural network remains almost the same.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…[45] Recently a novel attempt has been made where DNA numerical sequence approach is practiced. [35] It preserves nucleotide positional information that further taken as input by NN (Neural Network). Results obtained are significantly better than previous methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

Singh

Kaur

Goel

2015

Nucleosides, Nucleotides and Nucleic Acids

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In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.

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Section: Other Methodsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

Singh

Kaur

Goel

2015

Nucleosides, Nucleotides and Nucleic Acids

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“…Note that C4 encoding Arniker et al (2011) is also a sequential model that preserve the global sequence order information, however, it is more computationally complex than C2 encoding, hence we here adopt C2 encoding to substitute for C4 encoding.…”

Section: Hybrid Encodingmentioning

confidence: 99%

“…For example, s i,r = AAAAACCC would have two codons f (AAA) = 1 and f (AAC) = 1. Note that C4 encoding Arniker et al (2011) is also a sequential model that preserves the global sequence order information, however, it is more computationally expensive than C2 encoding, hence we here adopt C2 encoding to substitute for C4 encoding. Meanwhile, the sliding window size is relevant to prediction performance, the use of a smaller size can increase the accuracy on the border of exon but leads to of higher rate of false positive prediction Hatzigeorgiou et al (1996).…”

Section: Hybrid Encodingmentioning

confidence: 99%

Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding^★,★★

Wei

Zhang

Yuan

2020

Preprint

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Protein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifier capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping kmer, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. In fact, kmer features that count the occurrence frequency of trinucleotides only reflect the local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. In viewing of the point, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploiting global sequence order information, non-overlapping kmer features and statistical dependencies among coding labels. Evaluated on genomic and transcript sequences, our proposed method significantly outperforms existing state-of-the-art methods.

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Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation

Zeng

et al. 2017

Bioinformatics Research and Applications

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DNA numerical representation and neural network based human promoter prediction system

Cited by 7 publications

References 12 publications

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding^★,★★

Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation

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DNA numerical representation and neural network based human promoter prediction system

Cited by 7 publications

References 12 publications

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction

Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding★,★★

Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation

Contact Info

Product

Resources

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Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding^★,★★