CNN for human exons and introns classification

Nasr, Feriel Ben; Oueslati, Afef Elloumi

doi:10.1109/ssd52085.2021.9429303

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…These genes contain 5 different exon regions and 6 different intron regions. In the genes used, the shortest exon sequence consists of 15 Modelling of biological features and biomarkers associated with DNA sequences [18], classification of DNA sequences [15] and identification of exon and intron regions [8] are complex and challenging work. Therefore, in this study, a simple hierarchy independent of DNA length was constructed.…”

Section: Datasetmentioning

confidence: 99%

“…The increase in the number of data in recent years is important for analyzes that offer fast, accurate and low computational costs, and this situation can be tolerated using approaches in the framework of artificial intelligence [18]. For example, natural language representations are a deep learning area that is widely used in the pre-training process of data [24].…”

Section: Sentence Bert (Sbert) Modelmentioning

confidence: 99%

“…The maximum success rate for this model was found to be %96.1. The [18] study aimed the classification human exon and intron sequences. Therefore it expressed the text format of DNA sequences as images using percentage calculation.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS ve SBERT Yaklaşımlarının Hibrit Kullanımı ile DNA Dizilimleri Üzerinde Ekson ve İntron Bölgelerinin Sınıflandırılması

AKALIN

Yumuşak

2023

Journal of Polytechnic

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DNA is the part of the genome that contains enormous amounts of information related to life. Amino acids are formed by coding three nucleotides in this genome part, and the encoded amino acids are called codes in DNA. The frequency of the triple nucleotide in the DNA sequence allows for the evaluation of protein-coding (exon) and non-protein-coding (intron) regions. Distinguishing these regions enables the analysis of vital functions related to life. This study provides the classification of exon and intron regions for BCR-ABL and MEFV genes obtained from NCBI and Ensemble datasets, respectively. Then, existing DNA sequences are clustered using pretrained models in the scope of the SBERT approach. In the clustering process, K-Means and Agglomerative Clustering approaches are used consecutively. The frequency of repetition of codes is calculated with a representative sample selected from each cluster. The matrix is created using the frequencies of 64 different codons that constitute genetic code. This matrix is given as input to the ANFIS structure. The %88.88 accuracy rate is obtained with the ANFIS approach to classify exon and intron DNA sequences. As a result of this study, a successful result was produced independently of DNA length.

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Section: Datasetmentioning

confidence: 99%

Section: Sentence Bert (Sbert) Modelmentioning

confidence: 99%

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ANFIS ve SBERT Yaklaşımlarının Hibrit Kullanımı ile DNA Dizilimleri Üzerinde Ekson ve İntron Bölgelerinin Sınıflandırılması

AKALIN

Yumuşak

2023

Journal of Polytechnic

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“…In the [24] study, the estimation of exon regions from eukaryotic DNA sequences was provided with the developed bidirectional LSTM and RNN-based deep learning models. In the [25] study, is introduced a convolutional neural network model for the classification of human exon and intron regions. In the [26] study, Frequency Chaos Game Representation and CNN structure were used together.…”

Section: Introductionmentioning

confidence: 99%

Detection of Exon and Intron Regions in Dna Sequences by the Proposed Hashing Function

AKALIN

Yumuşak

2023

Journal of Scientific Reports-A

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Chromosomes, which are formed by the combination of DNA and special proteins, are structures that can show some changes with the effect of genetic or environmental factors. The DNA molecule in these structures carries vital information in elucidating critical information about life. DNA, which is formed by the combination of sugar, phosphate and organic bases, has exon and intron regions separation. Information about the processes in the life cycle of cells, the changes experienced by stem cells, the regulations in the growth and development stage, the development status of cancer, mutation occurrences and protein synthesis are stored in exon regions. Distinguishing exon regions that form %3 of a cell's DNA is considered a challenging problem. However, the detection of diseases on genetically based facts offers more precise outputs. In this study, the BCR-ABL gene, which is an important indicator in the diagnosis of ALL and CML malignancies, was obtained from the NCBI dataset. In the first step, sequences were digitized using the integer mapping technique to detect exon and intron regions in the BCR-ABL gene region. The digitized sequences were given as input to the hash function. The outputs obtained from the hash function, which has a mathematical basis suitable for the structure of DNA, were expressed in the binary system and placed in the hash table. Then, these key values, which define the exon and intron regions, were shown as clusters in order to find the region of the new input easily. As a result of the analysis performed for the BCR-ABL gene, one collision occurred. However, to test the power of the proposed hierarchy, the same processes were used again on the BRCA-1 mutation carrier gene seen in high-risk breast cancer patients. As a result of the study also carried out on the BRCA-1 gene, one collision occurred. This shows the success and stability of the proposed method. By this study, a simple hierarchy based on a mathematical basis, independent of nucleotide length, was created.

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A Study to Classify Virus Genome Through Analyzing DNA Sequences Using Transformer Model

Shiraj,

Yousuf

2024

2024 6th International Conference on Electrical Engineering and Information &Amp;amp; Communication Technology (ICEEICT)

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CNN for human exons and introns classification

Cited by 7 publications

References 14 publications

ANFIS ve SBERT Yaklaşımlarının Hibrit Kullanımı ile DNA Dizilimleri Üzerinde Ekson ve İntron Bölgelerinin Sınıflandırılması

ANFIS ve SBERT Yaklaşımlarının Hibrit Kullanımı ile DNA Dizilimleri Üzerinde Ekson ve İntron Bölgelerinin Sınıflandırılması

Detection of Exon and Intron Regions in Dna Sequences by the Proposed Hashing Function

A Study to Classify Virus Genome Through Analyzing DNA Sequences Using Transformer Model

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