A novel gene identification algorithm with Bayesian classification

Bataineh, Mohammad Al; Al-qudah, Zouhair

doi:10.1016/j.bspc.2016.07.002

Cited by 7 publications

(4 citation statements)

References 62 publications

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“…Subsequently, the classification algorithms are tested using the testing datasets to verify its performance in detection. Table 2 shows the obtained results of the four Bayesian classifiers when applied to the E. coli MG1655 bacterial strain being compared to the GLIMMER, GeneMark gene finding software, and to the period-3 gene detection algorithm proposed in [5]. The performance of the four Bayesian classifiers is assessed using the True Positive Rate (TPR, also referred to as sensitivity), the False Positive Rate (FPR, also known as fall-out), the False Negative Rate (FNR) and the True Negative Rate (TNR, also referred to as specificity).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

Bataineh¹,

Al-qudah²

2017

IJBBB

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Dramatic advances in genomics and computational biology have resulted in large amounts of data and have encouraged the development of computational algorithms for the identification and analysis of coding regions. This paper proposes a novel application of fundamental principles and concepts from communications theory for the identification of exact translation initiation sites in prokaryotic genomes. It employs several Bayesian classifiers to assess the performance of the ribosome binding sites detection algorithms investigated in this work. The proposed classification algorithms utilize well-known principles in communications theory such as cross correlation and Euclidean distance based metrics to make precise real-time decisions of weather a given open reading frame (ORF) is a valid protein coding region or not. The simulation results confirm that the proposed Bayesian classification algorithms can provide a efficient and accurate gene identification with sensitivity and specificity values comparable to the ones obtained by the well-known prokaryotic gene detection methods such as GLIMMER and GeneMark. This further confirms the significance of applying communications theory concepts to genomic sequence analysis.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

Bataineh¹,

Al-qudah²

2017

IJBBB

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“…En un estudio de clasificación de regiones codificantes de proteínas en genomas procariotas realizado por Al Bataineh (Al Bataineh & Al-qudah, 2017), proponen un algoritmo para la clasificación de genes utilizando un clasificador bayesiano, obteniendo resultados competitivos, en comparación con los métodos de detección de genes conocidos en procariotas como GLIMMER y GeneMark dando oportunidad de utilizar su algoritmo para la detección de enfermedades.…”

Section: Trabajos Relacionadosunclassified

Proceedings Ciencias Multidisciplinarias

Trejo-Macotela¹

2020

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Ninguna parte de este escrito amparado por la Ley Federal de Derechos de Autor, podrá ser reproducida, transmitida o utilizada en cualquier forma o medio, ya sea gráfico, electrónico o mecánico, incluyendo, pero sin limitarse a lo siguiente: Citas en artículos y comentarios bibliográficos, de compilación de datos periodísticos radiofónicos o electrónicos. Para los efectos de los artículos 13, 162,163 fracción I, 164 fracción I, 168, 169,209 fracción III y demás relativos de la Ley Federal de Derechos de Autor. Violaciones: Ser obligado al procesamiento bajo ley de copyright mexicana. El uso de nombres descriptivos generales, de nombres registrados, de marcas registradas, en esta publicación no implican, uniformemente en ausencia de una declaración específica, que tales nombres son exentos del protector relevante en leyes y regulaciones de México y por lo tanto libre para el uso general de la comunidad científica internacional. PCM es parte de los medios de ECORFAN-México, S.C, E: 94-443.F:008-(www.ecorfan.org).

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Section: Trabajos Relacionadosunclassified

Análisis de bancos de datos médicos y financieros mediante algoritmos de cómputo inteligente

Cruz¹

2020

Proceedings Ciencias Multidisciplinarias

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A novel gene identification algorithm with Bayesian classification

Cited by 7 publications

References 62 publications

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

Bayesian Classification of Ribosome Binding Sites in Prokaryotic Genome Sequences: A Communications Theory Approach

Proceedings Ciencias Multidisciplinarias

Análisis de bancos de datos médicos y financieros mediante algoritmos de cómputo inteligente

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