Machine Learning Techniques for Predicting Bacillus subtilis Promoters

Monteiro, Meika I.; Souto, Michael Vandesteen Silva; Gonçalves, Luiz Marcos Garcia; Agnez-Lima, Lucymara Fassarella

doi:10.1007/11532323_9

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…By the analysis of distinct biophysical functions in the internal interactions of dinucleotides and trinucleotides, it is possible to model nucleotides sequences as correlated structures sequences [15,19] or use these biophysical functions to model compositional features [18,20]. Since the start of the development of new computational methods for the promoter prediction research, several studies applied different classification methods, like Naive Bayes Approach [21,22], K-Nearest Neighbors (KNN) [15,19,22], Support Vector Machine (SVM) [15,19,23,22], Random Forest (RF) [19,20,24], feed-forward Artificial Neural Network (ANN) [13,25,21,22,26]. Although deep learning-based techniques achieved outstanding results, outperforming previous state-of-the-art machine learning methods for the promoter prediction problem [3].…”

Section: Introductionmentioning

confidence: 99%

CapsProm: A Capsule Network For Promoter Prediction

Moraes¹,

Luz²,

Moreira³

2021

Preprint

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Locating the promoter region in DNA sequences is of paramount importance in the field of bioinformatics. This is a problem widely studied in the literature, however, not yet fully resolved. Some researchers have presented remarkable results using convolution networks, that allowed the automatic extraction of features from a DNA chain. However, a universal architecture that could generalize to several organisms has not yet been achieved, and thus, requiring researchers to seek new architectures and hyperparameters for each new organism evaluated. In this work, we propose a versatile architecture, based on capsule network, that can accurately identify promoter sequences in raw DNA data from seven different organisms, eukaryotic, and prokaryotic. Our model, the CapsProm, could assist in the transfer of learning between organisms and expand its applicability. Furthermore the CapsProm showed competitive results, overcoming the baseline method in five out of seven of the tested datasets (F1score). The models and source code are made available at https://github.c om/lauromoraes/CapsNet-promoter.

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Section: Introductionmentioning

confidence: 99%

CapsProm: A Capsule Network For Promoter Prediction

Moraes¹,

Luz²,

Moreira³

2021

Preprint

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Structural Analysis of Promoter Sequences Using Grammar Inference and Support Vector Machine

Damaševičius

Lecture Notes in Computer Science

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Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria

et al. 2018

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This paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip.

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Machine Learning Techniques for Predicting Bacillus subtilis Promoters

Cited by 6 publications

References 16 publications

CapsProm: A Capsule Network For Promoter Prediction

CapsProm: A Capsule Network For Promoter Prediction

Structural Analysis of Promoter Sequences Using Grammar Inference and Support Vector Machine

Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria

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