Protein Structure Prediction Using Multiple Artificial Neural Network Classifier

Bordoloi, Hemashree; Sarma, Kandarpa Kumar

doi:10.1007/978-3-642-25507-6_12

Cited by 15 publications

(11 citation statements)

References 3 publications

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“…ANN and RSM models were then compared for their predictive capacity. Several researchers had reported the combined ANN and RSM model development in various bioprocessing optimization studies [15,16,[23][24][25]. While the use of ANN or RSM optimization method for lactic acid bacteria (LAB) cultivation and protein biosynthesis has been reported [19,25,26]; this is the first such report on lysine-methionine biosynthesis by Pediococcus pentosaceus using these two approaches.…”

Section: Introductionmentioning

confidence: 99%

Comparative analyses on medium optimization usingone-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis byPediococcus pentosaceusRF-1

Nor

Mohamed

Loh

et al. 2017

Biotechnology & Biotechnological Equipment

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Optimization strategy that encompassed one-factor-at-a-time (OFAT), response surface methodology (RSM), and artificial neural network method was implemented during medium formulation with specific aim for lysine-methionine biosynthesis employing a newly isolated strain of Pediococcus pentosaceus RF-1. OFAT technique was used in the preliminary screening of factors (molasses, nitrogen sources, fish meal, glutamic acid and initial medium pH) before proceeded to optimization study. Implementation of central composite design of experiment subsequently generated 30 experimental runs based on four factors (molasses, fish meal, glutamic acid, and initial medium pH). From RSM analysis, a quadratic polynomial model can be devoted to describing the relationship between various medium components and responses. It also suggested that using molasses (9.86 g/L), fish meal (10.06 g/L), glutamic acid (0.91 g/L), and initial medium pH (5.30) would enhance the biosynthesis of lysine (15.77 g/L) and methionine (4.21 g/L). Alternatively, a three-layer neural network topography at 4-5-2 predicted a further improvement in the biosynthesis of lysine (16.52 g/L) and methionine (4.53 g/L) by using formulation composed of molasses (10.02 g/L), fish meal (18.00 g/L), and glutamic acid (1.17 g/L) with initial medium pH (4.26), respectively.

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Section: Introductionmentioning

confidence: 99%

Comparative analyses on medium optimization usingone-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis byPediococcus pentosaceusRF-1

Nor

Mohamed

Loh

et al. 2017

Biotechnology & Biotechnological Equipment

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“…ANN is more successful [159] Support vector machines (SVM) A supervised learning model; associated with learning algorithms and classification and regression analysis in its construction of a hyperplane; can handle high-dimensional data; flexibility in modelling diverse types of data; high accuracy.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

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Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given.

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“…Basically proteins have three structures---primary, secondary and tertiary. The sequences of amino acids are called primary structures [1]. Secondary structure is the spatial arrangement and regularities of amino acids with respect to each other.…”

Section: Introductionmentioning

confidence: 99%

“…The three dimensional structure is responsible for the functional characteristics of proteins and it is termed as tertiary structure . A typical protein contains about 32% alpha helices, 21% beta sheets and 47% loops or non regular structures [1]. Theoretically, it is not possible to predict 100% accurate protein structure because of the fact that there are 20 different amino acids and thus no.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network aided Protein Structure Prediction

Deka¹,

Sarma²

2012

IJCA

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Protein structure prediction plays a vital role in drug design and biotechnology. Understanding protein structures is necessary to determine the function of a protein and its interaction with DNA, RNA and Enzymes. Experimental techniques such as NMR Spectroscopy and X-ray Crystallography have been the main source of information about protein structures. But these conventional methods are now replaced by Machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM)s. In this paper, ANNs are used as a two level classifier to estimate the tertiary structure of proteins. ANNs are trained to make them capable of recognizing the primary sequences and DSSP codes of protein structures and their association with the secondary structure is derived. Based on majority selection, the final secondary structure is evaluated. These secondary structures can be further used as inputs to classify between the basic tertiary folds and subclasses of tertiary folds.

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Protein Structure Prediction Using Multiple Artificial Neural Network Classifier

Cited by 15 publications

References 3 publications

Comparative analyses on medium optimization usingone-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis byPediococcus pentosaceusRF-1

Comparative analyses on medium optimization usingone-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis byPediococcus pentosaceusRF-1

Intelligent mining of large-scale bio-data: Bioinformatics applications

Artificial Neural Network aided Protein Structure Prediction

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