Artificial Neural Network aided Protein Structure Prediction

Deka, Arundhati; Sarma, Kandarpa Kumar

doi:10.5120/7450-0494

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The only analytical model that was able to contextualize the testicular ECS gene expression to the complexity of the reproductive function in terms of fertility outcome was a more sophisticated approach represented by ANN. It should be recalled that ANNs are computer-based algorithms inspired by the architecture and behaviour of neurons in human brain [ 73 , 74 ] that are already used in biology for a variety of complex issues such as the prediction of protein secondary and tertiary structures [ 75 , 76 ] and forensic age prediction using DNA-methylation patterns [ 77 ]. To date, ANNs represent a new frontier to interpret biological data, in order to develop novel diagnostic tools and targeted gene therapies [ 78 , 79 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles

Perruzza

Bernabò

Rapino

et al. 2018

BioMed Research International

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The relationship between varicocele and fertility has always been a matter of debate because of the absence of predictive clinical indicators or molecular markers able to define the severity of this disease. Even though accumulated evidence demonstrated that the endocannabinoid system (ECS) plays a central role in male reproductive biology, particularly in the testicular compartment, to date no data point to a role for ECS in the etiopathogenesis of varicocele. Therefore, the present research has been designed to investigate the relationship between testicular ECS gene expression and fertility, using a validated animal model of experimental varicocele (VAR), taking advantage of traditional statistical approaches and artificial neural network (ANN). Experimental induction of VAR led to a clear reduction of spermatogenesis in left testes ranging from a mild (Johnsen score 7: 21%) to a severe (Johnsen score 4: 58%) damage of the germinal epithelium. However, the mean number of new-borns recorded after two sequential matings was quite variable and independent of the Johnsen score. While the gene expression of biosynthetic and degrading enzymes of AEA (NAPE-PLD and FAAH, respectively) and of 2-AG (DAGLα and MAGL, respectively), as well as their binding cannabinoid receptors (CB1 and CB2), did not change between testes and among groups, a significant downregulation of vanilloid (TRPV1) expression was recorded in left testes of VAR rats and positively correlated with animal fertility. Interestingly, an ANN trained by inserting the left and right testicular ECS gene expression profiles (inputs) was able to predict varicocele impact on male fertility in terms of mean number of new-borns delivered (outputs), with a very high accuracy (average prediction error of 1%). The present study provides unprecedented information on testicular ECS gene expression patterns during varicocele, by developing a freely available predictive ANN model that may open new perspectives in the diagnosis of varicocele-associated infertility.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Network to Predict Varicocele Impact on Male Fertility through Testicular Endocannabinoid Gene Expression Profiles

Perruzza

Bernabò

Rapino

et al. 2018

BioMed Research International

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“…Furthermore, we have compared the proposed technique with four widely used techniques, SPIDER2, JPred4, FSVM, and SSpro5. As per the methodology used by the authors the SPIDER2, JPred4 use a deep learning neural network, JNet algorithm, and Deep Convolution Neural Fields (DeePCNF) to predict protein secondary structure [16,17]. One more criteria adopted i.e.…”

Section: Ch Ce and CC Indicate Correlation Coefficient Of α-Helix (H) β-Sheet (E) And Coil (C)mentioning

confidence: 99%

Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction

Rout¹,

Mishra²,

Sahoo³

2021

RIA

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The protein secondary structure prediction (PSP) of the large biological molecule protein is an important task of bioinformatics and in the last decades many machines learning and soft computing methodologies play vital roles in achieving satisfactory results. The protein structural class determination is an important topic in protein science because an idea about protein structural class is quite useful to know about the changes and reaction of a living body in order to design new drugs and medicines. Though several hard computing techniques may be helpful in these areas but focusing upon the steady development and big data size in protein sequences that are entering into databanks, it is a challenge to do experiments with the hard computing techniques. Soft computing techniques like Artificial Neural Network, Fuzzy logic, Genetic Algorithm play a vital role for these types of genomic researches. To face these complex challenges, this article presents a novel method to predict the protein structure by using Genetic Algorithm. The Q3 accuracy and SOV measure analysis with SOVH, SOVE, SOVC value of respective α-helix (H), β-sheet (E) and coil/loop(C) structures are also discussed. The application of Genetic algorithm i.e. the proposed technique GApred provides better result than that of SPIDER2, JPred4, FSVM and SSpro5 for all the three datasets in the experiment. This method is helpful for distinct protein secondary structure prediction and a significant success rate was observed, which indicates that it can be used as a powerful tool in drug design and medicine research.

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“…homology, fold recognition, templates, machine learning and others methods [5][6][7][8][9][10][11]. Despite of years of attempts, the predictions quality of these methods has not achieved satisfactory levels, reaching an accuracy up to 50%.…”

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confidence: 99%