application of Artificial neural networking in genetic diversity

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Welcome…..

Transcript of application of Artificial neural networking in genetic diversity

Page 1: application of Artificial neural networking in genetic diversity

Welcome…..

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ARTIFICIAL NEURAL NETWORK APPLICATION IN DIVERSITY ANALYSIS

Gajendra C.V2016846101

Forest College and Research Institute, Mettupalayam

FOR - 809 BIOMETRIC ANALYSIS (1+1)

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Introduction - ANN

• The bioinformatics refers to the application of computational and mathematical

techniques in biological analysis

• To evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach

(multivariate) called artificial neural network (ANN)

• Information that flows through the network affects the structure of the ANN

because a neural network changes or learns, in a sense based on that input and

output

• ANNs have three layers that are interconnected. The first layer consists of input

neurons. Those neurons send data on to the second layer, which in turn sends the

output neurons to the third layer

• Used in various fields – horticulture, agriculture, forestry, medicine , defence etc.,

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Why ANN

• ANN’s can capture more complex features of the data, which is not

always possible with traditional statistical techniques

• The greatest advantage of ANN’s over the conventional methods is that

they do not require detailed information about the physical processes

of the system to be modelled

• Used to characterize the genetic structure plants as criteria and

indicators for the selection of promising genotypes for breeding

programs, aside from the conservation of germplasm

• Used in Perennial species and annuals – wide range of applicability

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A distinguishing the biological neuron versus artificial neuron

Comparative schemes of biological and artificial neural system. X= input variable; W= weight of in input; θ= internal threshold value; f=transfer function

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Working of ANN

In the development of neural network, two databases were tested to feed the input layer of the network; original means and standardized means, by the technique of

principal components

Best or OptimumHigh or low

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Types of artificial neural networksThe are many artificial neural networks……

1. Feed-forward and neural network

2. Radial basis function (RBF) network

3. Kohonen self organising network

4. learning vector quantization

5. Recurrent neural network

6. Modular neural networks

7. Physical neural network

8. Other types of networks

(holographic associative memory)

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Using neural networks in practice• Classification

– In marketing : consumer spending classification– In defence: radar and sonar image classification– In medicine: ultrasound and ECG image classification, medical diagnosis– In agriculture : soil map classification (nutrient)

• Recognition and identification– in general Computing and telecommunications: speech, vision and handwriting

recognition– In finance: signature verification and bank note verification

• Assessment– In engineering : Product inspection monitoring and control– In agriculture/ forestry : diversity analysis, identification

• Forecasting and prediction– In finance: foreign exchange rate and stock market forecasting– In agril. : crop yield forecasting– In meteorology : weather prediction

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Artificial neural network analysis of genetic diversity in Carica papaya L. (Cibelle et al., 2011)

• The study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics – fruit agronomic traits.

• n= 37, m=8, k=4

Fruit weight, fruit length, fruit diameter, flesh thickness, firmness, external and internal fruit, soluble solids and incidence of skin freckles

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Kohonen Neural Networks - KNN Artificial Neural Networks ANN

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1. To find out the best matching neuron in terms of similarity –

criterion of minimum distance between the accessions.

2. The synaptic weight vector is the criterion for acceptance or

rejection of a group of accessions or plants

3. The similarity between the input and the neuron was measured

as the average Euclidean distance between vectors

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Output ANN

The groups generated by the ANN facilitate the selection of divergent genotypes for improvement by the generation of hybrids, since they allow the selection of genotypes indicated for crosses from different heterotic groups. Thus, the probability of obtaining

superior genotypes is greater

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Energy input output analysis and application of artificial neural networks for predicting greenhouse basil production

(Pahlavan et al., 2012)

The ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions

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Artificial neural networks as a tool for plant identification: A case study on Vietnamese tea accessions

(Camilla Pandolfi et al., 2009)

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Output graphs obtained by the BPNN. Each frame is dedicated to a specific accession and shows the BPNN output for the input represented by the phyllometric parameters of 40 leaves. Reported lines show the averaged

output data

Back-Propagation Neural Networks

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The dendrogram obtained from the UPGMA clusteranalysis of the 17 tea accessions.

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Artificial Intelligence: A novel approach to model, understand and optimize cereals genetic transformation

• To understand the cereal genetic transformations the ANNs, genetic algorithms and neuro-fuzzy logic, have been employed in plant science

Objective : To find the combination of inputs that will provide the “optimum/best/highest”

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Application of ANN in forestry

Artificial Neural Networks in Forest Resource Management includes….– Forest land mapping and classification – Forest growth and dynamics modelling– Spatial data analysis and modelling– Plant disease dynamics modelling– Climate change and ecology– Predicting Tree Height and Forest Stock Volume– Hydrology – predicting the surface runoff

Changhui Peng and Xuezhi Wen, 1999

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Conclusion

• Neural networks are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain

• It was observed that the neural network was not influenced by scale of input data. The classification by original data was the same as when using standardized data

• The neural network tends to perform better when the data are more heterogeneous, characterizing the plants with regard to their groups

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Thank you……