neural networks, based on human brain, plays major role in the field of bioinformatics..

1. By Nancy Bansal

2.  Introduction
 Architecture
 Learning
 Applications

3.  Originally conceived as computational models
of the way in which the human brain works
 Learn relationships b/w sets of variables;
generalization
 Graceful degradation- knowledge learnt as
set of weights; any of these weights if
removed, network can still function but with
reduced performance

4.  Consists of interconnected units, arranged as
layers
 Perceptrons- 1 input, 1 output layer; linear
relationships b/w variables
 Multi-layer perceptron- no. of hidden layers
of units; 2 sets of weights increase the power
of network; non-linear relationships

5.  Training- repeatedly presents examples to
the network
 Transducers- converting one form of input to
another form of output; transfer functions or
activation functions
 Supervised learning- once trained, network
predicts output; tree identification
 Unsupervised learning- required response is
not known, training solely based on input
data, have no input, output and hidden layer
distinctions; clustering tasks

6.  Supervised learning
 feed-forward- direction of flow of data;
backpropagation- errors incurred are
propagated back through the network
 Classification, simulation

8.  Kohonen Self Organizing Map
 Unsupervised learning
 All the input nodes are connected to every
node; no distinct output layer
 Clustering, pattern recognition

10.  Response of the network is compared is to
the desired response
 Discrepancy b/w the two is calculated and the
network make changes to its internal weights
to reduce the error the next time input is
presented. Repeated for all the input data,
once completed, constitutes one epoch.

13.  Hidden layer
 Sigmoid function

16.  All the input nodes are interconnected with
all the output nodes without any hidden layer
 Winning node- output node with higher
activation function than all other output
nodes

18.  Coding region recognition and gene
identification
 Recognition of transcription and translational
signals
 Sequence feature analysis and classification
 Protein structure prediction
 Prediction of signal peptides
 Biometrics
 Data mining