The document provides an overview of machine learning concepts and applications in bioinformatics. It discusses key topics like supervised vs unsupervised learning, classification vs regression, linear vs non-linear models, and examples of machine learning algorithms like naive Bayes, neural networks, and support vector machines. Specific examples mentioned include using these algorithms for protein function prediction, gene finding, and predicting RNA binding sites in proteins.
36. Naïve Bayes Algorithm n n n n c P c x X x X x X P c P c x X x X x X P x X c P x X c P 2 2 1 1 2 2 1 1 ) 0 ( ) 0 | ,..., , ( ) 1 ( ) 1 | ,..., , ( ) | 0 ( ) | 1 (
41. Biological Neurons Dendrites receive inputs, Axon gives output Image from Christos Stergiou and Dimitrios Siganos http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
42. Artificial Neuron – “Perceptron” Image from Christos Stergiou and Dimitrios Siganos http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
43. The perceptron X1 X2 XN w1 w2 wN T Input Threshold Unit Output The perceptron classifies the input vector X into two categories. If the weights and threshold T are not known in advance, the perceptron must be trained . Ideally, the perceptron must be trained to return the correct answer on all training examples, and perform well on examples it has never seen. The training set must contain both type of data (i.e. with “1” and “0” output).
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45. The perceptron Training a perceptron: Find the weights W that minimizes the error function: P: number of training data X i : training vectors F(W.X i ): output of the perceptron t(X i ) : target value for X i Use steepest descent: - compute gradient: - update weight vector: - iterate (e: learning rate)
47. Artificial Neural Network A complete neural network is a set of perceptrons interconnected such that the outputs of some units becomes the inputs of other units. Many topologies are possible! Neural networks are trained just like perceptron, by minimizing an error function:
48. Support Vector Machines - SVMs Image from http://en.wikipedia.org/wiki/Support_vector_machine
49. SVM finds the maximum margin hyperplane Image from http://en.wikipedia.org/wiki/Support_vector_machine