Machine Learning Method in Bioinformatics
Bioinformatics involves an integrated approach involving the use of information technology, computer science to biology and medicine as professional and knowledge fields. It encompasses the knowledge associated with information systems, artificial intelligence, databases, and algorithms, soft computing, software engineering, image processing, modeling and simulation, data mining, signal processing, computation theory and information, system an d control theory, discrete mathematics, statistics and circuit theory. On the other hand, machine learning entails a sub-division of artificial intelligence and operates with technical skills to permit computers to adapt to certain responses and initiate actions (Zhang et al., 2009). Machine learning entails a range technical knowledge that looks at the scientific application of search engines, natural language processing, bioinformatics, medical diagnosis and cheminformatics, analysis of the stock market, game playing and computer vision.
The development of machine learning has been as a matter of necessity given the fact that, current knowledge that needs some levels of sophistication and technological advancement has been on the consistent growth. These are included in the revolution in the genomic field that entails amino acid sequencing and nucleotide sequences. To accomplish the possibility of storing these essential informati0on, machine learning has been imperative has led t-o the building of several sophisticated interfaces that researchers can manipulate to establish access to the available databases. In general, it is evident that the abilities of computers in learning these large numbers of application has not only provided solutions to a great deal of technological problems, but has also provided a prolific ground for knowledge acquisition (Fasconi & Nato, 2003).
Machine Learning Approaches
The process of machine learning involves the adoption of certain approaches that assist in .performing separate objectives or functions. These involve two most applied learning scenarios with their distinct criterion functions (Zhang et al., 2009). These two approaches are commonly referred to as supervised and unsupervised selection approaches/criteria.
Supervised approach
This may also be intimated to as discrimination or prediction classification. In this approach, algorithms are developed to levels priori-defined. The construction of algorithm takes place in the dataset training followed by comprehensive tests on independent data set to examine the algorithmic accuracy and efficiency. In the process of regression and classification, a group of support vectors that are related to methods of supervised learning. Such related vector machines include among others linear classification, which develops a straight line providing a distinct boundary between two dimensions (zhang et al., 2009). These lines may also be referred to as hyper lines which have replaced the use of the dot product for reasons of fitting in the maximum-margin. A decision tree structure may also be applies whereby classifications are represented by the leaves while feature conjunctions that direct to the classification are represented by the branches. Decision tree algorithm may be efficiently changed into a paradigm of rules of production. The supervised appr5oach also entails the use of artificial Neural Networks, a group of nodes that are interconnected that process information through the use of computational model. The information that flows through the network whether external or internal may change ANN's structure. The relationship that exists between inputs and outputs can be modeled by the use of ANN. Multi-Layer Perception (MLP) and the Radial basis function (RBF) are the most used algorithms of the ANN.
Unsupervised classification
It involves two distinct ways applicable in designing of selection criteria. They are identified on their metric of performance illustrated as classification driven criterio0n and fidelity driven criterion. Fidelity driven criterion is dependent upon the bulk of the, original information stored or discarded after the reduction of the feature dimension. Unsupervised approach operates on the basis of cluster analysis in which the method of clustering separates objects, into a number of predetermined groups assuming a pattern...
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