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2018 | OriginalPaper | Buchkapitel

Granular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-metric Spaces

verfasst von : Alessio Martino, Alessandro Giuliani, Antonello Rizzi

Erschienen in: Computational Intelligence for Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Computational intelligence and pattern recognition techniques are gaining more and more attention as the main computing tools in bioinformatics applications. This is due to the fact that biology by definition, deals with complex systems and that computational intelligence can be considered as an effective approach when facing the general problem of complex systems modelling. Moreover, most data available on shared databases are represented by sequences and graphs, thus demanding the definition of meaningful dissimilarity measures between patterns, which are often non-metric in nature. Especially in such cases, evolutive and fully automatic machine learning systems are mandatory for dealing with parametric dissimilarity measures and/or for performing suitable feature selection. Besides other approaches, such as kernel methods and embedding in dissimilarity spaces, granular computing is a very promising framework not only for designing effective data-driven modelling systems able to determine automatically the correct representation (abstraction) level, but also for giving to field-experts (biologists) the possibility to investigate information granules (frequent substructures) that have been discovered by the machine learning system as the most relevant for the problem at hand. We expect that many important discoveries in biology and medicine in the next future will be determined by an increasingly stronger integration between the ongoing research efforts of natural sciences and modern inductive modelling tools based on computational intelligence, pattern recognition and granular computing techniques.

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Vorheriges Kapitel Deep Neural Networks for Structured Data
Nächstes Kapitel Multi-classifier-Systems: Architectures, Algorithms and Applications
Fußnoten
1
For example, let us consider a classification/clustering algorithm driven by the Euclidean distance. A common problem with the Euclidean distance is that features spanning a wider range of values have more influence in the resulting distance measure, therefore normalising all attributes in the same range (usually [0, 1] or \([-1,+1]\)) ensures fair contribution from all attributes, regardless of their original range.
 
2
In Statistics, outliers are “anomalous data” that for a given dissimilarity measure lie far away from most observations.
 
3
Non sunt multiplicanda entia sine necessitate (Entities are not to be multiplied without necessity), commonly known as “The Ockham’s Razor” Criterion (William of Ockham, circa 1287–1347). This criterion states that among a set of predicting models sharing the same performances, the simplest one (i.e. the one with the simplest decision surfaces) should be preferred. It is for sure one of the fundamental axioms for thoughtful and practical data-driven modelling.
 
4
Also known as hyperparameters in the Machine Learning terminology.
 
5
That is why evolutionary optimisation metaheuristics fall within the derivative-free methods.
 
6
A common choice for a genetic algorithm fitness function takes into account both the model performance and its structural complexity. Specifically, whilst the former should be maximised, the latter should be minimised in order to avoid overfitting (cf. the Ockham’s Razor Criterion).
 
7
That is why in most of the Chapter, unless explicitly specified, the generic term (dis)similarity will be used.
 
8
Indeed, the anatomical structure changes in the order of months/years depending on the age of subjects.
 
9
A finite set of points equipped with a notion of distance in a finite multidimensional space.
 
10
According to which the distance between two strings of equal length is given by the number of mismatches.
 
11
Also known as the Gram Matrix, after Danish mathematician Jørgen Pedersen Gram.
 
12
If the similarity measure at hand is not symmetric, patterns’ distance vectors as taken by rows or columns will be different. In order to overcome this problem, one can ‘force’ a similarity measure to be symmetric by considering \(\mathbf {S}:=(\mathbf {S}+\mathbf {S}^T)/2\) (e.g. [14]).
 
13
Also known as the Krebs cycle.
 
14
Protein molecules driving the folding of other protein systems.
 
15
Indeed, the absolute entity of metabolic rate can vary for a lot of reasons going from anatomical differences among patients to their actual nutrition state.
 
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Metadaten
Titel
Granular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-metric Spaces
verfasst von
Alessio Martino
Alessandro Giuliani
Antonello Rizzi
Copyright-Jahr
2018
Buch
Computational Intelligence for Pattern Recognition

Print ISBN: 978-3-319-89628-1

Electronic ISBN: 978-3-319-89629-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-89629-8_3

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