Machine Learning in Bioinformatics
Aus der Reihe
Wiley Series in Bioinformatics
125,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
Kopierschutz
Ja
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
26.03.2009
Herausgeber
Yanqing Zhang + weitereVerlag
WileySeitenzahl
400 (Printausgabe)
Dateigröße
9618 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9780470397411
An introduction to machine learning methods and their applications
to problems in bioinformatics
Machine learning techniques are increasingly being used to
address problems in computational biology and bioinformatics. Novel
computational techniques to analyze high throughput data in the
form of sequences, gene and protein expressions, pathways, and
images are becoming vital for understanding diseases and future
drug discovery. Machine learning techniques such as Markov models,
support vector machines, neural networks, and graphical models have
been successful in analyzing life science data because of their
capabilities in handling randomness and uncertainty of data noise
and in generalization.
From an internationally recognized panel of prominent
researchers in the field, Machine Learning in Bioinformatics
compiles recent approaches in machine learning methods and their
applications in addressing contemporary problems in bioinformatics.
Coverage includes: feature selection for genomic and proteomic data
mining; comparing variable selection methods in gene selection and
classification of microarray data; fuzzy gene mining;
sequence-based prediction of residue-level properties in proteins;
probabilistic methods for long-range features in biosequences; and
much more.
Machine Learning in Bioinformatics is an indispensable resource
for computer scientists, engineers, biologists, mathematicians,
researchers, clinicians, physicians, and medical informaticists. It
is also a valuable reference text for computer science,
engineering, and biology courses at the upper undergraduate and
graduate levels.
to problems in bioinformatics
Machine learning techniques are increasingly being used to
address problems in computational biology and bioinformatics. Novel
computational techniques to analyze high throughput data in the
form of sequences, gene and protein expressions, pathways, and
images are becoming vital for understanding diseases and future
drug discovery. Machine learning techniques such as Markov models,
support vector machines, neural networks, and graphical models have
been successful in analyzing life science data because of their
capabilities in handling randomness and uncertainty of data noise
and in generalization.
From an internationally recognized panel of prominent
researchers in the field, Machine Learning in Bioinformatics
compiles recent approaches in machine learning methods and their
applications in addressing contemporary problems in bioinformatics.
Coverage includes: feature selection for genomic and proteomic data
mining; comparing variable selection methods in gene selection and
classification of microarray data; fuzzy gene mining;
sequence-based prediction of residue-level properties in proteins;
probabilistic methods for long-range features in biosequences; and
much more.
Machine Learning in Bioinformatics is an indispensable resource
for computer scientists, engineers, biologists, mathematicians,
researchers, clinicians, physicians, and medical informaticists. It
is also a valuable reference text for computer science,
engineering, and biology courses at the upper undergraduate and
graduate levels.
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