1,835 research outputs found
Predicting Genetic Regulatory Response Using Classification
We present a novel classification-based method for learning to predict gene
regulatory response. Our approach is motivated by the hypothesis that in simple
organisms such as Saccharomyces cerevisiae, we can learn a decision rule for
predicting whether a gene is up- or down-regulated in a particular experiment
based on (1) the presence of binding site subsequences (``motifs'') in the
gene's regulatory region and (2) the expression levels of regulators such as
transcription factors in the experiment (``parents''). Thus our learning task
integrates two qualitatively different data sources: genome-wide cDNA
microarray data across multiple perturbation and mutant experiments along with
motif profile data from regulatory sequences. We convert the regression task of
predicting real-valued gene expression measurement to a classification task of
predicting +1 and -1 labels, corresponding to up- and down-regulation beyond
the levels of biological and measurement noise in microarray measurements. The
learning algorithm employed is boosting with a margin-based generalization of
decision trees, alternating decision trees. This large-margin classifier is
sufficiently flexible to allow complex logical functions, yet sufficiently
simple to give insight into the combinatorial mechanisms of gene regulation. We
observe encouraging prediction accuracy on experiments based on the Gasch S.
cerevisiae dataset, and we show that we can accurately predict up- and
down-regulation on held-out experiments. Our method thus provides predictive
hypotheses, suggests biological experiments, and provides interpretable insight
into the structure of genetic regulatory networks.Comment: 8 pages, 4 figures, presented at Twelfth International Conference on
Intelligent Systems for Molecular Biology (ISMB 2004), supplemental website:
http://www.cs.columbia.edu/compbio/geneclas
Kernel methods in genomics and computational biology
Support vector machines and kernel methods are increasingly popular in
genomics and computational biology, due to their good performance in real-world
applications and strong modularity that makes them suitable to a wide range of
problems, from the classification of tumors to the automatic annotation of
proteins. Their ability to work in high dimension, to process non-vectorial
data, and the natural framework they provide to integrate heterogeneous data
are particularly relevant to various problems arising in computational biology.
In this chapter we survey some of the most prominent applications published so
far, highlighting the particular developments in kernel methods triggered by
problems in biology, and mention a few promising research directions likely to
expand in the future
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences. <br /
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
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The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception
Building and Querying Large Modelbases
Model building is one of the most important objectives of
data mining and data analysis. As many data mining
applications, such as personalization, bioinformatics and
some large enterprise-wide business applications, become
increasingly complex and require a very large number of
models, it is becoming progressively more difficult for data
analysts to built and to manage a large number of models
in these applications on their own. Therefore, development
of software tools helping data analysts in these tasks is
becoming a pressing issue. This paper presents a model
management system supporting various types of data
mining models. It describes how to build and populate
large heterogeneous modelbases. It also presents a query
language for querying these modelbases and examines
performance results for some of the queries.Information Systems Working Papers Serie
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