9,602 research outputs found
Finding rule groups to classify high dimensional gene expression datasets
Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches <br /
An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
Multi-test Decision Tree and its Application to Microarray Data Classification
Objective:
The desirable property of tools used to investigate biological data is
easy to understand models and predictive decisions.
Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity.
Methods:
We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions.
Results:
Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on datasets by an average percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model
are supported by biological evidence in the literature.
Conclusion:
This paper introduces a new type of decision tree which is more suitable for solving biological problems.
MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts
Transcription Factor-DNA Binding Via Machine Learning Ensembles
We present ensemble methods in a machine learning (ML) framework combining
predictions from five known motif/binding site exploration algorithms. For a
given TF the ensemble starts with position weight matrices (PWM's) for the
motif, collected from the component algorithms. Using dimension reduction, we
identify significant PWM-based subspaces for analysis. Within each subspace a
machine classifier is built for identifying the TF's gene (promoter) targets
(Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool.
Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string)
feature PWM-based subspaces that stand out in identifying gene targets. We
approach Problem 3 (binding sites) with a novel machine learning approach that
uses promoter string features and ML importance scores in a classification
algorithm locating binding sites across the genome. For target gene
identification this method improves performance (measured by the F1 score) by
about 10 percentage points over the (a) motif scanning method and (b) the
coexpression-based association method. Top motif outperformed 5 component
algorithms as well as two other common algorithms (BEST and DEME). For
identifying individual binding sites on a benchmark cross species database
(Tompa et al., 2005) we match the best performer without much human
intervention. It also improved the performance on mammalian TFs.
The ensemble can integrate orthogonal information from different weak
learners (potentially using entirely different types of features) into a
machine learner that can perform consistently better for more TFs. The TF gene
target identification component (problem 1 above) is useful in constructing a
transcriptional regulatory network from known TF-target associations. The
ensemble is easily extendable to include more tools as well as future PWM-based
information.Comment: 33 page
Dynamic Linear Discriminant Analysis in High Dimensional Space
High-dimensional data that evolve dynamically feature predominantly in the
modern data era. As a partial response to this, recent years have seen
increasing emphasis to address the dimensionality challenge. However, the
non-static nature of these datasets is largely ignored. This paper addresses
both challenges by proposing a novel yet simple dynamic linear programming
discriminant (DLPD) rule for binary classification. Different from the usual
static linear discriminant analysis, the new method is able to capture the
changing distributions of the underlying populations by modeling their means
and covariances as smooth functions of covariates of interest. Under an
approximate sparse condition, we show that the conditional misclassification
rate of the DLPD rule converges to the Bayes risk in probability uniformly over
the range of the variables used for modeling the dynamics, when the
dimensionality is allowed to grow exponentially with the sample size. The
minimax lower bound of the estimation of the Bayes risk is also established,
implying that the misclassification rate of our proposed rule is minimax-rate
optimal. The promising performance of the DLPD rule is illustrated via
extensive simulation studies and the analysis of a breast cancer dataset.Comment: 34 pages; 3 figure
Context-aware visual exploration of molecular databases
Facilitating the visual exploration of scientific data has
received increasing attention in the past decade or so. Especially
in life science related application areas the amount
of available data has grown at a breath taking pace. In this
paper we describe an approach that allows for visual inspection
of large collections of molecular compounds. In
contrast to classical visualizations of such spaces we incorporate
a specific focus of analysis, for example the outcome
of a biological experiment such as high throughout
screening results. The presented method uses this experimental
data to select molecular fragments of the underlying
molecules that have interesting properties and uses the
resulting space to generate a two dimensional map based
on a singular value decomposition algorithm and a self organizing
map. Experiments on real datasets show that
the resulting visual landscape groups molecules of similar
chemical properties in densely connected regions
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