11,948 research outputs found
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
Exploiting the accumulated evidence for gene selection in microarray gene expression data
Machine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an important role in the modeling process, since these tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this particular scenario, it is extremely important to select genes by taking into account the possible interactions with other gene subsets. This paper shows that, by accumulating the evidence in favour (or against) each gene along the search process, the obtained gene subsets may constitute better solutions, either in terms of predictive accuracy or gene size, or in both. The proposed technique is extremely simple and applicable at a negligible overhead in cost.Postprint (published version
Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer
Methods for extracting marker genes that trigger the growth
of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution
can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification
accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
The Cure: Making a game of gene selection for breast cancer survival prediction
Motivation: Molecular signatures for predicting breast cancer prognosis could
greatly improve care through personalization of treatment. Computational
analyses of genome-wide expression datasets have identified such signatures,
but these signatures leave much to be desired in terms of accuracy,
reproducibility and biological interpretability. Methods that take advantage of
structured prior knowledge (e.g. protein interaction networks) show promise in
helping to define better signatures but most knowledge remains unstructured.
Crowdsourcing via scientific discovery games is an emerging methodology that
has the potential to tap into human intelligence at scales and in modes
previously unheard of. Here, we developed and evaluated a game called The Cure
on the task of gene selection for breast cancer survival prediction. Our
central hypothesis was that knowledge linking expression patterns of specific
genes to breast cancer outcomes could be captured from game players. We
envisioned capturing knowledge both from the players prior experience and from
their ability to interpret text related to candidate genes presented to them in
the context of the game.
Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted
more than 1,000 registered players who collectively played nearly 10,000 games.
Gene sets assembled through aggregation of the collected data clearly
demonstrated the accumulation of relevant expert knowledge. In terms of
predictive accuracy, these gene sets provided comparable performance to gene
sets generated using other methods including those used in commercial tests.
The Cure is available at http://genegames.org/cure
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