1,341 research outputs found
Differential gene expression graphs: A data structure for classification in DNA microarrays
This paper proposes an innovative data structure to be used as a backbone in designing microarray phenotype sample classifiers. The data structure is based on graphs and it is built from a differential analysis of the expression levels of healthy and diseased tissue samples in a microarray dataset. The proposed data structure is built in such a way that, by construction, it shows a number of properties that are perfectly suited to address several problems like feature extraction, clustering, and classificatio
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Heterogeneous network embedding enabling accurate disease association predictions.
BackgroundIt is significant to identificate complex biological mechanisms of various diseases in biomedical research. Recently, the growing generation of tremendous amount of data in genomics, epigenomics, metagenomics, proteomics, metabolomics, nutriomics, etc., has resulted in the rise of systematic biological means of exploring complex diseases. However, the disparity between the production of the multiple data and our capability of analyzing data has been broaden gradually. Furthermore, we observe that networks can represent many of the above-mentioned data, and founded on the vector representations learned by network embedding methods, entities which are in close proximity but at present do not actually possess direct links are very likely to be related, therefore they are promising candidate subjects for biological investigation.ResultsWe incorporate six public biological databases to construct a heterogeneous biological network containing three categories of entities (i.e., genes, diseases, miRNAs) and multiple types of edges (i.e., the known relationships). To tackle the inherent heterogeneity, we develop a heterogeneous network embedding model for mapping the network into a low dimensional vector space in which the relationships between entities are preserved well. And in order to assess the effectiveness of our method, we conduct gene-disease as well as miRNA-disease associations predictions, results of which show the superiority of our novel method over several state-of-the-arts. Furthermore, many associations predicted by our method are verified in the latest real-world dataset.ConclusionsWe propose a novel heterogeneous network embedding method which can adequately take advantage of the abundant contextual information and structures of heterogeneous network. Moreover, we illustrate the performance of the proposed method on directing studies in biology, which can assist in identifying new hypotheses in biological investigation
How is a data-driven approach better than random choice in label space division for multi-label classification?
We propose using five data-driven community detection approaches from social
networks to partition the label space for the task of multi-label
classification as an alternative to random partitioning into equal subsets as
performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector,
infomap, walktrap and label propagation algorithms. We construct a label
co-occurence graph (both weighted an unweighted versions) based on training
data and perform community detection to partition the label set. We include
Binary Relevance and Label Powerset classification methods for comparison. We
use gini-index based Decision Trees as the base classifier. We compare educated
approaches to label space divisions against random baselines on 12 benchmark
data sets over five evaluation measures. We show that in almost all cases seven
educated guess approaches are more likely to outperform RAkELd than otherwise
in all measures, but Hamming Loss. We show that fastgreedy and walktrap
community detection methods on weighted label co-occurence graphs are 85-92%
more likely to yield better F1 scores than random partitioning. Infomap on the
unweighted label co-occurence graphs is on average 90% of the times better than
random paritioning in terms of Subset Accuracy and 89% when it comes to Jaccard
similarity. Weighted fastgreedy is better on average than RAkELd when it comes
to Hamming Loss
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Statistical Methods in Integrative Genomics
Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions
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