121 research outputs found
DMET-Miner: Efficient discovery of association rules from pharmacogenomic data
AbstractMicroarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient’s samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation
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A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease
This work investigates the problem of feature selection
in neuroimaging features from structural MRI brain images
for the classification of subjects as healthy controls, suffering
from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic
Algorithm wrapper method for feature selection is adopted
in conjunction with a Support Vector Machine classifier. In very
large feature sets, feature selection is found to be redundant as
the accuracy is often worsened when compared to an Support
Vector Machine with no feature selection. However, when just
the hippocampal subfields are used, feature selection shows a
significant improvement of the classification accuracy. Three-class
Support Vector Machines and two-class Support Vector
Machines combined with weighted voting are also compared with
the former and found more useful. The highest accuracy achieved
at classifying the test data was 65.5% using a genetic algorithm
for feature selection with a three-class Support Vector Machine
classifier
MuLaN: a MultiLayer Networks Alignment Algorithm
A Multilayer Network (MN) is a system consisting of several topological
levels (i.e., layers) representing the interactions between the system's
objects and the related interdependency. Therefore, it may be represented as a
set of layers that can be assimilated to a set of networks of its own objects,
by means inter-layer edges (or inter-edges) linking the nodes of different
layers; for instance, a biological MN may allow modeling of inter and intra
interactions among diseases, genes, and drugs, only using its own structure.
The analysis of MNs may reveal hidden knowledge, as demonstrated by several
algorithms for the analysis. Recently, there is a growing interest in comparing
two MNs by revealing local regions of similarity, as a counterpart of Network
Alignment algorithms (NA) for simple networks. However, classical algorithms
for NA such as Local NA (LNA) cannot be applied on multilayer networks, since
they are not able to deal with inter-layer edges. Therefore, there is the need
for the introduction of novel algorithms. In this paper, we present MuLaN, an
algorithm for the local alignment of multilayer networks. We first show as
proof of concept the performances of MuLaN on a set of synthetic multilayer
networks. Then, we used as a case study a real multilayer network in the
biomedical domain. Our results show that MuLaN is able to build high-quality
alignments and can extract knowledge about the aligned multilayer networks.
MuLaN is available at https://github.com/pietrocinaglia/mulan
10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017
Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017. Procedia Computer Science, 108, 1113-1114. https://doi.org/10.1016/j.procs.2017.05.279We present the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017, held in Zurich, 12 - 14 June 2017.publishersversionpublishe
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Advanced feature selection methods in multinominal dementia classification from structural MRI data
Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification
BioPAX-Parser: parsing and enrichment analysis of BioPAX pathways
Abstract
Summary
Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, e.g. BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or stand-alone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform pathway enrichment analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap, we introduce BiP (BioPAX-Parser), an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with PEA by using information coming from pathways encoded in BioPAX.
Availability and implementation
BiP is freely available for academic and non-profit organizations at https://gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License.
Supplementary information
Supplementary data are available at Bioinformatics online
A System for the Analysis of Snore Signals
AbstractSleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring.This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring
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