4,489 research outputs found
Towards knowledge-based gene expression data mining
The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013
International Conference on Data Minin
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
Background The prediction of human gene–abnormal phenotype associations is a
fundamental step toward the discovery of novel genes associated with human
disorders, especially when no genes are known to be associated with a specific
disease. In this context the Human Phenotype Ontology (HPO) provides a
standard categorization of the abnormalities associated with human diseases.
While the problem of the prediction of gene–disease associations has been
widely investigated, the related problem of gene–phenotypic feature (i.e., HPO
term) associations has been largely overlooked, even if for most human genes
no HPO term associations are known and despite the increasing application of
the HPO to relevant medical problems. Moreover most of the methods proposed in
literature are not able to capture the hierarchical relationships between HPO
terms, thus resulting in inconsistent and relatively inaccurate predictions.
Results We present two hierarchical ensemble methods that we formally prove to
provide biologically consistent predictions according to the hierarchical
structure of the HPO. The modular structure of the proposed methods, that
consists in a “flat” learning first step and a hierarchical combination of the
predictions in the second step, allows the predictions of virtually any flat
learning method to be enhanced. The experimental results show that
hierarchical ensemble methods are able to predict novel associations between
genes and abnormal phenotypes with results that are competitive with state-of-
the-art algorithms and with a significant reduction of the computational
complexity. Conclusions Hierarchical ensembles are efficient computational
methods that guarantee biologically meaningful predictions that obey the true
path rule, and can be used as a tool to improve and make consistent the HPO
terms predictions starting from virtually any flat learning method. The
implementation of the proposed methods is available as an R package from the
CRAN repository
Benchmarking network propagation methods for disease gene identification
In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version
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