@article {aRoisman18, title = {Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas}, journal = {British Journal of Haematology}, volume = {184}, year = {2019}, month = {Feb 2019}, pages = {373-383}, issn = {ISSN:1365-2141}, doi = {DOI: 10.1111/bjh.15656}, author = {A. Roisman and A. Navarro and G. Clot and G. Castellano and B. Gonzalez-Farre and P. P{\'e}rez-Galan and A. Esteve and M. Dabad and S. Heath and M. Gut and Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras and I. Slavusky and L. Magnano and H. Horn and A. Rosenwald and G. Ott and M. Aymerich and A. L{\'o}pez-Guillermo and P. Jares and J.I. Mart{\'\i}n-Subero and E. Campo and L. Hern{\'a}ndez} } @inbook {bBellot19, title = {Unsupervised GRN Ensemble}, booktitle = {Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology }, volume = {1883}, year = {2019}, pages = {283-302}, publisher = { Springer science, Humana Press}, organization = { Springer science, Humana Press}, address = {New York, NY}, issn = {978-1-4939-8881-5}, doi = {https://doi-org.recursos.biblioteca.upc.edu/10.1007/978-1-4939-8882-2}, author = {P. Bellot and Salembier, P. and Pham, N.C. and Meyer, P. E.} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @article {aBellot Pujalte15, title = {NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {09/2015}, abstract = {

Background:
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results:
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions:
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

}, keywords = {Benchmark, Bioconductor package, Gene expression, Gene regulation network reconstruction, Gene regulatory networks, Synthetic genetic networks}, doi = {10.1186/s12859-015-0728-4}, url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0728-4}, author = {P. Bellot and Olsen, Catharina and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @conference {Bell1512:Study, title = {Study of Normalization and Aggregation Approaches for Consensus Network Estimation}, booktitle = {2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Artificial Life (2015 IEEE ALIFE)}, year = {2015}, address = {Cape Town, South Africa}, abstract = {

Inferring gene regulatory networks from expression data is a very difficult problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that the different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this paper, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because the are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

}, author = {P. Bellot and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @article {cBellot Pujalte14, title = {Efficient combination of pairwise feature networks}, journal = {JMLR: Workshop and Conference Proceedings }, volume = {46}, year = {2014}, month = {10/2015}, pages = {77 - 84}, publisher = {JMLR: Workshop and Conference Proceedings}, address = {Nancy, France}, abstract = {

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). We are able to better eliminate indirect links, improving therefore the quality of the network via a normalization and ensemble process of GTE and three new informative features. The approach is based on a simple combination of networks, which is remarkably fast. The performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition.

}, keywords = {connectomes, elimination of indirect links, network reconstruction algorithms}, author = {P. Bellot and Meyer, P. E.} } @conference {cBosio13a, title = {Ensemble learning and hierarchical data representation for microarray classification}, booktitle = {13th IEEE International Conference on BioInformatics and BioEngineering BIBE}, year = {2013}, month = {11/2013}, publisher = {13th IEEE International Conference on BioInformatics and BioEngineering,}, organization = {13th IEEE International Conference on BioInformatics and BioEngineering,}, address = {Chania, Crete}, abstract = {

Microarray data classification is an open and active research field.

The development of more accurate algorithms is of great interest also because many of the techniques can be straightforwardly applied in analyzing different kinds of omics data.

In this work, an ensemble feature selection algorithm is applied within a binary classification framework from [1] that already got good predictive results. Ensemble feature selection is a rich field of research.

Ensemble techniques take some individual experts (i.e. classifiers) to combine them to improve the individual expert results with a voting scheme.

In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote.

Two versions of an ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC [2].

The studied ensemble technique improves the state of the art results by producing classifiers with significantly better results and the proposed key elements have shown to be useful for the prediction performance.

}, keywords = {Microarray classification; metagenes; hierarchical representation; Treelets; feature selection; LDA; ensemble.}, author = {Bosio, M. and Salembier, P. and Albert Oliveras and P. Bellot} } @conference {cBosio13, title = {Hierarchical Clustering Combining Numerical and Biological Similarities for Gene Expression Data Classification}, booktitle = {35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}13) }, year = {2013}, month = {07/2013}, publisher = {IEEE EMBS}, organization = {IEEE EMBS}, address = {Osaka, Japan}, keywords = {Biological interpretation, Hierarchical representation, microarray classification}, author = {Bosio, M. and Salembier, P. and P. Bellot and Albert Oliveras} } @article {aBosio12, title = {Gene Expression Data Classification Combining Hierarchical Representation and Efficient Feature Selection}, journal = {Journal of Biological Systems}, volume = {20}, year = {2012}, pages = {349-375}, abstract = {

A general framework for microarray data classification is proposed in this paper. It produces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the common behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of functionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process.

The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

}, doi = {10.1142/S0218339012400025}, url = {http://www.worldscientific.com/doi/abs/10.1142/S0218339012400025}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12, title = {Microarray classification with hierarchical data representation and novel feature selection criteria}, booktitle = {IEEE 12th International Conference on BioInformatics and BioEngineering}, year = {2012}, month = {11/2012}, address = {Larnaca, Cyprus}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12a, title = {Multiclass cancer microarray classification algorithm with Pair-Against-All redundancy}, booktitle = {The 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS{\textquoteright}12)}, year = {2012}, month = {12/2012}, address = {Washington, DC, USA}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio11, title = {Feature set enhancement via hierarchical clustering for microarray classification}, booktitle = {IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2011}, year = {2011}, month = {dec.}, pages = {226 -229}, abstract = {

A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible to reduce the risk of overfitting is obtained. This method has been tested on three public cancer datasets: leukemia, lymphoma and colon. The proposed method has obtained interesting classification results and the experiments have confirmed the utility of both metagenes and feature ranking criterion to improve the final classifier.

}, keywords = {cancer, classical classification error rate, classifier, colon, feature set enhancement, gene expression classification, hierarchical clustering, leukemia, lymphoma, medical computing, metagenes, microarray classification, overfitting risk reduction, pattern classification, pattern clustering, public cancer datasets, reliability measure, wrapper feature selection process}, doi = {10.1109/GENSiPS.2011.6169486}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} }