Yves Moreau

A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling

MOTIVATION Transcriptome analysis allows detection and clustering of genes that are coexpressed under various biological circumstances. Under the assumption that coregulated genes share cis-acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. To improve the robustness of the Gibbs sampling algorithm to noisy data sets we propose an extension of this algorithm for motif finding with a higher-order background model. RESULTS Simulated data and real biological data sets with well-described regulatory elements are used to test the influence of the different background models on the performance of the motif detection algorithm. We show that the use of a higher-order model considerably enhances the performance of our motif finding algorithm in the presence of noisy data. For Arabidopsis thaliana, a reliable background model based on a set of carefully selected intergenic sequences was constructed. AVAILABILITY Our implementation of the Gibbs sampler called the Motif Sampler can be used through a web interface: http://www.esat.kuleuven.ac.be/~thijs/Work/MotifSampler.html. CONTACT gert.thijs@esat.kuleuven.ac.be; yves.moreau@esat.kuleuven.ac.be

Emerging patterns of cryptic chromosomal imbalance in patients with idiopathic mental retardation and multiple congenital anomalies: a new series of 140 patients and review of published reports

Background: Chromosomal abnormalities are a major cause of mental retardation and multiple congenital anomalies (MCA/MR). Screening for these chromosomal imbalances has mainly been done by standard karyotyping. Previous array CGH studies on selected patients with chromosomal phenotypes and normal karyotypes suggested an incidence of 10–15% of previously unnoticed de novo chromosomal imbalances. Objective: To report array CGH screening of a series of 140 patients (the largest published so far) with idiopathic MCA/MR but normal karyotype. Results: Submicroscopic chromosomal imbalances were detected in 28 of the 140 patients (20%) and included 18 deletions, seven duplications, and three unbalanced translocations. Seventeen of 24 imbalances were confirmed de novo and 19 were assumed to be causal. Excluding subtelomeric imbalances, our study identified 11 clinically relevant interstitial submicroscopic imbalances (8%). Taking this and previously reported studies into consideration, array CGH screening with a resolution of at least 1 Mb has been undertaken on 432 patients with MCA/MR. Most imbalances are non-recurrent and spread across the genome. In at least 8.8% of these patients (38 of 432) de novo intrachromosomal alterations have been identified. Conclusions: Array CGH should be considered an essential aspect of the genetic analysis of patients with MCA/MR. In addition, in the present study three patients were mosaic for a structural chromosome rearrangement. One of these patients had monosomy 7 in as few as 8% of the cells, showing that array CGH allows detection of low grade mosaicisims.

Multi-view Text Mining for Disease Gene Prioritization and Clustering

Text mining helps biologists to collect disease-gene associations automatically from large volumes of biological literature. During the past ten years, there was a surge of interests in automatic exploration of the biomedical literature, ranging from the modest approach of annotating and extracting keywords from text to more ambitious attempts such as Natural Language Processing, text-mining based network construction and inference. In particular, these efforts effectively help biologists to identify the most likely disease candidates for further experimental validation. The most important resource for text mining applications now is the MEDLINE database developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM). MEDLINE covers all aspects of biology, chemistry, and medicine, there is almost no limit to the types of information that may be recovered through careful and exhaustive mining [45]. Therefore, a successful text mining approach relies much on an appropriate model. To create a text mining model, the selection of Controlled Vocabulary (CV) and the representation schemes of terms occupy a central role and the efficiency of biomedical knowledge discovery varies greatly between different text mining models. To address these challenges, we propose a multi-view text mining approach to retrieve information from different biomedical domain levels and combine them to identify the disease relevant genes through prioritization. The view represents a text mining result retrieved by a specific CV, so the concept of multi-view text mining is featured as applying multiple controlled vocabularies to retrieve the gene-centric perspectives from free text publications. Since all the information is retrieved from the same MEDLINE database but only varied by the CV, the term view also indicates that the data consists of multiple domain-based perspectives of the same corpus. We expect that the correlated and complementary information contained in the multi-view textual data can facilitate the understanding about the roles of genes in genetic diseases.

L n -norm Multiple Kernel Learning and Least Squares Support Vector Machines

In the era of information overflow, data mining and machine learning are indispensable tools to retrieve information and knowledge from data. The idea of incorporating several data sources in analysis may be beneficial by reducing the noise, as well as by improving statistical significance and leveraging the interactions and correlations between data sources to obtain more refined and higher-level information [50], which is known as data fusion. In bioinformatics, considerable effort has been devoted to genomic data fusion, which is an emerging topic pertaining to a lot of applications. At present, terabytes of data are generated by high-throughput techniques at an increasing rate. In data fusion, these terabytes are further multiplied by the number of data sources or the number of species. A statistical model describing this data is therefore not an easy matter. To tackle this challenge, it is rather effective to consider the data as being generated by a complex and unknown black box with the goal of finding a function or an algorithm that operates on an input to predict the output. About 15 years ago, Boser [8] and Vapnik [51] introduced the support vector method which makes use of kernel functions. This method has offered plenty of opportunities to solve complicated problems but also brought lots of interdisciplinary challenges in statistics, optimization theory, and the applications therein [40].

Cross-Species Candidate Gene Prioritization with MerKator

In modern biology, the use of high-throughput technologies allows researchers and practicians to quickly and efficiently screen the genome in order to identify the genetic factors of a given disorder.However these techniques are often generating large lists of candidate genes among which only one or a few are really associated to the biological process of interest. Since the individual validation of all these candidate genes is often too costly and time consuming, only the most promising genes are experimentally assayed.

Rayleigh Quotient-Type Problems in Machine Learning

For real matrices and vectors, given a positive definite matrix Q and a nonzero vector w, a Rayleigh quotient (also known as Rayleigh-Ritz ratio) is defined as