Anticipating annotations and emerging trends in biomedical literature

The BioJournalMonitor is a decision support system for the analysis of trends and topics in the biomedical literature. Its main goal is to identify potential diagnostic and therapeu-tic biomarkers for specific diseases. Several data sources are continuously integrated to provide the user with up-to-date information on current research in this field. State-of-the-art text mining technologies are deployed to provide added value on top of the original content, including named en-tity detection, relation extraction, classification, clustering, ranking, summarization, and visualization. We present two novel technologies that are related to the analysis of tem-poral dynamics of text archives and associated ontologies. Currently, the MeSH ontology is used to annotate the sci-entific articles entering the PubMed database with medical terms. Both the maintenance of the ontology as well as the annotation of new articles is performed largely manually. We describe how probabilistic topic models can be used to anno-tate recent articles with the most likely MeSH terms. This provides our users with a competitive advantage because, when searching for MeSH terms, articles are found long be-fore they are manually annotated. We further present a study on how to predict the inclusion of new terms in the MeSH ontology. The results suggest that early prediction of emerging trends is possible. The trend ranking functions are deployed in our system to enable interactive searches for the hottest new trends relating to a disease

Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous Data

Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients, or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and, therefore, might converge toward local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to Order-Preserving Submatrices (OPSM), our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer data set, and random data. Results on the yeast genome showed that 89 percent of the 100 biggest nonoverlapping biclusters were enriched with Gene Ontology annotations. A comparison with the methods OPSM and Iterative Signature Algorithm (ISA, a generalization of singular value decomposition) demonstrated a better efficiency when using gene and condition orders. We present results on random and real data sets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters

HELIOS: pHotonics ELectronics functional Integration on CMOS

Silicon photonics have generated an increasing interest in the recent year, mainly for optical telecommunications or for optical interconnects in microelectronic circuits. The rationale of silicon photonics is the reduction of the cost of photonic systems through the integration of photonic components and an IC on a common chip, or in the longer term, the enhancement of IC performance with the introduction of optics inside a high performance chip. In order to build a Opto-Electronic Integrated circuit (OEIC), a large European project HELIOS has been launched two years ago. The objective is to combine a photonic layer with a CMOS circuit by different innovative means, using microelectronics fabrication processes. High performance generic building blocks that can be used for a broad range of applications are developed such as WDM sources by III-V/Si heterogeneous integration, fast Si modulators and Ge or InGaAs detectors, Si passive circuits and specific packaging. Different scenari for integrating photonic with an electronic chip and the recent advances on the building blocks of the Helios project are presented