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 towards 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 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 dataset and random data. Results on the yeast genome showed that 89% of the one hundred biggest non-overlapping biclusters were enriched with Gene Ontology annotations. A comparison with OPSM and ISA demonstrated a better efficiency when using gene and condition orders. We present results on random and real datasets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.

Emerging trend prediction in biomedical literature.

We present a study on how to predict new emerging trends in the biomedical domain based on textual data. We thereby propose a way of anticipating the transformation of arbitrary information into ground truth knowledge by predicting the inclusion of new terms into the MeSH ontology. We also discuss the preparation of a dataset for the evaluation of emerging trend prediction algorithms that is based on PubMed abstracts and related MeSH terms. The results suggest that early prediction of emerging trends is possible.