latest news & announcements

Netter: re-ranking gene network inference predictions using structural network properties

Background: Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology. However, the structural properties of the predicted network do not resemble those typical for a gene regulatory network, as most algorithms only take into account connections found in the data and do not include known graph properties in their inference process.

Taking Aim at Moving Targets in Computational Cell Migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data.

Congratulations to BIG N2N in the media

"Indeed interdisciplinary collaboration is crucial. Glad to let you know BIG N2N at UGent and BioImaging Core are right on track."
Alexander Botzski, VIB-BITS

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel.

Cell line name recognition in support of the identification of synthetic lethality in cancer from text

Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature.

Identification and Validation of WISP1 as an Epigenetic Regulator of Metastasis in Oral Squamous Cell Carcinoma

Lymph node (LN) metastasis is the most important prognostic factor in oral squamous cell carcinoma (OSCC) patients. However, in approximately one third of OSCC patients nodal metastases remain undetected, and thus are not adequately treated. Therefore, clinical assessment of LN metastasis needs to be improved. The purpose of this study was to identify DNA methylation biomarkers to predict LN metastases in OSCC. Genome wide methylation assessment was performed on six OSCC with (N+) and six without LN metastases (N0).

Bioinformatics in the class room: 'Wetenschap op Stap'

In the context of the VIB outreach project Wetenschap op Stap (link here), the BIG N2N project manager Dr. Katrijn Vannerum visited three primary school classes in Ghent to acquaint them with bioinformatics and its applications. The children were inducted into the genetic code: they cracked it both with their brains and with bioinformatic tools. Their enthusiasm for science is aroused!

Arne De Coninck obtained his PhD at BIG N2N: 'High performance computing for large-scale genomic prediction'

In the past decades genetics was studied intensively leading to the knowledge that DNA is the molecule behind genetic inheritance and starting from the new millennium next-generation sequencing methods made it possible to sample this DNA with an ever decreasing cost. Animal and plant breeders have always made use of genetic information to predict agronomic performance of new breeds.