Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments. Results In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is tested on three manually constructed gene document collections. Its utility and performance as a knowledge discovery tool is demonstrated using a set of genes associated with Autism. Conclusions FAUN not only assists researchers to use biomedical literature efficiently, but also provides utilities for knowledge discovery. This Web-based software environment may be useful for the validation and analysis of functional associations in gene subsets identified by high-throughput experiments

IT skills in vocational training curricula and labour market outcomes

We use vocational training curricula to investigate how IT skills are trained within broader skills packages and how these relate to labour market outcomes. Skills packages are the typical combinations of IT skills (e.g., CNC) and technical or nontechnical skills (e.g., material sciences or work safety) that are jointly required in the real world and occur in training curricula. This broadened perspective of teaching IT skills offers new insights into how digital skills can be successfully integrated into future education and training programmes. We use legally binding vocational education and training (VET) curricula of dual apprenticeship training in Switzerland. We apply natural language processing methods to analyse the extensive curriculum texts, which meticulously define the skills that have to be taught. We identify four typical skills packages, each of which are centred around one of four different types of IT skill (CNC/CAD, control technologies, system technologies, IT-applications). Our empirical analyses show that VET graduates trained in these skills packages receive positive labour market outcomes compared to VET graduates without these skills packages. Moreover, we find that the positive outcomes are not just driven by differences in cognitive skill requirements of the respective occupations

Discovering gene functional relationships using a literature-based NMF model

The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. Several bioinformatics tools have been developed to extract and identify gene relationships from various biological databases. However, an intuitive user-interface tool that allows the biologist to determine functional relationships among genes is still not available. In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation. The FAUN site is publicly available at http://grits.eecs.utk.edu/faun