We report on Nimfa, is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported.
COBISS.SI-ID: 9067604
We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 data sets from the Gene Expression Omnibus we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines.
COBISS.SI-ID: 8578900
We have developed a new network layout optimization technique which can incorporate additional information on relations between unconnected network components. It uses a two-step approach by first arranging the nodes within each of the components and then placing the components so that their proximity in the network corresponds to their relatedness. In the experimental study with on leukemia we demonstrate that FragViz can obtain network layouts which are more interpretable and hold additional information that could not be exposed using classical network layout optimization algorithms.
COBISS.SI-ID: 7964756
Using high-throughput genetics we have determined on the molecular level an effect of a toxic phospholipase A2 in yeast S. cerevisiae. With the help of bioinformatics methods, the results of the genetic screenings we have generated hypotheses and subsequently tested them. This way we demonstrated that yeast is a useful model organism for studying also the molecular targets of toxins that have evolutionary evolved for action in mammalian cells.
COBISS.SI-ID: 23541287
OBJECTIVES: With particular emphasis on interactions between cholesterol homeostasis and drug metabolism we investigate the transcriptome of human primary hepatocytes treated by two commonly prescribed cholesterol lowering drugs atorvastatin and rosuvastatin and by rifampicin that serves as an outgroup as well as a model substance for induction of nuclear pregnane X receptor. METHODS: Hepatocytes from human donors have been treated with rosuvastatin, atorvastatin, and rifampicin for 12, 24, and 48 h. Expression profiling with cholesterol and drug metabolism enriched low density SteroltalkcDNA and whole genome Affymetrix HG-U133 Plus 2.0 arrays has been applied. Differential expression (DE) of genes and gene set enrichment analysis of KEGG pathways were performed. Lists of differentially expressed genes and gene sets were cross-compared. Selected genes were confirmed by quantitative real-time PCR. RESULTS: Statins lead to: (a) upregulation of cholesterol-related genes indicating an increased LDL uptake and storage of esterified cholesterol, elevated bile acid/drug export and lower capacity to form HDL; (b) perturbation of genes in glucose and fatty acid homeostasis, influencing acetyl-CoA pools, promoting gluconeogenesis and glucose export; (c) elevated expression of ADIPOR2 suggesting increased sensitivity to adiponectin; (d) perturbations in genes of lipoprotein particle formation, differently for each statin; (e) perturbed expression of many metabolic genes that are directly controlled by nuclear receptors constitutive androstan and/or pregnane X. CONCLUSION: These data provide a novel global insight into hepatic effects of statins, offering biochemical explanations for higher bloodglucose in statin-treated patients, and for drug-induced secondary fatty liver disease.
COBISS.SI-ID: 28866009