The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278915 | PMC |
http://dx.doi.org/10.7150/jca.10631 | DOI Listing |
PLoS One
January 2025
Department of Biochemistry, College of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Long non-coding RNAs (lncRNAs) are among the most abundant types of non-coding RNAs in the genome and exhibit particularly high expression levels in the brain, where they play crucial roles in various neurophysiological and neuropathological processes. Although ischemic stroke is a complex multifactorial disease, the involvement of brain-derived lncRNAs in its intricate regulatory networks remains inadequately understood. In this study, we established a cerebral ischemia-reperfusion injury model using middle cerebral artery occlusion (MCAO) in male Sprague-Dawley rats.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
View Article and Find Full Text PDFNeuro Oncol
January 2025
Department of Neurosurgery, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg.
Background: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells.
View Article and Find Full Text PDFMetab Brain Dis
January 2025
Hepato-Neuro Laboratory, Centre Hospitalier de l'Université de Montréal (CRCHUM), Université de Montréal, 900, Rue Saint-Denis - Pavillon R, R08.422, Montréal (Québec), H2X 0A9, Canada.
Sarcopenia and hepatic encephalopathy (HE) are complications of chronic liver disease (CLD), which negatively impact clinical outcomes. Hyperammonemia is considered to be the central component in the pathogenesis of HE, however ammonia's toxic effects have also been shown to impinge on extracerebral organs including the muscle. Our aim was to investigate the effect of attenuating hyperammonemia with ornithine phenylacetate (OP) on muscle mass loss and associated molecular mechanisms in rats with CLD.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Civil Engineering, University of Qom, Qom, Iran.
In this study, the water-energy nexus is investigated throughout coupling the Water Evaluation and Planning (WEAP) and Low Emission Analysis Platform (LEAP) models under the climate change effects in the Marun basin, Iran. For this purpose, first, the climate change effects on water resources and consumption nodes are calculated under representative concentration pathway (RCP) scenarios from the fifth report of the International Panel on Climate Change (IPCC). Artificial neural network (ANN) is used to model river inflow and Cropwat model is used for agricultural water demand in future time (2015-2040).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!