Review of Patient Gene Profiles Obtained through a Non-Negative Matrix Factorization-Based Framework to Determine the Role Inflammation Plays in Neuroblastoma Pathogenesis.

Neuroblastoma is the most common extracranial solid tumor in children. It is a highly heterogeneous tumor consisting of different subcellular types and genetic abnormalities. Literature data confirm the biological and clinical complexity of this cancer, which requires a wider availability of gene targets for the implementation of personalized therapy.

: a new crystallographic machine learning platform for crystal system determination.

Determination of the crystal system and space group is the first step of crystal structure analysis. Often this turns out to be a bottleneck in the material characterization workflow for polycrystalline compounds, thus requiring manual interventions. This work proposes a new machine-learning (ML)-based web platform, (Crystallography MachinE LeArning), for crystal systems classification.

Cluster of resistance-inducing genes in MCF-7 cells by estrogen, insulin, methotrexate and tamoxifen extracted via NMF.

Breast cancer has become a leading cause of death for women as the economy has grown and the number of women in the labor force has increased. Several biomarkers with diagnostic, prognostic, and therapeutic implications for breast cancer have been identified in studies, leading to therapeutic advances. Resistance, on the other hand, is one of clinical practice's limitations.

Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework.

Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments.

Colorectal cancer in Crohn's disease evaluated with genes belonging to fibroblasts of the intestinal mucosa selected by NMF.

Crohn's disease (CD) is a type of chronic, inflammatory bowel disease (IBD) which affects any part of the gastrointestinal tract. This study aims to understand the mechanism which activate mucosal fibroblasts in the microenvironment of the colon in CD and colorectal carcinomas and to extract fibroblasts phenotypes via a novel framework based on non-negative factorization of matrix (NMF). The results identify a fibroblast phenotype characterized by intense pro-inflammatory activity ensured by the presence of genes belonging to the APOBEC1 family, such as APOBEC3F and APOBEC3G.

Analysis of fibroblast genes selected by NMF to reveal the potential crosstalk between ulcerative colitis and colorectal cancer.

Patients with ulcerative colitis (UC) have an increased risk of developing colorectal cancer (CRC). The CRC risk extent raises with increasing age, duration of symptoms, severity of inflammation and dysplasia. CRC is a complex multi-stage process and associated with UC represents 2% of all colon cancers.

An NMF-Based Methodology for Selecting Biomarkers in the Landscape of Genes of Heterogeneous Cancer-Associated Fibroblast Populations.

The rapid development of high-performance technologies has greatly promoted studies of molecular oncology producing large amounts of data. Even if these data are publicly available, they need to be processed and studied to extract information useful to better understand mechanisms of pathogenesis of complex diseases, such as tumors. In this article, we illustrated a procedure for mining biologically meaningful biomarkers from microarray datasets of different tumor histotypes.

Orthogonal joint sparse NMF for microarray data analysis.

The 3D microarrays, generally known as gene-sample-time microarrays, couple the information on different time points collected by 2D microarrays that measure gene expression levels among different samples. Their analysis is useful in several biomedical applications, like monitoring dose or drug treatment responses of patients over time in pharmacogenomics studies. Many statistical and data analysis tools have been used to extract useful information.

Improving knowledge on the activation of bone marrow fibroblasts in MGUS and MM disease through the automatic extraction of genes via a nonnegative matrix factorization approach on gene expression profiles.

Background: Multiple myeloma (MM) is a cancer of terminally differentiated plasma that is part of a spectrum of blood diseases. The role of the micro-environment is crucial for MM clonal evolution.

Methods: This paper describes the analysis carried out on a limited number of genes automatically extracted by a nonnegative matrix factorization (NMF) based approach from gene expression profiles of bone marrow fibroblasts of patients with monoclonal gammopathy of undetermined significance (MGUS) and MM.

Dynamical modeling of liver Aquaporin-9 expression and glycerol permeability in hepatic glucose metabolism.

Liver is crucial in the homeostasis of glycerol, an important metabolic intermediate. Plasma glycerol is imported by hepatocytes mainly through Aquaporin-9 (AQP9), an aquaglyceroporin channel negatively regulated by insulin in rodents. AQP9 is of critical importance in glycerol metabolism since hepatic glycerol utilization is rate-limited at the hepatocyte membrane permeation step.