An ontology-based approach for modelling and querying Alzheimer's disease data.

Background: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity.

Upper gut heat shock proteins HSP70 and GRP78 promote insulin resistance, hyperglycemia, and non-alcoholic steatohepatitis.

A high-fat diet increases the risk of insulin resistance, type-2 diabetes, and non-alcoholic steato-hepatitis. Here we identified two heat-shock proteins, Heat-Shock-Protein70 and Glucose-Regulated Protein78, which are increased in the jejunum of rats on a high-fat diet. We demonstrated a causal link between these proteins and hepatic and whole-body insulin-resistance, as well as the metabolic response to bariatric/metabolic surgery.

A Content-Based Retrieval Framework for Whole Metagenome Sequencing Samples.

Finding similarities and differences between metagenomic samples within large repositories has been rather a significant issue for researchers. Over the recent years, content-based retrieval has been suggested by various studies from different perspectives. In this study, a content-based retrieval framework for identifying relevant metagenomic samples is developed.

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.

Background: The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes.

Metabolic surgery improves insulin resistance through the reduction of gut-secreted heat shock proteins.

Metabolic surgery improves insulin resistance and is associated with the remission of type 2 diabetes, but the mechanisms involved remain unknown. We find that human jejunal mucosa secretes heat shock proteins (HSPs) in vitro, in particular HSP70 and GRP78. Circulating levels of HSP70 are higher in people resistant to insulin, compared to the healthy and normalize after duodenal-jejunal bypass.

Combining EEG signal processing with supervised methods for Alzheimer's patients classification.

Background: Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.

Methods: In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples.

Incidence of Hypoglycemia After Gastric Bypass vs Sleeve Gastrectomy: A Randomized Trial.

Context: We compared the incidence of hypoglycemia after Roux-en-Y gastric bypass (RYGB) vs sleeve gastrectomy (SG).

Design, Setting, And Main Outcome Measures: Randomized, open-label trial conducted at the outpatient obesity clinic in a university hospital in Rome, Italy. The primary aim was the incidence of reactive hypoglycemia (<3.

Intensive lifestyle modifications with or without liraglutide 3mg vs. sleeve gastrectomy: A three-arm non-randomised, controlled, pilot study.

Background/objectives: As only 1% of clinically eligible subjects choose to undergo surgical treatment for obesity, other options should be investigated. This study aimed to assess the effects of intensive lifestyle modification (ILM) with or without 3-mg liraglutide daily vs. sleeve gastrectomy (SG) on BMI after 1 year.

Downregulation of Insulin Sensitivity After Oral Glucose Administration: Evidence for the Anti-Incretin Effect.

Intestinal nutrients stimulate insulin secretion more potently than intravenous (IV) glucose administration under similar plasma glucose levels (incretin effect). According to the anti-incretin theory, intestinal nutrients should also cause a reduction of insulin sensitivity and/or secretion (anti-incretin effect) to defend against hyperinsulinemia-hypoglycemia. An exaggerated anti-incretin effect could contribute to insulin resistance/type 2 diabetes, whereas reduction of anti-incretin signals might explain diabetes improvement after bariatric surgery.

TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas.

Background: Data extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types.

Results: We propose TCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration.

MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification.

Background: Continuous improvements in next generation sequencing technologies led to ever-increasing collections of genomic sequences, which have not been easily characterized by biologists, and whose analysis requires huge computational effort. The classification of species emerged as one of the main applications of DNA analysis and has been addressed with several approaches, e.g.

Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis.

Insulin resistance is the common denominator of several diseases including type 2 diabetes and cancer, and investigating the mechanisms responsible for insulin signaling impairment is of primary importance. A mathematical model of the insulin signaling network (ISN) is proposed and used to investigate the dose-response curves of components of this network. Experimental data of C2C12 myoblasts with phosphatase and tensin homologue (PTEN) suppressed and data of L6 myotubes with induced insulin resistance have been analyzed by the model.

Comparing Alzheimer's and Parkinson's diseases networks using graph communities structure.

Background: Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors.

LAF: Logic Alignment Free and its application to bacterial genomes classification.

Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa.

CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules.

Motivation: Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes).

Bariatric-metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-centre, randomised controlled trial.

Background: Randomised controlled trials have shown that bariatric surgery is more effective than conventional treatment for the short-term control of type-2 diabetes. However, published studies are characterised by a relatively short follow-up. We aimed to assess 5 year outcomes from our randomised trial designed to compare surgery with conventional medical treatment for the treatment of type 2 diabetes in obese patients.

Predictors of remission of diabetes mellitus in severely obese individuals undergoing bariatric surgery: do BMI or procedure choice matter? A meta-analysis.

Objective: To compare diabetes remission after bariatric surgery in subjects with body mass index (BMI) of 35 kg/m2 or more or BMI of less than 35 kg/m to determine which predictors are best.

Background: BMI is currently the only selection criterion for bariatric surgery in diabetic subjects. Many studies have challenged BMI for predicting diabetes remission.

Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers.

Scarce work has been done in the analysis of the composition of conserved non-coding elements (CNEs) that are identified by comparisons of two or more genomes and are found to exist in all metazoan genomes. Here we present the analysis of CNEs with a methodology that takes into account word occurrence at various lengths scales in the form of feature vector representation and rule based classifiers. We implement our approach on both protein-coding exons and CNEs, originating from human, insect (Drosophila melanogaster) and worm (Caenorhabditis elegans) genomes, that are either identified in the present study or obtained from the literature.

Supervised DNA Barcodes species classification: analysis, comparisons and results.

Background: Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms.

Mathematical modeling of renal tubular glucose absorption after glucose load.

A partial differential Progressive Tubular Reabsorption (PTR) model, describing renal tubular glucose reabsorption and urinary glucose excretion following a glucose load perturbation, is proposed and fitted to experimental data from five subjects. For each subject the Glomerular Filtration Rate was estimated and both blood and urine glucose were sampled following an Intra-Venous glucose bolus. The PTR model was compared with a model representing the conventional Renal Threshold Hypothesis (RTH).

BLOG 2.0: a software system for character-based species classification with DNA Barcode sequences. What it does, how to use it.

BLOG (Barcoding with LOGic) is a diagnostic and character-based DNA Barcode analysis method. Its aim is to classify specimens to species based on DNA Barcode sequences and on a supervised machine learning approach, using classification rules that compactly characterize species in terms of DNA Barcode locations of key diagnostic nucleotides. The BLOG 2.