12 research outputs found
WISDoM: Wishart Distributed Matrices Multiple Order classification. Definition and application to fMRI resting state data.
In this work we introduce the Wishart Distributed Matrices Multiple Order Classification (WISDoM) method.
The WISDoM Classification method consists of a pipeline for single feature analysis, supervised learning,cross validation and classification for any problems whose elements can be tied to a symmetric positive-definite matrix representation.
The general idea is for informations about properties of a certain system contained in a symmetric positive-definite matrix representation (i.e covariance and correlation matrices) to be extracted by modelling an estimated distribution for the expected classes of a given problem.
The application to fMRI data classification and clustering processing follows naturally: the WISDoM classification method has been tested on the ADNI2 (Alzheimer's Disease Neuroimaging Initiative) database.
The goal was to achieve good classification performances between Alzheimer's Disease diagnosed patients (AD) and Normal Control (NC) subjects, while retaining informations on which features were the most informative decision-wise.
In our work, the informations about topological properties contained in ADNI2 functional correlation matrices are extracted by modelling an estimated Wishart distribution for the expected diagnostical groups AD and NC, and allowed a complete separation between the two groups
The NMR added value to the Green Foodomics perspective: advances by machine learning to the holistic view on food and nutrition
Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modelling a necessity. High-throughput techniques, such as Nuclear Magnetic Resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modelling food-human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients, detect biomarkers of intake and study how they impact the metabolism of different individuals, study the kinetics of compounds in foods or their by-products to detect pathological conditions and improve the efficiency of in-silico models of the metabolic network
A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning
The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a
complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of
omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns.
The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure
Understanding the Kinetics of Nutrients Bioaccessibility by Modelling Foodomics Data
Holistic methods at the basis of the foodomics approach are allowing the in-depth understanding, at molecular and supramolecular level, of the complexity of food matrix. The latter, in turn, affects the nutrient bioaccessibility, one of the crucial factors impacting on the final effect of diets. However, many levels of complexity are emerging, relating to food-human interactions, while bolus descends along the whole gastrointestinal tract. Such complexity makes in-vitro and in-silico models still unable to fully describe intertwined kinetics between food matrix and human compartments. A possible framework to unravel complexity is outlined, starting from bioaccessibility modelling all the way down the to inter-compartmental kinetics. The aim is enhancing algorithms and models for prediction of the impact of a food category on a class of individuals. The proposed framework can consider many levels of complexity, provided that time-resolved experiments, suitable for integration with food matrix description, are correctly designed for this purpose
Spotting Frozen Curd in PDO Buffalo Mozzarella Cheese Through Insights on Its Supramolecular Structure Acquired by 1H TD-NMR Relaxation Experiments
The addition of frozen curd (FC) during the production process of \u201cMozzarella di Bufala Campana\u201d, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet availabl
Modello bayesiano per la regressione di dati troncati con applicazione a dati biologici
In questa tesi vengono valutati gli effetti sui modelli di regressione lineare da parte di troncature deterministiche dei dati analizzati, e viene proposto un metodo alternativo per effettuare queste regressioni, che rimuova queste distorsioni. In particolare vengono discussi questi effetti nel campo della ricerca biologica, come nel progetto Mark-Age.
Il progetto Mark-Age ha come obiettivo quello di ottenere un set di biomarcatori per l'invecchiamento, attraverso l'uso di metodi di data learning in analisi di tipo trasversale; elaborando cioè diverse variabili misurate sulle popolazioni esaminate riguardanti più sistemi fisiologici contemporaneamente e senza escludere interazioni locali fra queste.
E' necessario tenere conto in queste analisi che questi dati sono deterministicamente troncati per via dei criteri di selezione sull’età dei partecipanti, e che questo ha un effetto rilevante sui metodi di analisi standard, i quali invece ipotizzano che non vi sarebbe alcuna relazione fra l’assenza di un dato ed il suo valore, se questo fosse misurato.
In questa tesi vengono studiati gli effetti di questa troncatura sia per quanto riguarda la selezione di modelli ottimali, che della stima dei parametri per questi modelli.
Vengono studiati e caratterizzati questi effetti nell'ambito di un toy model, che permette di quantificare la distorsione e la perdita di potenza dovuta alla troncatura.
Viene inoltre introdotto un appropriato metodo di regressione, chiamato Tobit, che tenga conto di questi effetti. Questo metodo viene infine applicato ad un sottoinsieme dati del progetto Mark-Age, dimostrando una notevole riduzione del bias di predizione, ottenendo anche una stima della precisione di queste predizioni
Surface processing for iron-based degradable alloys: A preliminary study on the importance of acid pickling
The formation of a heterogeneous oxidized layer, also called scale, on metallic surfaces is widely recognized as a rapid manufacturing event for metals and their alloys. Partial or total removal of the scale represents a mandatory integrated step for the industrial fabrication processes of medical devices. For biodegradable metals, acid pickling has already been reported as a preliminary surface preparation given further processes, such as electropolishing. Unfortunately, biodegradable medical prototypes presented discrepancies concerning acid pickling studies based on samples with less complex geometry (e.g., non-uniform scale removal and rougher surface). Indeed, this translational knowledge lacks a detailed investigation on this process, deep characterization of treated surfaces properties, as well as a comprehensive discussion of the involved mechanisms. In this study, the effects of different acidic media (HCl, HNO3, H3PO4, CH3COOH, H2SO4 and HF), maintained at different temperatures (21 and 60 °C) for various exposition time (15–240 s), on the chemical composition and surface properties of a Fe–13Mn-1.2C biodegradable alloy were investigated. Changes in mass loss, morphology and wettability evidenced the combined effect of temperature and time for all conditions. Pickling in HCl and HF solutions favor mass loss (0.03–0.1 g/cm2) and effectively remove the initial scale
K-Clique Multiomics Framework: A Novel Protocol to Decipher the Role of Gut Microbiota Communities in Nutritional Intervention Trials
International audienceThe availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions