Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Role of Non-coding RNAs in chemotherapeutic treatments

The transcribed ultraconserved regions (T-UCRs) are a group of long non-coding RNAs involved in human carcinogenesis. The factors regulating the expression of T-UCRs and their mechanism of action in human cancers are unknown. In this work it was shown that high expression of uc.339 associates with lower survival in 204 non-small cell lung cancer (NSCLC) patients. Moreover, it was shown that uc.339 found up-regulated in archival NSCLC samples, acts as a decoy RNA for miR-339-3p, -663-3p and -95-5p. So, Cyclin E2, a direct target of three microRNAs is up-regulated, inducing cancer growth and migration. Evidence of this mechanism was provided from cell lines and primary samples confirming that TP53 directly regulates uc.339. These results support a key role for uc.339 in lung cancer.

Biomedical engineering for healthy ageing. Predictive tools for falls

Falls are common and burdensome accidents among the elderly. About one third of the population aged 65 years or more experience at least one fall each year. Fall risk assessment is believed to be beneficial for fall prevention. This thesis is about prognostic tools for falls for community-dwelling older adults. We provide an overview of the state of the art. We then take different approaches: we propose a theoretical probabilistic model to investigate some properties of prognostic tools for falls; we present a tool whose parameters were derived from data of the literature; we train and test a data-driven prognostic tool. Finally, we present some preliminary results on prediction of falls through features extracted from wearable inertial sensors. Heterogeneity in validation results are expected from theoretical considerations and are observed from empirical data. Differences in studies design hinder comparability and collaborative research. According to the multifactorial etiology of falls, assessment on multiple risk factors is needed in order to achieve good predictive accuracy.

"Device landing zone" calcification, assessed by MSCT, as a predictive factor for pacemaker implantation after TAVI

After trans-catheter aortic valve implantation (TAVI), the need for postinterventional pacemaker (PM) implantation can occur in as many as 10-50% of cases, but it is not yet clear, how this need can be predicted. The aim of this study was to assess the possible predictive factors of post TAVI PM implantation based on Computed Tomography (CT) measured aortic valve calcification and its distribution.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Predictive ability of a modified Örebro Musculoskeletal Pain Questionnaire in an acute/subacute low back pain working population

The original 'Örebro Musculoskeletal Pain Questionnaire' (original-ÖMPQ) has been shown to have limitations in practicality, factor structure, face and content validity. This study addressed these concerns by modifying its content producing the 'Örebro Musculoskeletal Screening Questionnaire' (ÖMSQ). The ÖMSQ and original-ÖMPQ were tested concurrently in acute/subacute low back pain working populations (pilot n = 44, main n = 106). The ÖMSQ showed improved face and content validity, which broadened potential application, and improved practicality with two-thirds less missing responses. High reliability (0.975, p < 0.05, ICC: 2.1), criterion validity (Spearman's r = 0.97) and internal consistency (α = 0.84) were achieved, as were predictive ability cut-off scores from ROC curves (112-120 ÖMSQ-points), statistically different ÖMSQ scores (p < 0.001) for each outcome trait, and a strong correlation with recovery time (Spearman's, r = 0.71). The six-component factor structure reflected the constructs originally proposed. The ÖMSQ can be substituted for the original-ÖMPQ in this population. Further research will assess its applicability in broader populations.

Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study

We evaluated the concurrent and predictive validity of a novel robotic surgery simulator in a prospective, randomized study.

Variation of vector quantisation and speech waveform coding

A new idea for waveform coding using vector quantisation (VQ) is introduced. This idea makes it possible to deal with codevectors much larger than before for a fixed bit per sample rate. Also a solution to the matching problem (inherent in the present context) in the &-norm describing a measure of neamess is presented. The overall computational complexity of this solution is O(n3 log, n). Sample results are presented to demonstrate the advantage of using this technique in the context of coding of speech waveforms.