Knowledge extraction from biomedical data using machine learning

PhD ThesisThanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.European Union Seventh Framework Programme (FP7/2007- 2013) that funded part of this work under the “D-BOARD” project (grant agreement number 305815)

Nicotine dependence and psychological distress: outcomes and clinical implications in smoking cessation

Nicotine dependence is characteristically a chronic and relapsing disease. Although 75%–85% of smokers would like to quit, and one-third make at least three serious lifetime attempts, less than 50% of smokers succeed in stopping before the age of 60. Relevant and complex factors contributing to sustained cigarette consumption, and strongly implicated in the clinical management of smokers, are the level of nicotine dependence and psychological distress. In this review of the literature, these two factors will be examined in detail to show how they may affect smoking cessation outcome and to encourage clinicians to assess patients so they can offer tailored support in quitting smoking

Organization of the Autonomic Nuclei in the Spinal Cord : Functional Morphology

Disease-associated genes. Complete list of the disease-associated genes for each dataset. (XZ 46 kb

Intensive insulin therapy increases glutathione synthesis rate in surgical ICU patients with stress hyperglycemia

OBJECTIVE: The glutathione system plays an essential role in antioxidant defense after surgery. We assessed the effects of intensive insulin treatment (IIT) on glutathione synthesis rate and redox balance in cancer patients, who had developed stress hyperglycemia after major surgery. METHODS: We evaluated 10 non-diabetic cancer patients the day after radical abdominal surgery combined with intra-operative radiation therapy. In each patient, a 24-hr period of IIT, aimed at tight euglycemic control, was preceded, or followed, by a 24-hr period of conventional insulin treatment (CIT) (control regimen). Insulin was administered for 24 hours, during total parenteral nutrition, at a dosage to maintain a moderate hyperglycemia in CIT, and normoglycemic blood glucose levels in IIT (9.3\ub10.5 vs 6.5\ub10.3 mmol/L respectively, P<0.001; coefficient of variation, 9.7\ub11.4 and 10.5\ub11.1%, P = 0.43). No hypoglycemia (i.e., blood glucose < 3.9 mmol/L) was observed in any of the patients. Insulin treatments were performed on the first and second day after surgery, in randomized order, according to a crossover experimental design. Plasma concentrations of thiobarbituric acid reactive substances (TBARS) and erythrocyte glutathione synthesis rates (EGSR), measured by primed-constant infusion of L-[2H2]cysteine, were assessed at the end of each 24-hr period of either IIT or CIT. RESULTS: Compared to CIT, IIT was associated with higher EGSR (2.70\ub10.51 versus 1.18\ub10.29 mmol/L/day, p = 0.01) and lower (p = 0.04) plasma TBARS concentrations (2.2\ub10.2 versus 2.9\ub10.4 nmol/L). CONCLUSIONS: In patients developing stress hyperglycemia after major surgery, IIT, in absence of hypoglycemia, stimulates erythrocyte glutathione synthesis, while decreasing oxidative stress

Upper limits on the strength of periodic gravitational waves from PSR J1939+2134

The first science run of the LIGO and GEO gravitational wave detectors presented the opportunity to test methods of searching for gravitational waves from known pulsars. Here we present new direct upper limits on the strength of waves from the pulsar PSR J1939+2134 using two independent analysis methods, one in the frequency domain using frequentist statistics and one in the time domain using Bayesian inference. Both methods show that the strain amplitude at Earth from this pulsar is less than a few times $10^{-22}$.Comment: 7 pages, 1 figure, to appear in the Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 200

Improving the sensitivity to gravitational-wave sources by modifying the input-output optics of advanced interferometers

We study frequency dependent (FD) input-output schemes for signal-recycling interferometers, the baseline design of Advanced LIGO and the current configuration of GEO 600. Complementary to a recent proposal by Harms et al. to use FD input squeezing and ordinary homodyne detection, we explore a scheme which uses ordinary squeezed vacuum, but FD readout. Both schemes, which are sub-optimal among all possible input-output schemes, provide a global noise suppression by the power squeeze factor, while being realizable by using detuned Fabry-Perot cavities as input/output filters. At high frequencies, the two schemes are shown to be equivalent, while at low frequencies our scheme gives better performance than that of Harms et al., and is nearly fully optimal. We then study the sensitivity improvement achievable by these schemes in Advanced LIGO era (with 30-m filter cavities and current estimates of filter-mirror losses and thermal noise), for neutron star binary inspirals, and for narrowband GW sources such as low-mass X-ray binaries and known radio pulsars. Optical losses are shown to be a major obstacle for the actual implementation of these techniques in Advanced LIGO. On time scales of third-generation interferometers, like EURO/LIGO-III (~2012), with kilometer-scale filter cavities, a signal-recycling interferometer with the FD readout scheme explored in this paper can have performances comparable to existing proposals. [abridged]Comment: Figs. 9 and 12 corrected; Appendix added for narrowband data analysi

Search for gravitational wave bursts in LIGO's third science run

We report on a search for gravitational wave bursts in data from the three LIGO interferometric detectors during their third science run. The search targets subsecond bursts in the frequency range 100-1100 Hz for which no waveform model is assumed, and has a sensitivity in terms of the root-sum-square (rss) strain amplitude of hrss ~ 10^{-20} / sqrt(Hz). No gravitational wave signals were detected in the 8 days of analyzed data.Comment: 12 pages, 6 figures. Amaldi-6 conference proceedings to be published in Classical and Quantum Gravit

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure