82 research outputs found
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 eâ”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 diâ”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
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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 eâ”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 âD-BOARDâ 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%â85% 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 .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
. 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
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