Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Accesos vasculares para hemodiálisis, cuál es el más costo efectivo?

Marco conceptual: La enfermedad renal crónica es un serio problema de salud pública en nuestro país por la gran cantidad de recursos económicos que requiere su atención. La hemodiálisis es el tratamiento más usado en nuestro medio; el acceso vascular y sus complicaciones derivadas son el principal aspecto que incrementa los costos de atención en éstos pacientes. Materiales y métodos: Se realizó un estudio económico de los accesos vasculares en pacientes incidentes de hemodiálisis en el año 2012 en la agencia RTS-Fundación Cardio Infantil. Se estableció el costo de creación y mantenimiento del acceso con catéter central, fístula arteriovenosa nativa, fístula arteriovenosa con injerto; y el costo de atención de las complicaciones para cada acceso. Se determinó la probabilidad de ocurrencia de complicaciones. Mediante un árbol de decisiones se trazó el comportamiento de cada acceso en un período de 5 años. Se establecieron los años de vida ajustados por calidad (QALY) en cada acceso y el costo para cada uno de éstos QALY. Resultados: de 36 pacientes incidentes de hemodiálisis en 2012 el 100% inició con catéter central, 16 pacientes cambiaron a fístula arteriovenosa nativa, 1 a fístula arteriovenosa con injerto que posteriormente pasó a CAPD, 15 continuaron su acceso con catéter y 4 pacientes fallecieron. En 5 años se obtuvieron 2,36 QALY para los pacientes con catéter central que costarían $ 24.813.036,39/QALY y 2,535 QALY para los pacientes con fístula nativa que costarían $ 6.634.870,64/QALY. Conclusiones: el presente estudio muestra que el acceso vascular mediante fístula arteriovenosa nativa es el más costo-efectivo que mediante catéter

Análisis fármaco económico de dos tratamientos para pacientes vih positivos con carga viral indetectable

Los tratamientos para aumentar los niveles de cúmulo de diferenciación 4 – CD4 en personas que padecen la enfermedad ocasionada por el Virus de la Inmunodeficiencia Humana (VIH), son importantes tanto para el mejoramiento del bienestar de los pacientes, como para el buen funcionamiento de las instituciones de salud. La presente investigación compara la intervención farmacológica de dos líneas de tratamiento, Lamivudina, Zidovudina, Efavirenz contra Efavirenz, Emtricitabina, Disoproxilo de Tenofovir que se encuentran en la recomendación de esquema de primera línea según la Guía Práctica Clínica (2014). Se evaluó el efecto costo-efectivo de estos dos tratamientos basado en el aumento de los niveles de CD4 a lo largo de tres tiempos diferentes (inicial, 6 y 12 meses) y los costos de los medicamentos de acuerdo a los precios en Colombia según el SISMED en el año 2014. Se realizó un análisis de varianza factorial con medidas repetidas, un árbol de decisiones y un análisis de costo-efectividad incremental (ACEI). Se obtuvo información de 546 pacientes, tanto hombres como mujeres, de la Institución Asistencia Científica de Alta Complejidad S.A.S de la ciudad de Bogotá. Se encontró que el esquema 1 (Lamivudina, Zidovudina, Efavirenz) fue considerado más efectivo y menos costoso que el tratamiento 2 (Efavirenz, Emtricitabina, Disoproxilo de Tenofovir), sin embargo no se evidenció una alta frecuencia de efectos adversos que pueda contribuir a la escogencia de un tratamiento u otro. De acuerdo a estos resultados la institución o los médicos tratantes tienen una alternativa farmacoeconómica para la toma de decisión del tratamiento a utilizar y así iniciar la terapia antirretroviral de pacientes que conviven con VHI con carga viral indetectable.

Análisis fármaco económico de dos tratamientos para pacientes VIH positivos con carga viral indetectable

Los tratamientos para aumentar los niveles de cúmulo de diferenciación 4 – CD4 en personas que padecen la enfermedad ocasionada por el Virus de la Inmunodeficiencia Humana (VIH), son importantes tanto para el mejoramiento del bienestar de los pacientes, como para el buen funcionamiento de las instituciones de salud. La presente investigación compara la intervención farmacológica de dos líneas de tratamiento, Lamivudina, Zidovudina, Efavirenz contra Efavirenz, Emtricitabina, Disoproxilo de Tenofovir que se encuentran en la recomendación de esquema de primera línea según la Guía Práctica Clínica (2014). Se evaluó el efecto costo-efectivo de estos dos tratamientos basado en el aumento de los niveles de CD4 a lo largo de tres tiempos diferentes (inicial, 6 y 12 meses) y los costos de los medicamentos de acuerdo a los precios en Colombia según el SISMED en el año 2014. Se realizó un análisis de varianza factorial con medidas repetidas, un árbol de decisiones y un análisis de costo-efectividad incremental (ACEI). Se obtuvo información de 546 pacientes, tanto hombres como mujeres, de la Institución Asistencia Científica de Alta Complejidad S.A.S de la ciudad de Bogotá. Se encontró que el esquema 1 (Lamivudina, Zidovudina, Efavirenz) fue considerado más efectivo y menos costoso que el tratamiento 2 (Efavirenz, Emtricitabina, Disoproxilo de Tenofovir), sin embargo no se evidenció una alta frecuencia de efectos adversos que pueda contribuir a la escogencia de un tratamiento u otro. De acuerdo a estos resultados la institución o los médicos tratantes tienen una alternativa farmacoeconómica para la toma de decisión del tratamiento a utilizar y así iniciar la terapia antirretroviral de pacientes que conviven con VHI con carga viral indetectable.

An evaluation of Bayesian designs for dose-escalation studies in healthy volunteers

In this paper, Bayesian decision procedures previously proposed for dose-escalation studies in healthy volunteers are reviewed and evaluated. Modifications are made to the expression of the prior distribution in order to make the procedure simpler to implement and a more relevant criterion for optimality is introduced. The results of an extensive simulation exercise to establish the proper-ties of the procedure and to aid choice between designs are summarized, and the way in which readers can use simulation to choose a design for their own trials is described. The influence of the value of the within-subject correlation on the procedure is investigated and the use of a simple prior to reflect uncertainty about the correlation is explored. Copyright (c) 2005 John Wiley & Sons, Ltd.

Decision-theoretic designs for phase II clinical trials allowing for competing studies

This article describes an approach to optimal design of phase II clinical trials using Bayesian decision theory. The method proposed extends that suggested by Stallard (1998, Biometrics54, 279–294) in which designs were obtained to maximize a gain function including the cost of drug development and the benefit from a successful therapy. Here, the approach is extended by the consideration of other potential therapies, the development of which is competing for the same limited resources. The resulting optimal designs are shown to have frequentist properties much more similar to those traditionally used in phase II trials.

Implementation of a Bayesian design in a dose-escalation study of an experimental agent in healthy volunteers

Bayesian decision procedures have recently been developed for dose escalation in phase I clinical trials concerning pharmacokinetic responses observed in healthy volunteers. This article describes how that general methodology was extended and evaluated for implementation in a specific phase I trial of a novel compound. At the time of writing, the study is ongoing, and it will be some time before the sponsor will wish to put the results into the public domain. This article is an account of how the study was designed in a way that should prove to be safe, accurate, and efficient whatever the true nature of the compound. The study involves the observation of two pharmacokinetic endpoints relating to the plasma concentration of the compound itself and of a metabolite as well as a safety endpoint relating to the occurrence of adverse events. Construction of the design and its evaluation via simulation are presented.

A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints

Bayesian decision procedures have already been proposed for and implemented in Phase I dose-escalation studies in healthy volunteers. The procedures have been based on pharmacokinetic responses reflecting the concentration of the drug in blood plasma and are conducted to learn about the dose-response relationship while avoiding excessive concentrations. However, in many dose-escalation studies, pharmacodynamic endpoints such as heart rate or blood pressure are observed, and it is these that should be used to control dose-escalation. These endpoints introduce additional complexity into the modeling of the problem relative to pharmacokinetic responses. Firstly, there are responses available following placebo administrations. Secondly, the pharmacodynamic responses are related directly to measurable plasma concentrations, which in turn are related to dose. Motivated by experience of data from a real study conducted in a conventional manner, this paper presents and evaluates a Bayesian procedure devised for the simultaneous monitoring of pharmacodynamic and pharmacokinetic responses. Account is also taken of the incidence of adverse events. Following logarithmic transformations, a linear model is used to relate dose to the pharmacokinetic endpoint and a quadratic model to relate the latter to the pharmacodynamic endpoint. A logistic model is used to relate the pharmacokinetic endpoint to the risk of an adverse event.

Increasing detection rate of user-to-root attacks using genetic algorithms

An extensive set of machine learning and pattern classification techniques trained and tested on KDD dataset failed in detecting most of the user-to-root attacks. This paper aims to provide an approach for mitigating negative aspects of the mentioned dataset, which led to low detection rates. Genetic algorithm is employed to implement rules for detecting various types of attacks. Rules are formed of the features of the dataset identified as the most important ones for each attack type. In this way we introduce high level of generality and thus achieve high detection rates, but also gain high reduction of the system training time. Thenceforth we re-check the decision of the user-to- root rules with the rules that detect other types of attacks. In this way we decrease the false-positive rate. The model was verified on KDD 99, demonstrating higher detection rates than those reported by the state- of-the-art while maintaining low false-positive rate.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.