Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

Background\ud Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.\ud \ud Methods\ud We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.\ud \ud Results\ud The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.\ud \ud Conclusions\ud Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters

Random Forests Based Rule Learning And Feature Elimination

Much research combines data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. We propose an efficient approach, combining rule extraction and feature elimination, based on 1-norm regularized random forests. This approach simultaneously extracts a small number of rules generated by random forests and selects important features. To evaluate this approach, we have applied it to several drug activity prediction data sets, microarray data sets, a seacoast chemical sensors data set, a Stockori flowering time data set, and three data sets from the UCI repository. This approach performs well compared to state-of-the-art prediction algorithms like random forests in terms of predictive performance and generates only a small number of decision rules. Some of the decision rules extracted are significant in solving the problem being studied. It demonstrates high potential in terms of prediction performance and interpretation on studying real applications

BAYESIAN FRAMEWORKS FOR PARSIMONIOUS MODELING OF MOLECULAR CANCER DATA

In this era of precision medicine, clinicians and researchers critically need the assistance of computational models that can accurately predict various clinical events and outcomes (e.g,, diagnosis of disease, determining the stage of the disease, or molecular subtyping). Typically, statistics and machine learning are applied to ‘omic’ datasets, yielding computational models that can be used for prediction. In cancer research there is still a critical need for computational models that have high classification performance but are also parsimonious in the number of variables they use. Some models are very good at performing their intended classification task, but are too complex for human researchers and clinicians to understand, due to the large number of variables they use. In contrast, some models are specifically built with a small number of variables, but may lack excellent predictive performance. This dissertation proposes a novel framework, called Junction to Knowledge (J2K), for the construction of parsimonious computational models. The J2K framework consists of four steps: filtering (discretization and variable selection), Bayesian network generation, Junction tree generation, and clique evaluation. The outcome of applying J2K to a particular dataset is a parsimonious Bayesian network model with high predictive performance, but also that is composed of a small number of variables. Not only does J2K find parsimonious gene cliques, but also provides the ability to create multi-omic models that can further improve the classification performance. These multi-omic models have the potential to accelerate biomedical discovery, followed by translation of their results into clinical practice

Implementación de los modelos gráficos probabilísticos bayesianos en la ayuda al manejo clínico de la bronquiolitis aguda del lactante

Fecha de Lectura de Tesis: 6 de Abril de 2018.La Bronquiolitis Aguda (BA) del lactante supone el mayor motivo de ingreso hospitalario de los Servicios de Pediatría en general y una de las causas de mayor ocupación, consumo de recursos y estancias hospitalarias. En torno al 3-5% de la BAs precisará ingreso en un hospital, el 6-16% de los ingresados acabará en cuidados intensivos y un 3-8% de los ingresos sufrirá episodios de apnea. El objetivo de la investigación es la confección y desarrollo de un modelo gráfico probabilístico Naïve Bayes (NB) selectivo, utilizado como herramienta de epidemiología clínica, para la predicción de la evolución grave y de la aparición de apneas en la BA del lactante. La metodología se basa en el estudio de los factores de riesgo de evolución grave y de aparición de apneas durante el ingreso en la BA sobre la experiencia de un hospital de tercera referencia, la elaboración de una red probabilística NB mediante OpenMarkov (modelo gráfico probabilístico) y la implementación del modelo tras evaluación de la sensibilidad y especificidad de sus predicciones para conocer prospectivamente su validez y fiabilidad comparado con un modelo de regresión logística. Por ello se plantearon unos objetivos específicos, como el análisis epidemiológico general de una amplia serie de casos de BA durante las epidemias desde octubre-2010 hasta marzo-2015, para conocer su realidad en un espacio-tiempo concreto. Además, para estimar la incidencia de apneas en pacientes hospitalizados por BA y estudiar los factores de riesgo relacionados con su aparición y también conocer la incidencia de ingresos en UCIP para VM y estudiar los factores asociados a mala evolución, centrado este aspecto sobre el agente etiológico primordial en casos graves de BA: el VRS. En cada caso se utilizó el procedimiento de regresión logística (RL) y se estimó su capacidad de predicción

Automated Detection of Anomalous Patterns in Validation Scores for Protein X-Ray Structure Models

Structural bioinformatics is a subdomain of data mining focused on identifying structural patterns relevant to functional attributes in repositories of biological macromolecular structure models. This research focused on structures determined via x-ray crystallography and deposited in the Protein Data Bank (PDB). Protein structures deposited in the PDB are products of experimental processes, and only approximately model physical reality. Structural biologists address accuracy and precision concerns via community-enforced consensus standards of accepted practice for proper building, refinement, and validation of models. Validation scores are quantitative partial indicators of the likelihood that a model contains serious systematic errors. The PDB recently convened a panel of experts, which placed renewed emphasis on troubling anomalies among deposited structure models. This study set out to detect such anomalies. I hypothesized that community consensus standards would be evident in patterns of validation scores, and deviations from those standards would appear as unusual combinations of validation scores. Validation attributes were extracted from PDB entry headers and multiple software tools (e.g., WhatCheck, SFCheck, and MolProbity). Independent component analysis (ICA) was used for attribute transformation to increase contrast between inliers and outliers. Unusual patterns were sought in regions of locally low density in the space of validation score profiles, using a novel standardization of Local Outlier Factor (LOF) scores. Validation score profiles associated with the most extreme outlier scores were demonstrably anomalous according to domain theory. Among these were documented fabrications, possible annotation errors, and complications in the underlying experimental data. Analysis of deep inliers revealed promising support for the hypothesized link between consensus standard practices and common validation score values. Unfortunately, with numerical anomaly detection methods that operate simultaneously on numerous continuous-valued attributes, it is often quite difficult to know why a case gets a particular outlier score. Therefore, I hypothesized that IF-THEN rules could be used to post-process outlier scores to make them comprehensible and explainable. Inductive rule extraction was performed using RIPPER. Results were mixed, but they represent a promising proof of concept. The methods explored are general and applicable beyond this problem. Indeed, they could be used to detect structural anomalies using physical attributes

LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES

Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature-mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. This dissertation presents a novel semi-automated framework, named Knowledge Enhanced Data Analysis (KEDA), which incorporates the following components: 1) literature mining of text; 2) classification modeling; and 3) pathway analysis. This framework aids researchers in assigning literature-mining-based prior knowledge values to genes and proteins associated with disease biology. It incorporates prior knowledge into the modeling of experimental datasets, enriching the development process with current findings from the scientific community. New knowledge is presented in the form of lists of known disease-specific biomarkers and their accompanying scores obtained through literature mining of millions of lung and breast cancer abstracts. These scores can subsequently be used as prior knowledge values in Bayesian modeling and pathway analysis. Ranked, newly discovered biomarker-disease-biofluid relationships which identify biomarker specificity across biofluids are presented. A novel method of identifying biomarker relationships is discussed that examines the attributes from the best-performing models. Pathway analysis results from the addition of prior information, ultimately lead to more robust evidence for pathway involvement in diseases of interest based on statistically significant standard measures of impact factor and p-values. The outcome of implementing the KEDA framework is enhanced modeling and pathway analysis findings. Enhanced knowledge discovery analysis leads to new disease-specific entities and relationships that otherwise would not have been identified. Increased disease understanding, as well as identification of biomarkers for disease diagnosis, treatment, or therapy targets should ultimately lead to validation and clinical implementation

Bayesian rule learning for biomedical data mining

Motivation: Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially lead to the discovery of better, more discriminative markers. Data mining methods are routinely applied to such analyses of biomedical datasets generated from high-throughput ‘omic’ technologies applied to clinical samples from tissues or bodily fluids. Past work has demonstrated that rule models can be successfully applied to this problem, since they can produce understandable models that facilitate review of discriminative biomarkers by biomedical scientists. While many rule-based methods produce rules that make predictions under uncertainty, they typically do not quantify the uncertainty in the validity of the rule itself. This article describes an approach that uses a Bayesian score to evaluate rule models