scholarly journals Integration of large scale knowledge bases using probabilistic graphical models

2014 ◽  
Author(s):  
Arnab Kumar Dutta
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Probabilistic Graphical Models ◽  
Knowledge Bases

scholarly journals Most Probable Explanations for Probabilistic Database Queries

2017 ◽  
Author(s):  
İsmail İlkan Ceylan ◽  
Stefan Borgwardt ◽  
Thomas Lukasiewicz
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Complexity Analysis ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Query Languages ◽  
Query Evaluation ◽  
Database Queries ◽  
Probabilistic Database ◽  
Probabilistic Query

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

scholarly journals Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

2021 ◽  
Author(s):  
Arjun P. Athreya ◽  
Tanja Brückl ◽  
Elisabeth B. Binder ◽  
A. John Rush ◽  
Joanna Biernacka ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Graphical Models ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Probabilistic Graphical Models ◽  
Sociodemographic Factors ◽  
Response To Therapy ◽  
Antidepressant Response ◽  
Symptom Profiles ◽  
Poor Outcomes ◽  
Multiple Drugs

AbstractHeterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Extended Probability Trees for Probabilistic Graphical Models

2014 ◽  
pp. 113-128
Author(s):  
Andrés Cano ◽  
Manuel Gómez-Olmedo ◽  
Serafín Moral ◽  
Cora B. Pérez-Ariza
Keyword(s):  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Probability Trees

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

scholarly journals Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

2017 ◽  
Author(s):  
Stefano Beretta ◽  
Mauro Castelli ◽  
Ivo Gonçalves ◽  
Ivan Merelli ◽  
Daniele Ramazzotti
Keyword(s):  
Breast Cancer ◽  
Graphical Models ◽  
Protein Interactions ◽  
Large Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Cancer Data ◽  
Correlation Networks ◽  
Model Complex ◽  
Cancer Networks

AbstractGene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

scholarly journals Financial networks as directed cyclic graphs - draft

2014 ◽  
Vol 11 (4) ◽  
pp. 520-529 ◽  
Author(s):  
Alexander Denev
Keyword(s):  
Computer Science ◽  
Graphical Models ◽  
Network Theory ◽  
Probabilistic Graphical Models ◽  
Financial Networks ◽  
New Approach ◽  
Global Properties ◽  
Cyclic Graphs

Financial networks’ study and understanding has become extremely important since the global financial meltdown in 2007-2009 when the inter-connectedness of institutions has surfaced as one of the major culprits for the magnitude of the distress. This paper aims at providing a new approach to describe and better understand the networks of institutions and their global properties. It is based on Directed Cyclic Graphs - a subset of Probabilistic Graphical Models which have already found use in other domains such as physics and computer science. The paper draws some parallels and contrasts with other studies in the field of Network Theory. It then concludes with a stylized example.

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