scholarly journals Probabilistic graphical models for climate data analysis

2011 ◽  
Author(s):  
Arindam Banerjee
Keyword(s):  
Data Analysis ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Climate Data

scholarly journals Setting the Port Planning Parameters In Container Terminals through Bayesian Networks

2015 ◽  
Vol 27 (5) ◽  
pp. 395-403 ◽  
Author(s):  
Tomás Rodríguez García ◽  
Nicoletta González Cancelas ◽  
Francisco Soler-Flores
Keyword(s):  
Data Analysis ◽  
Bayesian Networks ◽  
Graphical Models ◽  
Simulation Models ◽  
Probabilistic Graphical Models ◽  
Resource Planning ◽  
Container Terminals ◽  
Correct Prediction ◽  
Port Planning ◽  
Transport Logistics

The correct prediction in the transport logistics has vital importance in the adequate means and resource planning and in their optimisation. Up to this date, port planning studies were based mainly on empirical, analytical or simulation models. This paper deals with the possible use of Bayesian networks in port planning. The methodology indicates the work scenario and how the network was built. The network was afterwards used in container terminals planning, with the support provided by the tools of the Elvira code. The main variables were defined and virtual scenarios inferences were realised in order to carry out the analysis of the container terminals scenarios through probabilistic graphical models. Having performed the data analysis on the different terminals and on the considered variables (berth, area, TEU, crane number), the results show the possible relationships between them. Finally, the conclusions show the obtained values on each considered scenario.

scholarly journals Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

2020 ◽  
Vol 144 ◽  
pp. 107500 ◽  
Author(s):  
Ming Bo Cai ◽  
Michael Shvartsman ◽  
Anqi Wu ◽  
Hejia Zhang ◽  
Xia Zhu
Keyword(s):  
Data Analysis ◽  
Graphical Models ◽  
Probabilistic Graphical Models ◽  
Fmri Data ◽  
Fmri Data 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 ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

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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