Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Abigail Sheerin, Giuseppe Vinci
The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic graphical models offer powerful tools for uncovering the dependence structures and…
Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson + 1 more
'Yongxin Chen'] We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular, an entropy regularized multi-marginal optimal transport is…
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger + 2 more
'John A. Miller' 'William S. York'] In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks…
Liying Chen, Satwik Acharyya, Chunyu Luo, Yang Ni + 1 more
Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad…
Edoardo M Airoldi, William Noble
Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses…
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger + 6 more
'John A. Miller' 'Khaled Rasheed' 'Krys J. Kochut' 'William S. York' 'René Ranzinger' 'Melody Porterfield'] Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of…
Malte Rørmose Damgaard, Rasmus Pedersen, Thomas Bak
Title: Summary Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by…
Hexuan Liu, Jimin Kim, Eli Shlizerman
We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate…
Simon Dirmeier, Niko Beerenwinkel
Genetic perturbation screening is an experimental method in biology to study cause and effect relationships between different biological entities. However, knocking out or knocking down genes is a highly error-prone process that complicates estimation of the effect sizes of the interventions. Here, we introduce a…
Wouter W. L. Nuijten, Dmitry Bagaev, Bert de Vries, Dawn E. Holmes
This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely represents probabilistic models as factor graphs. A notable feature of GraphPPL.jl is its model nesting capability, which facilitates the creation of modular graphical models and significantly…
Nikolas Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza
We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the Matthews correlation…
Yang Ni, Veerabhadran Baladandayuthapani, Marina Vannucci, Francesco C. Stingo
'Francesco C. Stingo'] Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the…
Roland R. Ramsahai
Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in…
Erika Banzato, Alberto Roverato, Alessandra Buja, Giovanna Boccuzzo
Background The use of graphical models in the multimorbidity context is increasing in popularity due to their intuitive visualization of the results. A comprehensive understanding of the model itself is essential for its effective utilization and optimal application. This article is a practical guide on the use of…
Neil Hallonquist
| 1 | Introduction | | 2 | | --- | --- | --- | --- | | | 1.1 | Random Graphs | 4 | | | 1.1.1 | Literature | 4 | | | 1.1.2 | Other Literature | 5 | | | 1.1.3 | Issues | 6 | | | 1.1.4 | Structure | 8 | | | 1.2 | Random Trees | 9 | | | 1.2.1 | Literature | 9 | | | 1.3 | Outline | 10 | | 2 | Random Graphs | | 12 | | | 2.1…
Simon Olsson, Frank Noé
Most current molecular dynamics simulation and analysis methods rely on the idea that the molecular system can be characterized by a single global state, e.g., a Markov State in a Markov State Model (MSM). In this approach, molecules can be extensively sampled and analyzed when they only possess a few metastable…
Simon Schwab, Ruth Harbord, Valerio Zerbi, Lloyd Elliott + 5 more
There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. However, directed relationships in networks are difficult to estimate, and only very few methods are available. Here, we consider a method for dynamic…
Arkaprava Roy, David B Dunson
Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence structure. This is particularly true when interest focuses on inferring the conditional independence graph. In this article, we propose a…
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu + 1 more
'Peter Battaglia'] Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs…
Ralf Strobl, Eva Grill, Ulrich Mansmann
Background Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
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Learning aqueous solubility remains a key challenge in drug development for improving oral bioavailability. Traditional data-driven solubility estimations using standard supervised models, however, can often suppress the information embedded in a molecule’s chemical properties and the intricate connectivity of its…
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Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
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Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…