24 papers · ranked by Valyu relevance
Edoardo M Airoldi, William Noble
A probabilistic graphical model defines a family of probability distributions that can be represented in terms of a graph. Nodes in the graph correspond to random variables; its structure translates into statistical dependencies (among such variables) that drive the computation of joint, conditional, and marginal…
Marco Scutari, Radhakrishnan Nagarajan
Objective Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction…
David Heckerman
In 1987, Eric Horvitz, Greg Cooper, and I visited I.J. Good at Virginia Polytechnic and State University. The three of us were at a conference in Washington DC and made the short drive to see him. The primary reason we wanted to see him was not because he worked with Alan Turing to help win WWII by decoding encrypted…
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…
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…
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…
Yang Ni, Su Chen, Zeya Wang
Survey questionnaires are commonly used by psychologists and social scientists to measure various latent traits of study subjects. Various causal inference methods such as the potential outcome framework and structural equation models have been used to infer causal effects. However, the majority of these methods assume…
Nanny Wermuth, D. R. Cox
We describe how graphical Markov models emerged in the last 40 years, based on three essential concepts that had been developed independently more than a century ago. Sequences of joint or single regressions and their regression graphs are singled out as being the subclass that is best suited for analyzing longitudinal…
Zeyuan Song, Sophia Gunn, Stefano Monti, Gina Marie Peloso + 3 more
Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data…
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…
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…
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…
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
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…
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…
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…
Marco Bertolini, Linlin Zhao, Djork-Arné Clevert, Floriane Montanari
The field of explainable AI applied to molecular property prediction models has often been reduced to deriving atomic contributions. This has impaired the interpretability of such models, as chemists rather think in terms of larger, chemically meaningful structures, which often do not simply reduce to the sum of their…
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
Authors not listed
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…
Authors not listed
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…
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…
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter + 1 more
There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested…