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Search · four archives
21 papers · ranked by Valyu relevance
Oliver Richardson, Joseph Y. Halpern
We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how…
Shanbo Chu, Yong Jiang, Kewei Tu
Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence. In this paper, we propose a novel type of probabilistic models named latent dependency forest models (LDFMs). A LDFM models the dependencies between random variables with a forest structure that can change…
Abdul-Wahid Mohammed, Yang Xu, Ming Liu
Distributed reasoning in M2M leverages the expressive power of ontology to enable semantic interoperability between heterogeneous systems of connected devices. Ontology, however, lacks the built-in, principled support to effectively handle the uncertainty inherent in M2M application domains. Thus, efficient reasoning…
Oliver E. Richardson, Joseph Y. Halpern, Christopher De
Probabilistic dependency graphs (PDGs) are a flexible class of probabilistic graphical models, subsuming Bayesian Networks and Factor Graphs. They can also capture inconsistent beliefs, and provide a way of measuring the degree of this inconsistency. We present the first tractable inference algorithm for PDGs with…
Glauber De Bona, Fabio G. Cozman
There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian networks, while Relational Bayesian Networks (RBNs) employ…
Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick, Xiaoqian Yin + 1 more
'Yan V. Sun'] Abstract. A Relational Dependency Network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relational autocorrelations. We describe an approach for learning both the RDN's structure and its parameters, given an…
David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite + 1 more
'Robert Rounthwaite' 'Carl Kadie'] We describe a graphical representation of probabilistic relationships-an alternative to the Bayesian network-called a dependency network. Like a Bayesian network, a dependency network has a graph and a probability component. The graph component is a (cyclic) directed graph such that a…
Toyosi Ademujimi, Vittaldas Prabhu, Jongmyon Kim
Bayesian Network (BN) models are being successfully applied to improve fault diagnosis, which in turn can improve equipment uptime and customer service. Most of these BN models are essentially trained using quantitative data obtained from sensors. However, sensors may not be able to cover all faults and therefore such…
Yuanfang Ren, Ahmet Ay, Tamer Kahveci
Background Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths…
Antti Larjo, Harri Lähdesmäki
Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of…
Musfiqur Sazal, Kalai Mathee, Daniel Ruiz-Perez, Trevor Cickovski + 1 more
Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any…
Kaixian Yu, Zihan Cui, Xing Qiu, Jinfeng Zhang
Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past…
Grigoriy Gogoshin, Sergio Branciamore, Andrei S. Rodin
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for…
B. Schaeffer, V. Nicolas, F. Austerlitz, C. Larédo
Several classes of methods have been proposed for inferring the history of populations from genetic polymorphism data. As connectivity is a key factor to explain the structure of populations, several graph-based methods have been developed to this aim, using population genetics data. Here we propose an original method…
Grigoriy Gogoshin, Sergio Branciamore, Andrei S. Rodin
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology…
Qingyang Zhang, Xuan Shi
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately…
Zuguang Gu, Daniel Hübschmann
Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of unique dependencies that a parent brings to a package, and we…
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…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
Zachary J. Gale-Day, Laura Shub, Kangway V. Chuang, Michael J. Keiser
Message-passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the Protein-Graph Network (PGN) package, an open-source toolkit that constructs ligand-receptor…
Authors not listed
In the real world, many reversal phenomena occur—for example, cases in which a statement once regarded as false is later recognized as true. Upside-Down Logic is a framework designed to formalize such reversal phenomena as a logical system. It inverts the truth and falsity of propositions through contextual…