Results 41 to 50 of about 584,257 (325)

Evaluation of a Bayesian inference network for ligand-based virtual screening [PDF]

, 2009
Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to
A Abdo, A Bender, AG Maldonado, AN Jain, AR Leach, AR Leach, Beining Chen, Christoph Mueller, CX Zhai, D Metzler, EJ Gardiner, EM Voorhees, G Salton, GW Bemis, H Eckert, H Turtle, J Bajorath, J Hert, J Hert, J-F Truchon, JA Grant, JD Holliday, JP Callan, JP Callan, JR Fischer, K Spärck Jones, K Spärck Jones, N Nikolova, P Prathipati, P Willett, P Willett, P Willett, P Willett, P Willett, Peter Willett, RC Glen, RD Brown, RP Sheridan, RP Sheridan, S Siegel, SJ Edgar, T Lengauer, T Strohman, TI Oprea, WR Greiff, X Chen   +45 more
core   +3 more sources

Bayesian utilitarianism and probability homogeneity [PDF]

Social Choice and Welfare, 2005
This paper investigates, within the axiomatic framework of Jeffrey-Bolker decision theory, two kinds of conditions and the relation between them: (1) The Utilitarian condition that social rankings of prospects be representable by an expected utility function that is a weighted sum of the expected utility functions representing individual rankings; and (
openaire   +3 more sources

Dynamic reliability prediction of bridge member based on Bayesian dynamic nonlinear model and monitored data

Advances in Mechanical Engineering, 2016
Bridge monitoring systems provide a huge number of stress data used for reliability prediction. In this article, the dynamic measure of structural stress over time is considered as a time series, and considering the limitation of the existing Bayesian ...
Xueping Fan, Yuefei Liu
doaj   +1 more source

Intuitionistc probability and the Bayesian objection to dogmatism [PDF]

Synthese, 2016
Given a few assumptions, the probability of a conjunction is raised, and the probability of its negation is lowered, by conditionalising upon one of the conjuncts. This simple result appears to bring Bayesian confirmation theory into tension with the prominent dogmatist view of perceptual justification—a tension often portrayed as a kind of ‘Bayesian ...
openaire   +3 more sources

Elucidating prognostic significance of purine metabolism in colorectal cancer through integrating data from transcriptomic, immunohistochemical, and single‐cell RNA sequencing analysis

Molecular Oncology, EarlyView.
Low expression of five purine metabolism‐related genes (ADSL, APRT, ADCY3, NME3, NME6) was correlated with poor survival in colorectal cancer. Immunohistochemistry analysis showed that low NME3 (early stage) and low ADSL/NME6 (late stage) levels were associated with high risk.
Sungyeon Kim, Myunghee Kang, Soyeon Jeong, Jisup Kim, Kyoung Oh Kim, Won‐Suk Lee, Jeong‐Heum Baek, Jung Ho Kim, Seungyoon Nam   +8 more
wiley   +1 more source

How to Train Novices in Bayesian Reasoning

Mathematics, 2022
Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations.
Theresa Büchter, Andreas Eichler, Nicole Steib, Karin Binder, Katharina Böcherer-Linder, Stefan Krauss, Markus Vogel   +6 more
doaj   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Frequentist and Bayesian measures of confidence via multiscale bootstrap for testing three regions

, 2008
A new computation method of frequentist $p$-values and Bayesian posterior probabilities based on the bootstrap probability is discussed for the multivariate normal model with unknown expectation parameter vector.
B. Efron, B. Efron, B. Efron, E.L. Lehmann, G.S. Datta, H. Akaike, H. Shimodaira, H. Shimodaira, H. Shimodaira, H.W. Peers, Hidetoshi Shimodaira, J. Felsenstein, J.P. DuPreez, M.D. Perlman, M.D. Perlman, R. Tibshirani   +15 more
core   +2 more sources

Stage‐Dependent Inhibitory Connectivity in Striatal‐Motor Circuit in Huntington's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Elucidating dysfunctional connectivity patterns among key brain regions in Huntington's disease (HD) underlying progression may have implications for developing treatment and therapeutic evaluation. Objective Explore the relationship between abnormal spontaneous resting‐state activity and atrophy in HD‐specific brain regions and ...
Yinghua Jing, Imis Dogan, Rena Theda Overbeck, Kathrin Reetz, Sandro Romanzetti   +4 more
wiley   +1 more source

An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications

Entropy, 2016
In this work, we propose a new approach of deriving the bounds between entropy and error from a joint distribution through an optimization means. The specific case study is given on binary classifications.
Bao-Gang Hu, Hong-Jie Xing
doaj   +1 more source