Article
Mathematical & Computational Biology
Ravi Goyal, Victor De Gruttola
Summary: A Bayesian approach is proposed for network model selection within the congruence class models (CCMs), which encompass various common network models. This method is effective in selecting models consistent with observed network generative mechanisms and can be applied to choose mechanisms for medical care networks, supporting heterogeneity in sociality.
STATISTICS IN MEDICINE
(2021)
Article
Psychology, Biological
Matteo Lisi, Gianluigi Mongillo, Georgia Milne, Tessa Dekker, Andrei Gorea
Summary: The study found that human confidence judgments tend to follow discrete confidence levels rather than Bayesian probabilities. While humans can express confidence about uncertain events, they do not fully adhere to the ideal Bayesian strategy. By developing a dual-decision task, researchers suggest that confidence judgments may be based on point estimates of relevant variables.
NATURE HUMAN BEHAVIOUR
(2021)
Article
Environmental Sciences
Maria Fernanda Morales Oreamuno, Sergey Oladyshkin, Wolfgang Nowak
Summary: Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework for comparing competing models using Bayesian model evidence (BME). However, BME-based analysis has limitations in accounting for a model's predictive performance after calibration and in comparing models using different calibration subsets. To address these limitations, we propose augmenting BMS and BMJ analyses with information-theoretic measures such as expected log-predictive density (ELPD), relative entropy (RE), and information entropy (IE). We demonstrate how these measures, alongside BME, enhance the understanding of the Bayesian updating process and enable objective model comparison using different calibration datasets.
WATER RESOURCES RESEARCH
(2023)
Article
Meteorology & Atmospheric Sciences
Robert Taggart
Summary: This article introduces a method for comparing point forecasts in a region of interest, emphasizing the importance of performance in the selected region of the variable's range, providing a natural interpretation rooted in optimal decision theory.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Biochemical Research Methods
Jacob Williams, Shuangshuang Xu, Marco A. R. Ferreira
Summary: In this study, a novel Bayesian variable selection method based on nonlocal priors is proposed for genome-wide association studies. The method, called BGWAS, effectively reduces false positive rates while maintaining the ability to detect true positive SNPs. It achieves this through a two-step process of screening and model selection.
BMC BIOINFORMATICS
(2023)
Article
Chemistry, Medicinal
Chao Yang, Yingkai Zhang
Summary: In this study, the robustness and applicability of machine-learning scoring functions were further improved by expanding the training set, developing meaningful features, using a linear empirical scoring function as the baseline, and applying extreme gradient boosting (XGBoost) with Delta-machine learning. The new scoring function demonstrated superior performance in scoring and ranking in various structure types and showed reliability and robustness in virtual screening applications.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Review
Biochemical Research Methods
Stephan Struckmann, Mathias Ernst, Sarah Fischer, Nancy Mah, Georg Fuellen, Steffen Moeller
Summary: Researchers are interested in finding new applications for known compounds by analyzing transcriptomics of biological samples from disease contexts. They found that matching a drug effect to the effect of the same drug at another concentration or in another cell line is a well-defined, reproducible challenge. By combining different similarity scores and heuristics, they were able to reduce the number of genes in the model and improve prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Engineering, Civil
Jasper A. Vrugt, Debora Y. de Oliveira, Gerrit Schoups, Cees G. H. Diks
Summary: This paper focuses on the formulation of an adequate likelihood function in Bayesian epistemology for uncertainty quantification of hydrologic models. It introduces distribution-adaptive likelihood functions, presents a revised implementation called GL+ function and a further generalization called UL function. It also discusses the use of proper scoring rules to evaluate and compare likelihood functions. The results show that GL+ function outperforms the GL function and the treatment of nuisance variables has a significant impact on the performance of the functions.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Electrical & Electronic
Koichi Iwai, Masanori Akiyoshi, Tomoki Hamagami
Summary: Credit scoring model is a risk management tool used to assess the credit worthiness of customer borrowers, traditionally built using logit model or decision tree algorithm, recently integrated with machine learning algorithms. The challenge it faces is the domain adaptation of customer borrowers, for which there is currently no appropriate transfer learning method available.
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
(2021)
Article
Biochemical Research Methods
Jacob Williams, Marco A. R. Ferreira, Tieming Ji
Summary: The study presents the use of the Bayesian Iterative Conditional Stochastic Search (BICOSS) method for genome wide association studies, which effectively controls false discovery rate and increases recall of variants with small and medium effect sizes. Two real world applications also demonstrate the utility and flexibility of BICOSS.
BMC BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Bjarne Grimstad, Mathilde Hotvedt, Anders T. Sandnes, Odd Kolbjornsen, Lars S. Imsland
Summary: Recent works have shown promising results in using machine learning for modeling flow rates in oil and gas wells. This paper introduces a probabilistic virtual flow meter based on Bayesian neural networks, which helps to describe uncertainty in the model and measurements using variational inference. The research findings suggest the need for alternative strategies to enhance the robustness of data-driven virtual flow meters.
APPLIED SOFT COMPUTING
(2021)
Article
Geosciences, Multidisciplinary
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, Oskar A. Landgren
Summary: We propose a framework for evaluating multi-model ensembles using common EOFs, which can extract salient spatio-temporal covariance structures from large climate data volumes. This framework provides weights for each model based on common principal components, allowing for objective evaluation of ensembles. Our analysis shows that CMIP6 simulations outperform CMIP5 in reproducing the seasonal cycle and interannual variability of temperature, precipitation, and pressure over the Nordic countries. We also find that CMIP ensembles are consistent with observed trends, and the leading common EOF principal component follows a roughly normal distribution for aggregated statistics.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Computer Science, Information Systems
Xianjie Guo, Kui Yu, Fuyuan Cao, Peipei Li, Hao Wang
Summary: Causal feature selection has gained much attention in recent years due to its improved robustness compared to traditional feature selection methods. However, existing algorithms that rely on conditional independence tests often encounter errors in practice, leading to degraded performance. In this paper, we propose an Error-Aware Markov Blanket learning algorithm with novel subroutines to address this issue, achieving better performance compared to state-of-the-art causal feature selection algorithms and traditional feature selection methods.
INFORMATION SCIENCES
(2022)
Article
Cardiac & Cardiovascular Systems
Edward Y. Chan, Farshad Amirkhosravi, Duc T. Nguyen, Ray K. Chihara, Edward A. Graviss, Min P. Kim
Summary: In octogenarians with pathologic stage I lung cancer, lobectomy provides better 5-year survival compared with sublobar resection regardless of the age at surgical procedure.
ANNALS OF THORACIC SURGERY
(2022)
Article
Engineering, Civil
Sharvil Alex Faroz, Siddhartha Ghosh
Summary: This paper introduces a method to estimate the corrosion rate of reinforced concrete structures through instrument calibration using probabilistic measurement error models within a Bayesian framework and hyper-robust calibration approach. The proposed approach is demonstrated for a linear polarisation resistance instrument and found to be suitable for the study case, as well as general enough to be applied to other NDT instruments.
Article
Genetics & Heredity
Wenjian Bi, Guolian Kang, Yanlong Zhao, Yuehua Cui, Song Yan, Yun Li, Cheng Cheng, Stanley B. Pounds, Michael J. Borowitz, Mary V. Relling, Jun J. Yang, Zhifa Liu, Ching-Hon Pui, Stephen P. Hunger, Christine M. Hartford, Wing Leung, Ji-Feng Zhang
ANNALS OF HUMAN GENETICS
(2015)
Meeting Abstract
Biochemical Research Methods
Iwona Pawlikowska, Zhifa Liu, Lei Shi, Tong Lin, Tanja Gruber, Giles Robinson, Arzu Onar-Thomas, Stan Pounds
BMC BIOINFORMATICS
(2015)
Article
Dermatology
Charles Lu, Jinghui Zhang, Panduka Nagahawatte, John Easton, Seungjae Lee, Zhifa Liu, Li Ding, Matthew A. Wyczalkowski, Marcus Valentine, Fariba Navid, Heather Mulder, Ruth G. Tatevossian, James Dalton, James Davenport, Zhirong Yin, Michael Edmonson, Michael Rusch, Gang Wu, Yongjin Li, Matthew Parker, Erin Hedlund, Sheila Shurtleff, Susana Raimondi, Vadodaria Bhavin, Yergeau Donald, Elaine R. Mardis, Richard K. Wilson, William E. Evans, David W. Ellison, Stanley Pounds, Michael Dyer, James R. Downing, Alberto Pappo, Armita Bahrami
JOURNAL OF INVESTIGATIVE DERMATOLOGY
(2015)
Article
Multidisciplinary Sciences
Emilia M. Pinto, Xiang Chen, John Easton, David Finkelstein, Zhifa Liu, Stanley Pounds, Carlos Rodriguez-Galindo, Troy C. Lund, Elaine R. Mardis, Richard K. Wilson, Kristy Boggs, Donald Yergeau, Jinjun Cheng, Heather L. Mulder, Jayanthi Manne, Jesse Jenkins, Maria J. Mastellaro, Bonald C. Figueiredo, Michael A. Dyer, Alberto Pappo, Jinghui Zhang, James R. Downing, Raul C. Ribeiro, Gerard P. Zambetti
NATURE COMMUNICATIONS
(2015)
Article
Oncology
Emilia Modolo Pinto, Carlos Rodriguez-Galindo, John Kim Choi, Stanley Pounds, Zhifa Liu, Geoffrey Neale, David Finkelstein, John M. Hicks, Alberto S. Pappo, Bonald C. Figueiredo, Raul C. Ribeiro, Gerard P. Zambetti
CLINICAL CANCER RESEARCH
(2016)
Article
Biochemical Research Methods
Stan Pounds, Cheng Cheng, Shaoyu Li, Zhifa Liu, Jinghui Zhang, Charles Mullighan
Article
Biochemical Research Methods
Iwona Pawlikowska, Gang Wu, Michael Edmonson, Zhifa Liu, Tanja Gruber, Jinghui Zhang, Stan Pounds
Meeting Abstract
Biochemical Research Methods
Zhifa Liu
BMC BIOINFORMATICS
(2013)
Meeting Abstract
Biochemical Research Methods
Nisrine Enyinda, Zhifa Liu, Areg Negatu, Stan Pounds
BMC BIOINFORMATICS
(2013)
Meeting Abstract
Biochemical Research Methods
Yuan Tan, Zhifa Liu
BMC BIOINFORMATICS
(2013)
Article
Biochemical Research Methods
Zhifa Liu, Stan Pounds
BMC BIOINFORMATICS
(2014)
Article
Genetics & Heredity
Xiaobo Guo, Zhifa Liu, Xueqin Wang, Heping Zhang
GENETIC EPIDEMIOLOGY
(2013)
Article
Multidisciplinary Sciences
Zhifa Liu, Xiaobo Guo, Yuan Jiang, Heping Zhang
SCIENTIFIC WORLD JOURNAL
(2013)