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Application of Bayesian networks on large-scale biological data

Yi Liu, Jing-Dong J. Han
2010 Frontiers in Biology  
of BNs, which is important for the discovery of causal knowledge from large-scale biological datasets.  ...  Whole genome sequencing has made it possible to examine the behavior of all the genes in a genome by high-throughput experimental techniques and to pinpoint molecular interactions on a genome-wide scale  ...  Acknowledgements We thank the support from the China National Science  ... 
doi:10.1007/s11515-010-0023-8 fatcat:iiepdm5eknc7njtlb2aarj7yj4

Towards Automatic Inference of Task Hierarchies in Complex Systems

Haohui Mai, Chongnan Gao, Xuezheng Liu, Xi Wang, Geoffrey M. Voelker
2008 Hot Topics in System Dependability  
The algorithm first enumerates all connected subgraphs (with more than one node) from the causal graph, and then clusters these subgraphs into different patterns based on their similarity (described further  ...  On the contrary, Scalpel only tracks synchronization operations and uses heuristics to infer causal dependencies. Therefore, it is more lightweight but provides less precise results.  ... 
dblp:conf/hotdep/MaiGLWV08 fatcat:6xsixlo5eneidphm5v3lb33ipa

Scalable Causal Structure Learning: New Opportunities in Biomedicine [article]

Pulakesh Upadhyaya, Kai Zhang, Can Li, Xiaoqian Jiang, Yejin Kim
2021 arXiv   pre-print
We review prominent traditional, score-based and machine-learning based schemes for causal structure discovery, study some of their performance over some benchmark datasets, and discuss some of the applications  ...  This paper gives a practical tutorial on popular causal structure learning models with examples of real-world data to help healthcare audiences understand and apply them.  ...  Fast GES Improved and parallelized version of GES K2 Perform a greedy heuristic search for the parents of each node.  ... 
arXiv:2110.07785v1 fatcat:3dk2kfkvzjdqhazuenuhvg5f7e

A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus [article]

Xinpeng Shen, Sisi Ma, Prashanthi Vemuri, M. Regina Castro, Pedro J. Caraballo, Gyorgy J. Simon
2020 arXiv   pre-print
Results and conclusions: The proposed method improved over the existing methods by successfully incorporating study design considerations, was robust in face of unreliable EHR timestamps and inferred causal  ...  Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging  ...  Contents of this document are the sole responsibility of the authors and do not necessarily represent official views of the NIH.  ... 
arXiv:2011.05489v1 fatcat:xxtkjlko2jhfxesxhc3y2ah7xe

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Stefano Beretta, Mauro Castelli, Ivo Gonçalves, Ivan Merelli, Daniele Ramazzotti
2016 Proceedings of the 8th International Joint Conference on Computational Intelligence  
In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in  ...  inferring the structure of the Bayesian Network from breast cancer data.  ...  NPhard problem and, therefore, one will need to make use of heuristics to perform such inference (Parsons, 2011) .  ... 
doi:10.5220/0006064102170224 dblp:conf/ijcci/0001CGMR16 fatcat:yq24i2ow6fcezbjq6io7vqkkui

On Causal Inference for Data-free Structured Pruning [article]

Martin Ferianc, Anush Sankaran, Olivier Mastropietro, Ehsan Saboori, Quentin Cappart
2021 arXiv   pre-print
In this work, we approach this challenge from a causal inference perspective, and we propose a scoring mechanism to facilitate structured pruning of NNs.  ...  The approach is based on measuring mutual information under a maximum entropy perturbation, sequentially propagated through the NN.  ...  Lastly, we thank ITCI'22 reviewers for feedback that helped us to improve the paper.  ... 
arXiv:2112.10229v1 fatcat:q2wpysboxfeojhohabzy3ewxdu

Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks [article]

Stefano Beretta, Mauro Castelli, Ivo Goncalves, Ivan Merelli, Daniele Ramazzotti
2017 bioRxiv   pre-print
In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in  ...  inferring the structure of the Bayesian Network from breast cancer data.  ...  NPhard problem and, therefore, one will need to make use of heuristics to perform such inference (Parsons, 2011) .  ... 
doi:10.1101/115261 fatcat:yj7dsp27ybe43h2cge5hyafwq4

Guest editorial: special issue on causal discovery

Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu
2017 International Journal of Data Science and Analytics  
The paper "Weakening faithfulness: some heuristic causal discovery algorithms" studies to what extent two of the most well-known algorithms for causal learning rely on this assumption.  ...  Given the prevalence of high-dimensional data sets, it is essential to improve the scalability of causal learning algorithms to solve a wider range of problems.  ... 
doi:10.1007/s41060-016-0041-y dblp:journals/ijdsa/LiZBL17 fatcat:5vr33zggmbctxghz7tqcoj5lda

Causal Collaborative Filtering [article]

Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang
2021 arXiv   pre-print
Experiments are conducted on two types of real-world datasets – traditional and randomized trial data – and results show that our framework can improve the recommendation performance of many CF algorithms  ...  Many of the traditional CF algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data for matching, including memory-based methods such as user/item-based  ...  Overall, all heuristic rules have the potential to improve a base recommendation algorithm, and the key to choose a heuristic rule is what practical meaning of the counterfactual examples is required in  ... 
arXiv:2102.01868v4 fatcat:ly4jyi4v6na25m3hgwn264d5wu

A Measurement Model of Value of Data for Decision Making in the Digital Era

Guido Siestrup, Martin Knahl, Ismini Vasileiou, Ganesh Sankaran
2021 The International Journal of Integrated Supply Management  
; 2) How should the organisation-specific blending of machine and human rationality be factored in the measurement model?  ...  pertinent questions: 1) How can decision makers measure the value of data by giving a holistic account?  ...  impact Augmenting the heuristic with the predictive capability of the causal inference model seems to have improved the performance of the marketing and sales sub-system.  ... 
doi:10.1504/ijism.2021.10034789 fatcat:flu7ejw2y5dgbedo2wj4j456v4

Scalable Techniques for Mining Causal Structures

Craig Silverstein, Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman
1998 Very Large Data Bases Conference  
Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form "the existence of item A implies the existence of item B."  ...  They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together.  ...  Acknowledgments We thank the members of the Stanford Data Mining research group, particularly Lise Getoor, for their useful comments and suggestions.  ... 
dblp:conf/vldb/SilversteinBMU98 fatcat:xgda4c6e2ze5zc6hbaehvdz7za

Time-lagged Ordered Lasso for network inference

Phan Nguyen, Rosemary Braun
2018 BMC Bioinformatics  
Since expression regulation is dynamic, time-course data can be used to infer causality, but these datasets tend to be short or sparsely sampled.  ...  In addition, temporal methods typically assume that the expression of a gene at a time point depends on the expression of other genes at only the immediately preceding time point, while other methods include  ...  The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.  ... 
doi:10.1186/s12859-018-2558-7 fatcat:j5gwi6asvnfizp5tui2v3w4jrq

Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset [article]

Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, Nigam Shah
2017 arXiv   pre-print
Many decisions in healthcare, business, and other policy domains are made without the support of rigorous evidence due to the cost and complexity of performing randomized experiments.  ...  Many causal inference methods have been developed to mitigate these biases. However, there is no way to know which method might produce the best estimate of a treatment effect in a given study.  ...  us to benchmark the performance of causal inference methods on the resulting synthetic data.  ... 
arXiv:1711.00083v1 fatcat:h3rx2byu3beefnlk6sjnmm4sxu

Artificial intelligence and machine learning in emergency medicine: a narrative review

Brianna Mueller, Takahiro Kinoshita, Alexander Peebles, Mark A. Graber, Sangil Lee
2022 Acute Medicine & Surgery  
Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.  ...  We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments.  ...  In addition to predictive studies, supervised ML algorithms have come into use in causal inference studies targeting the investigation of the effects of interventions on outcomes of interest.  ... 
doi:10.1002/ams2.740 pmid:35251669 pmcid:PMC8887797 fatcat:otj7fjb3uzfg5h2x2vnqwpacom

A fast PC algorithm for high dimensional causal discovery with multi-core PCs

Thuc Le, Tao Hoang, Jiuyong Li, Lin Liu, Huawen Liu, Shu Hu
2018 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
The experimental results show that parallel-PC helps improve both the efficiency and accuracy of the causal inference algorithm.  ...  The PC algorithm is the state-of-the-art constraint based method for causal discovery.  ...  These methods either search for only specific causal structures or use heuristic functions to improve the efficiency of the algorithm.  ... 
doi:10.1109/tcbb.2016.2591526 pmid:27429444 fatcat:fiai6kihzzdl7p5szayu5pt76u
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