Applying causal discovery to single-cell analyses using CausalCell [PDF]
eLife, 2023 Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality.
Yujian Wen +7 more
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Bayesian Networks and Causal Discovery [PDF]
EntropyThe discovery of the precise causal representations underlying complex data forms the bedrock of artificial intelligence research [...]
Xiaoguang Gao, Zidong Wang
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A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyA representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task.
Francesco Canonaco +5 more
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Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models [PDF]
BiomedicinesBackground/Objectives: Diabetes is a dangerous disease that is accompanied by various complications, including cardiovascular disease. As the global diabetes population continues to increase, it is crucial to identify its causes.
Mi Jin Noh, Yang Sok Kim
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AnchorFCI: harnessing genetic anchors for enhanced causal discovery of cardiometabolic disease pathways [PDF]
Frontiers in GeneticsIntroductionCardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases,
Adèle H. Ribeiro +7 more
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Causal Discovery in Manufacturing: A Structured Literature Review
Journal of Manufacturing and Materials Processing, 2022 Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining ...
Stefan Thalmann +2 more
exaly +3 more sources
Review of Causal Discovery Methods Based on Graphical Models
Frontiers in Genetics, 2019 A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even ...
Kun Zhang +2 more
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Correction: A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools [PDF]
Frontiers in Systems BiologyFrancesco Canonaco +5 more
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Collective Causal Relations Discovery Algorithm for Multivariate Time-Series [PDF]
Jisuanji gongcheng, 2023 Causal discovery from multivariate time-series is a significant and fundamental problem in numerous disciplines.The existing multivariate time-series causal discovery methods learn the causal relations for each individual while some individuals may share
CAI Ruichu, WU Yunjin, CHEN Wei, HAO Zhifeng
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Environmental Data Science, 2022 Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always ...
Xavier-Andoni Tibau +5 more
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