Results 61 to 70 of about 1,819,138 (329)
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
Bayesian causal discovery for policy decision making
Data & PolicyThis paper demonstrates how learning the structure of a Bayesian network, often used to predict and represent causal pathways, can be used to inform policy decision-making.
Catarina Moreira +6 more
doaj +1 more source
Survey and Evaluation of Causal Discovery Methods for Time Series
Journal of Artificial Intelligence Research, 2022 We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series.
Charles K. Assaad +2 more
semanticscholar +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source
Correlational data, causal hypotheses, and validity [PDF]
, 2010 A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This
Russo, Federica, Federica Russo
core +1 more source
Organoids in pediatric cancer research
FEBS Letters, EarlyView.Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source
Scientific Reports, 2017 Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated
Sofia Triantafillou +5 more
doaj +1 more source
Causal Discovery through Synergizing Large Language Model and Data-Driven Reasoning
Knowledge Discovery and Data MiningRevealing the underlying causal mechanisms in the real world is critical for scientific and technical progress. Despite advancements over the past decades, the lack of high-quality data and the inability of traditional causal discovery algorithms (TCDA ...
Huaming Du +8 more
semanticscholar +1 more source
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery [PDF]
Annual Meeting of the Association for Computational LinguisticsCausal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and
Chengao Shen +5 more
semanticscholar +1 more source
Reciprocal control of viral infection and phosphoinositide dynamics
FEBS Letters, EarlyView.Phosphoinositides, although scarce, regulate key cellular processes, including membrane dynamics and signaling. Viruses exploit these lipids to support their entry, replication, assembly, and egress. The central role of phosphoinositides in infection highlights phosphoinositide metabolism as a promising antiviral target.
Marie Déborah Bancilhon, Bruno Mesmin
wiley +1 more source