Results 41 to 50 of about 134,347 (253)

Mental health progress requires causal diagnostic nosology and scalable causal discovery

Frontiers in Psychiatry, 2022
Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other ...
Glenn N. Saxe, Leonard Bickman, Leonard Bickman, Sisi Ma, Constantin Aliferis   +4 more
doaj   +1 more source

The Role of “Adult‐Onset” Cancer Predisposition Genes in Pediatric Cancer: A Comprehensive Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Current literature estimates that 10% of pediatric cancers are caused by pathogenic or likely pathogenic (P/LP) germline variants in cancer predisposition genes (CPGs). Variants in CPGs thought to increase cancer risk exclusively during adulthood are referred to as “adult‐onset” CPGs (aoCPGs).
Maria Rozo, Miriam A. Bornhorst, Angela J. Waanders, Sara Zarnegar‐Lumley, Ashwin Lakshman Koppayi, Lucy A. Godley   +5 more
wiley   +1 more source

Causality, Causal Discovery, and Causal Inference in Structural Engineering

CoRR, 2022
Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand and/or
openaire   +2 more sources

Confidence in Causal Discovery with Linear Causal Models

, 2021
Structural causal models postulate noisy functional relations among a set of interacting variables. The causal structure underlying each such model is naturally represented by a directed graph whose edges indicate for each variable which other variables it causally depends upon. Under a number of different model assumptions, it has been shown that this
David Strieder, Tobias Freidling, Stefan Haffner, Mathias Drton   +3 more
openaire   +3 more sources

Stable Differentiable Causal Discovery

CoRR, 2023
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD methods are numerically unstable, with poor performance beyond tens of variables.
Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei   +3 more
openaire   +3 more sources

Health Literacy, Self‐Efficacy and Knowledge of Sickle Cell Disease Among Caregivers

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Sickle cell disease (SCD) is a hereditary blood disorder in which abnormal haemoglobin leads to severe anaemia, painful crises and organ failure. Caregivers’ health literacy (HL) – their ability to assess, understand and apply information, and interact with healthcare professionals – is crucial for managing children with SCD, yet ...
Melanie Bruinooge, Maite E. Houwing, Rowena L. Grohssteiner, Ferdows Atiq, Sonja A. M. C. Teuben, Mirjam P. Fransen, Marjon H. Cnossen   +6 more
wiley   +1 more source

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Journal of Geophysical Research: Machine Learning and Computation
Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science.
J. Jake Nichol, Michael Weylandt, G. Matthew Fricke, Melanie E. Moses, Diana Bull, Laura P. Swiler   +5 more
doaj   +1 more source

Comparing Causal Bayesian Networks Estimated from Data

Entropy
The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal mechanisms underlying multiple systems and uncovering ...
Sisi Ma, Roshan Tourani
doaj   +1 more source

Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

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, Vincenzo Lagani, Christina Heinze-Deml, Angelika Schmidt, Jesper Tegner, Ioannis Tsamardinos   +5 more
doaj   +1 more source

Psychosocial Functioning After Pediatric Bone Sarcoma: Generic and Survivor‐Specific Outcomes in Adolescent and Young Adult Patients

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric bone sarcoma patients and survivors may experience psychosocial challenges related to childhood cancer after their intensive, body‐altering treatment. This cross‐sectional study aimed to evaluate generic and survivor‐specific psychosocial outcomes in a national cohort of pediatric bone sarcoma patients and survivors, and ...
Hinke van der Hoek, Heleen Maurice‐Stam, Laura R. Beek, Jennifer van Dijk, Marjolein E. M. Langemeijer, Relinde W. Slooff‐Lentink, Alied M. van der Aa‐van Delden, Anne Westerweel, Marloes van Gorp, Hendrik W. B. Schreuder, Lianne M. Haveman, W. Peter Bekkering, Leonie G. Tigelaar, Johannes H. M. Merks, Martha A. Grootenhuis   +14 more
wiley   +1 more source