Moger-Reischer, R. Z., Glass, J. I., Wise, K. S., Sun, L.,
Bittencourt, D. M. C., Lehmkuhl, B. K., Schoolmaster, D. R., Lynch, M., & Lennon, J. T. (2023). Evolution of a minimal cell. Nature, 620(7972), 122–127. https://doi.org/10.1038/s41586-023-06288-x
Archive for the 'SciLit' Category
Evolution of a minimal cell | Nature
September 2, 2024A Unification of Mediator, Confounder, and Collider Effects – PMC
August 31, 2024https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967310/
MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193. https://doi.org/10.1007/s11121-021-01268-xMacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193.
https://doi.org/10.1007/s11121-021-01268-x
QT:{{”
Third-variable effects are not distinguishable solely by statistical methods. Each third-variable effect can be fit to the same data, and if the relations between the variables are substantial, there will be evidence for each effect. In this sense, the confounder, mediator, and collider models are equivalent, providing an equal representation of the information contained in the data for three variables (Stelzl, 1986). Although mediation, confounding, and collision may equally explain the statistical associations among three variables, they describe different causal relations among those variables. Like much recent research on causal analysis, this paper highlights the centrality of the causal model underlying a research study and the important distinction between the causal model and the statistical model. The appropriate causal model is determined by prior empirical research and theory. The statistical analysis provides estimates for the proposed causal model.
“}}
A Unification of Mediator, Confounder, and Collider Effects – PMC
August 31, 2024https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967310/
MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193. https://doi.org/10.1007/s11121-021-01268-xMacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193.
https://doi.org/10.1007/s11121-021-01268-x
QT:{{”
Third-variable effects are not distinguishable solely by statistical methods. Each third-variable effect can be fit to the same data, and if the relations between the variables are substantial, there will be evidence for each effect. In this sense, the confounder, mediator, and collider models are equivalent, providing an equal representation of the information contained in the data for three variables (Stelzl, 1986). Although mediation, confounding, and collision may equally explain the statistical associations among three variables, they describe different causal relations among those variables. Like much recent research on causal analysis, this paper highlights the centrality of the causal model underlying a research study and the important distinction between the causal model and the statistical model. The appropriate causal model is determined by prior empirical research and theory. The statistical analysis provides estimates for the proposed causal model.
“}}
2302.04265 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
August 11, 2024https://arxiv.org/abs/2302.04265
thought this was interesting
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., & Jaakkola, T. (2023, February 8). PFGM++: Unlocking the potential of
Physics-Inspired Generative Models. arXiv.org.
https://arxiv.org/abs/2302.04265
Detecting hallucinations in large language models using semantic entropy | Nature
August 11, 2024https://www.nature.com/articles/s41586-024-07421-0
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models | Bioinformatics | Oxford Academic
July 28, 2024Nallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C. (2023). CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models. Bioinformatics, 39(1).
https://doi.org/10.1093/bioinformatics/btad029
https://academic.oup.com/bioinformatics/article/39/1/btad029/6989624
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