Results 41 to 50 of about 67,619 (257)

Conceptual challenges for interpretable machine learning [PDF]

Synthese, 2020
AbstractAs machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users?
openaire   +3 more sources

Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability

Data & Policy
Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no ...
Patrick Rehill, Nicholas Biddle
doaj   +1 more source

SMILE: systems metabolomics using interpretable learning and evolution

BMC Bioinformatics, 2021
Background Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and ...
Chengyuan Sha, Miroslava Cuperlovic-Culf, Ting Hu   +2 more
doaj   +1 more source

Structure‐forward targeting of claudins with synthetic binders

FEBS Letters, EarlyView.
Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley   +1 more source

Interpretable machine learning for Kronecker coefficients

Advances in Theoretical and Mathematical Physics
We analyze the saliency of neural networks and employ interpretable machine learning models to predict whether the Kronecker coefficients of the symmetric group are zero or not. Our models use triples of partitions as input features, as well as b-loadings derived from the principal component of an embedding that captures the differences between ...
Giorgi Butbaia, Kyu-Hwan Lee, Fabian Ruehle   +2 more
openaire   +2 more sources

A Semiparametric Approach to Interpretable Machine Learning

CoRR, 2020
Black box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical decision-making processes.
Numair Sani, Jaron J. R. Lee, Razieh Nabi, Ilya Shpitser   +3 more
openaire   +2 more sources

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Interpretable Machine Learning for TabPFN

The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabular data, is able to achieve state-of-the-art performance on a variety of classification tasks while producing posterior predictive distributions in mere seconds by in-context ...
David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer 0001, Thomas Nagler, David Rügamer   +5 more
openaire   +4 more sources

Interpretable molecular encodings and representations for machine learning tasks

Computational and Structural Biotechnology Journal
Molecular encodings and their usage in machine learning models have demonstrated significant breakthroughs in biomedical applications, particularly in the classification of peptides and proteins.
Moritz Weckbecker, Aleksandar Anžel, Zewen Yang, Georges Hattab   +3 more
doaj   +1 more source

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source