Results 51 to 60 of about 44,463 (307)
SHAP feature dependence plots.
, 2020 The relationship between actual feature values (x-axis) and corresponding SHAP values (y-axis) is shown as green points. Positive SHAP values indicate an increased risk of decompensated tinnitus relative to the training set average, and vice versa.
Benjamin Boecking (8378490) +5 more
core +1 more source
, 2023 This supplementary file is in HTML format and can be used to check feature importance with respect to model predictions via the SHAP analysis. (HTM)
Ghadeer O. Ghosheh (17319215) +7 more
core +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Personalized Network‐Guided Neuromodulation Enhances Human Working Memory
Advanced Science, EarlyView.A personalized neuromodulation framework combining individualized functional brain network targeting with real‐time neural decoding is introduced. Using concurrent TMS–fMRI, participant‐specific stimulation targets and optimal frequencies are identified. Only optimal‐frequency stimulation improves working memory across sessions.
Ahsan Khan +13 more
wiley +1 more source
Fool SHAP with Stealthily Biased Sampling
CoRR, 2022 SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations.
Gabriel Laberge +4 more
openaire +3 more sources
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
Advanced Science, EarlyView.It is innovatively utilized single‐cell RNA sequencing to explore the underlying causes of diabetes mellitus‐induced erectile dysfunction, followed by machine learning‐driven design of a single‐atom nanozyme (Fe‐DMOF) for precision treatment of erectile dysfunction.
Xiang Zhou +8 more
wiley +1 more source
International Journal of Management and EconomicsThe study aims to establish patterns of relations between the profitability of the European Union (EU) banking sectors between 2007 and 2021 and sets of variables appropriate for clusters of countries into which the 27 countries of the EU are divided ...
Bernardelli Michał +2 more
doaj +1 more source
An interpretable probabilistic prediction algorithm for shield movement performance
Frontiers in Earth ScienceTotal thrust and torque are two key indicators of shield movement performance. Most existing data-driven machine learning studies focus on developing more accurate models for predicting total thrust and torque but overlook the interpretability of the ...
Yapeng Zhang +15 more
doaj +1 more source
, 2023 Each point represents each observation; the red line represents a trend line. X-axis is the covariate of interest, Age(years). The SHAP value represents the log-odds for heart disease. (TIF)
Samuel Y. Huang (14670660) +1 more
core +1 more source