Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Unraveling of Phosphotyrosine Signaling Complexes Associated with T Cell Exhaustion Using Multiplex Co-Fractionation/Mass Spectrometry by Wei Liu (20030)

“…T cell exhaustion, characterized by the upregulation of inhibitory receptors and loss of effector functions, plays a crucial role in tumor immune evasion. …”

Averaging over channels of interest. by Ambra Ferrari (5941640)

WTools 3D scalp maps. by Ambra Ferrari (5941640)

Feature intersection and sequence patterns for <i>S</i>. <i>cerevisiae</i> S site of five fungal species. by Chao Wang (146527)

Feature importance, contribution and dependency analysis. by Chao Wang (146527)

Comparison of WTools and EEGLAB. by Ambra Ferrari (5941640)

Comparison of complex Morlet wavelets. by Ambra Ferrari (5941640)

WTools time-frequency plots and 2D scalp maps. by Ambra Ferrari (5941640)

MFPSP workflow. by Chao Wang (146527)

WTools pipeline. by Ambra Ferrari (5941640)

Comprehensive time-frequency results. by Ambra Ferrari (5941640)

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx by Jingjing Chen (293564)

“…A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Simultaneous Inference for Monotone and Smoothly Time-Varying Functions Under Complex Temporal Dynamics by Tianpai Luo (22145637)

“…<p>We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. …”