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

“…Finally, qRT-PCR confirmed the differential expression of key genes in clinical samples.</p>Results<p>We identified 25 ribosome biogenesis-related differentially expressed genes, which were significantly enriched in RNA degradation and rRNA processing. …”

(A-H). Case 1: primary subcutaneous facial alveolar echinococcosis. by Louis Bohard (22250661)

Data Sheet 1_Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation.docx by Xianzhi Deng (20548430)

“…In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity. …”

Comparison of DDM and behavioral measures for all 300 networks before (blue) and after (pink) plasticity. by Jyotika Bahuguna (729510)

Evolution of DDM parameters with plasticity. by Jyotika Bahuguna (729510)

Supplementary file 1_Early learning curve changes in objective performance indicators during robotic cholecystectomy.docx by Derrick Liu (2071018)

“…As the first study to evaluate objective metrics throughout a learning curve for newly performing robotic cholecystectomy, we identify relevant OPIs that may be critical for future proficiency tracking, 8 of which impact a surgical step with a steep learning curve in transitioning from laparoscopic to robotic cholecystectomy, cystic duct ligation/division.…”

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

“…Finally, qRT-PCR confirmed the differential expression of key genes in clinical samples.</p>Results<p>We identified 25 ribosome biogenesis-related differentially expressed genes, which were significantly enriched in RNA degradation and rRNA processing. …”

Effect of reward sequences on changes in accuracy and reaction times (RTs). by Jyotika Bahuguna (729510)

Evolution of behavioral measures for 300 networks over 16 trials with plasticity. by Jyotika Bahuguna (729510)

Distance from the optimal direction for different levels of reward probabilities. by Jyotika Bahuguna (729510)

Decision-making deconstructed. by Jyotika Bahuguna (729510)

“…What remains unclear, and we address in this work, is how learning modulates the balance between control ensembles in a way that shifts decision policies so as to maximize reward rate. …”

Summarized results of targeted screen of 20 RBPs. by Ashley N. Hayden (19871128)

Targeted screen of 20 conserved neuronal RBPs reveals eight novel associative memory regulators. by Ashley N. Hayden (19871128)

Grad-CAM heatmaps showing model focus in sex-biased synovial recess distension detection. by M. Mendez (6624953)

Grad-CAM heatmaps showing model focus in sex-biased synovial recess distension detection. by M. Mendez (6624953)

Data Sheet 1_Convolutional neural networks decode finger movements in motor sequence learning from MEG data.docx by Aleksey Zabolotniy (22211695)

“…We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.…”

Image 2_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.tif by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Table 1_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.docx by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Image 1_Construction of a clinical prediction model for osteoporosis in asymptomatic elderly population based on machine learning algorithm.tif by Jiaming Wang (2637667)

“…</p>Method<p>In this study, a robust and accurate prediction model for osteoporosis was developed and validated based on machine learning and SHAP techniques. We validated the model using ROC, calibration, and DCA curves. …”

Imbalanced Dataset Distribution. by Mudhafar Jalil Jassim Ghrabat (22177655)

“…This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. …”