IgA is decreased in individual donor milk and is present in human milk-based fortifiers. by Christian Tamar (21100605)

The iTRAQ-based proteomics data. by Jing Mao (148640)

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Descriptive statistics based on type of therapy and time of assessment. by Nina Brkić-Jovanović (18886883)

eQTL analysis of significant SNPs. by Dai Nogimura (21445649)

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Significant associations in the candidate gene approach. by Dai Nogimura (21445649)

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Significant disease phenotypes with LADA loci. by Margaret Sunitha Selvaraj (13994016)

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Hierarchical regression results for emotional disturbances (anxiety/depression) and RLOC domains. by Shazli Ezzat Ghazali (6407102)

Predictive power of multivariate models compared to a baseline containing age, sex and population variables. by Peter Petschner (665825)

Data_Sheet_1_Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer’s Disease Based on Major Depressive Disorder Risk Variants.pdf by Suraj Upadhya (12208697)

“…Full models showed significant performance in predicting depression in LOAD for both datasets (P < 0.001 for all).…”

PGRN decreased Dectin-2 expression on the surface of macrophages and neutrophils. by Jiayu Liu (3951515)

Results of multiple regression analysis for body image dimensions predicting functioning in different areas among women with type 2 diabetes. by Andrzej Kokoszka (9557853)

Results of multiple regression analysis for body image dimensions predicting functioning in different areas among men with type 2 diabetes. by Andrzej Kokoszka (9557853)

Table_1_Machine Learning Prediction of Treatment Outcome in Late-Life Depression.pdf by Adrienne Grzenda (459874)

“…Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores.…”

Table_3_Machine Learning Prediction of Treatment Outcome in Late-Life Depression.pdf by Adrienne Grzenda (459874)

“…Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores.…”

Table_2_Machine Learning Prediction of Treatment Outcome in Late-Life Depression.pdf by Adrienne Grzenda (459874)

“…Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores.…”

Classification of injuries based on Delphi consensus. by Ali S. Raja (9465320)

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Ablation study of DeepGAM. by Chiyoung Lee (11782395)

“…In conclusion, DeepGAM with STE demonstrated accurate and interpretable performance in predicting depression compared to existing machine learning methods.…”

Performance comparisons on public data (cancer). by Chiyoung Lee (11782395)

“…In conclusion, DeepGAM with STE demonstrated accurate and interpretable performance in predicting depression compared to existing machine learning methods.…”

ROC curve visualization. by Chiyoung Lee (11782395)

“…In conclusion, DeepGAM with STE demonstrated accurate and interpretable performance in predicting depression compared to existing machine learning methods.…”

Results of simple logistic regression and multiple logistic regression for predicting determinants of adolescent romantic relationship. by Muhammad Ikhwan Mud Shukri (16624617)