Unraveling the gender-specific molecular landscape of lung squamous cell carcinoma progression by Ayushi Dwivedi (20658343)

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites by Gustav Olanders (3711889)

“…In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. …”

Supplementary Material for: Dynamic Prediction of Cardiovascular Death among Old People with Mildly Reduced Kidney Function Using Deep Learning Models Based on a Prospective Cohort... by figshare admin karger (2628495)

“…ABSTRACT Aim: Cardiovascular disease (CVD) is more likely to occur in old people with mildly reduced kidney function. We aimed to identify target features in this cohort to reduce cardiovascular death using deep learning models. …”

Abbreviations used in the text. by Lorenzo Ruinelli (13014138)

“…The majority of AKI episodes (77%) occurred within the first three days of hospitalization, and >50% of subjects with AKI were discharged before complete renal function recovery. …”

Raccoon use-availability and oral rabies vaccination (ORV) bait data from the Burlington, Vermont ORV zone by Katherine M. McClure (19660843)

“…The off-time bait calculator refers to the NRMP baiting delivery algorithm which denotes a percentage of time during which bait delivery is restricted by land cover type; these proportions are reported for each 30 meter resolution grid within the study area.…”

This figure highlights the limitations of manual scoring. by Saeed Mohammadzadeh (13492285)

Schematic representation of selected data from the Allen Institute dataset. by Saeed Mohammadzadeh (13492285)

Application of explainable clustering on cell features for unsupervised organization scoring. by Saeed Mohammadzadeh (13492285)

Main Figure 2: Characteristics of the gene groups selected by the HN-score by Sora Yonezawa (14618045)

“…Abbreviations: GO, Gene Ontology; BP, Biological Process; MF, Molecular Function; CC, Cellular Component</p>…”

Presentation_1_Understanding contrast perception in amblyopia: a psychophysical analysis of the ON and OFF visual pathways.pdf by Junhan Wei (10052356)

“…Using the quick contrast sensitivity function (qCSF) algorithm, we measured balanced CSF which would stimulate the ON and OFF pathways unselectively, and CSFs for increments and decrements that would selectively stimulate the ON and OFF visual pathways. …”

Predicted F1 score across temporal aggregation levels and BirdNET parameters. by Andrew J. Fairbairn (22231970)

Fundamental dataset of the study. by Weizheng Jiang (21647509)

“…We collected data from finance and business administration interns using surveys and the After-Action Review method and analyzed them using the gradient descent algorithm. The findings reveal a dual effect of trust in AI on cognition: while functional and emotional trust enhance higher-order cognition, the transparency dimension of cognitive trust inhibits cognitive processes. …”

Analysis of interactive impact coefficients. by Weizheng Jiang (21647509)

“…We collected data from finance and business administration interns using surveys and the After-Action Review method and analyzed them using the gradient descent algorithm. The findings reveal a dual effect of trust in AI on cognition: while functional and emotional trust enhance higher-order cognition, the transparency dimension of cognitive trust inhibits cognitive processes. …”

Questionnaire structure and item composition. by Weizheng Jiang (21647509)

“…We collected data from finance and business administration interns using surveys and the After-Action Review method and analyzed them using the gradient descent algorithm. The findings reveal a dual effect of trust in AI on cognition: while functional and emotional trust enhance higher-order cognition, the transparency dimension of cognitive trust inhibits cognitive processes. …”