Interactive visualization of ocean unsteady flow data based on dynamic adaptive pathline by Fenglin Tian (6002957)

“…This study presents an interactive visualization algorithm designed for the spatio-temporal correlated ocean unsteady flow field utilizing dynamic adaptive pathlines. …”

ProSAAS is expressed in multiple cell types in a mixed species meta-analysis. by Nicholas Schaffer (21376843)

Table 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.xlsx by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 3_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 1_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 7_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 6_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 5_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Image 4_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…Using occurrence data and environmental variable, we employ the Maximum Entropy (MaxEnt) algorithm within the species distribution modeling (SDM) framework to estimate occurrence probability at a spatial resolution of 1/12° (~10 km). …”

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. …”

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.…”

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. …”