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. …”

Gabor filters are created as sine wave filters in four different directions, namely 0, <i>π</i>/2, <i>π</i>, and 3<i>π</i>/4 degrees. by Reyhane Ahmadi (14857363)

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>…”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. …”

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

Data Sheet 1_Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.pdf by Everest Castaneda (21758900)

“…Node and edge arrangements within gene networks impact comparisons between classes of disease, and connectivity metrics, such as those focused on degrees, betweenness, and centrality, do not yield sufficient discernment of disease network classification. …”

Orbital-Based Correlated Electron–Nuclear Dynamics for Extended Systems with Exact Factorization by Daeho Han (10579081)

“…In this work, we introduce a practical orbital-based framework for simulating correlated electron–nuclear dynamics in extended systems within the exact factorization (XF) formalism. Building on our earlier derivation of time-dependent Kohn–Sham (TDKS) equations that merge real-time time-dependent density functional theory with XF, we apply the classical path approximation and incorporate pairwise XF-derived decoherence corrections in the Kohn–Sham basis. …”

Training results under different parameters. by Yang Zhang (30734)

“…Furthermore, the implementation of a nonmonotonic strategy for dynamically adjusting the loss function weights significantly boosts the model’s detection precision and training efficiency. …”

The efficient multi-scale attention. by Yang Zhang (30734)

“…Furthermore, the implementation of a nonmonotonic strategy for dynamically adjusting the loss function weights significantly boosts the model’s detection precision and training efficiency. …”