Table_1_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.XLSX by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_3_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_7_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_6_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_9_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_1_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Table_3_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.XLSX by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_4_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_10_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Table_5_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.XLSX by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_8_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Image_13_Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics.TIFF by Xingxing Zhao (2792998)

“…Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). …”

Known compounds and new lessons: structural and electronic basis of flavonoid-based bioactivities by Rohan J. Meshram (6563189)

“…Abbreviations2′HFN-2′</p><p>hydroxy flavonone</p>2D<p>2 dimension</p>3D<p>3 dimension</p>3H7MF<p>3-hydroxy-7-methoxy flavone</p>4′HFN-4′<p>hydroxy flavonone</p>4′MF- 4′<p>methoxy flavone</p>7HFN<p>7-hydroxy flavonone</p>CHARMM<p>Chemistry at Harvard Macromolecular Mechanics</p>COX<p>cyclooxygenase</p>COX-1<p>cyclooxygenase-1</p>COX-2<p>cyclooxygenase-2</p>DM<p>dipole moment</p>DPPH- 2, 2<p>diphenyl-1-picryl hydrazine</p>EA<p>electron affinities</p>EGFR<p>epidermal growth factor receptor</p>E-HOMO<p>Highest occupied molecular orbital energy</p>E-LUMO<p>Lowest unoccupied molecular orbital energy</p>EPA<p>eicosapentaenoic acid</p>FROG2<p>FRee Online druG conformation generation</p>GA<p>Genetic Algorithm</p>GROMACS<p>GROningen MAchine for Chemical Simulations</p>HOMO<p>Highest occupied molecular orbital</p>IP<p>Ionization potential</p>LOMO<p>Lowest unoccupied molecular orbital</p>MD<p>Molecular dynamics</p>MO<p>Molecular orbital</p>NAMD<p>Nanoscale Molecular Dynamics</p>NSAIDs<p>Non-Steroidal Anti Inflammatory Drugs</p>Ns<p>nanoseconds</p>NVE<p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p>PDB-ID<p>Protein Data Bank Identifier</p>PME<p>Particle Mesh Ewald</p>PyRX<p>Python Prescription</p>RMSD<p>Root-Mean-Square Deviation</p>RMSF<p>Root-Mean-Square Fluctuation</p>RLS<p>reactive lipid species</p>ROS<p>Reactive Oxygen Species</p>SASA<p>solvent accessible surface area</p>SMILES<p>simplified molecular-input line-entry system</p>SOR<p>superoxide anion radical</p>UFF<p>Universal force field</p>VEGF<p>vascular endothelial growth factor</p>VEGFR<p>vascular endothelial growth factor receptor</p>VMD<p>Visual molecular dynamics</p><p></p> <p>hydroxy flavonone</p> <p>2 dimension</p> <p>3 dimension</p> <p>3-hydroxy-7-methoxy flavone</p> <p>hydroxy flavonone</p> <p>methoxy flavone</p> <p>7-hydroxy flavonone</p> <p>Chemistry at Harvard Macromolecular Mechanics</p> <p>cyclooxygenase</p> <p>cyclooxygenase-1</p> <p>cyclooxygenase-2</p> <p>dipole moment</p> <p>diphenyl-1-picryl hydrazine</p> <p>electron affinities</p> <p>epidermal growth factor receptor</p> <p>Highest occupied molecular orbital energy</p> <p>Lowest unoccupied molecular orbital energy</p> <p>eicosapentaenoic acid</p> <p>FRee Online druG conformation generation</p> <p>Genetic Algorithm</p> <p>GROningen MAchine for Chemical Simulations</p> <p>Highest occupied molecular orbital</p> <p>Ionization potential</p> <p>Lowest unoccupied molecular orbital</p> <p>Molecular dynamics</p> <p>Molecular orbital</p> <p>Nanoscale Molecular Dynamics</p> <p>Non-Steroidal Anti Inflammatory Drugs</p> <p>nanoseconds</p> <p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p> <p>Protein Data Bank Identifier</p> <p>Particle Mesh Ewald</p> <p>Python Prescription</p> <p>Root-Mean-Square Deviation</p> <p>Root-Mean-Square Fluctuation</p> <p>reactive lipid species</p> <p>Reactive Oxygen Species</p> <p>solvent accessible surface area</p> <p>simplified molecular-input line-entry system</p> <p>superoxide anion radical</p> <p>Universal force field</p> <p>vascular endothelial growth factor</p> <p>vascular endothelial growth factor receptor</p> <p>Visual molecular dynamics</p> <p>Communicated by Ramaswamy H. …”

PySilsub—a toolbox for silent substitution by Joel Martin (11864048)

“…Device settings that will produce lights to selectively stimulate the photoreceptor(s) of interest can be found using a variety of analytic and algorithmic approaches. Here we present <em>PySilSub</em>, a novel Python package for silent substitution featuring object-oriented support for individual colorimetric observer models, multi-primary stimulation devices, and solving silent substitution problems with linear algebra and constrained numerical optimisation. …”

Image 7_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 5_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 3_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 6_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 4_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 1_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”