Table_2_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Table_5_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Table_1_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Table_2_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Table_3_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Table_5_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.xlsx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

DataSheet_1_Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology.docx by Vennila Suriyagandhi (22061534)

“…The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. …”

Tree-Enhanced Latent Space Models for Two-Mode Networks by Dan Pu (3714616)

“…In this framework, each node is characterized by a latent embedding vector, reparameterized as the aggregate of intermediate vectors corresponding to nodes within the tree structure. By optimizing the log-likelihood function augmented with a tree-based regularization term, the proposed model facilitates the simultaneous estimation of embedding vectors and the derivation of interpretable community structures. …”

Edge-disjoint spanning trees in the Möbius cube by Xiaorui Li (748417)

“…EDSTs in a network can facilitate many network functionalities such as improving the rate of data broadcasting, secure message distribution, fault-tolerant broadcasting, etc., and have inspired many researchers’ interest. …”

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. …”

An extensible framework for the probabilistic search of stochastically-moving targets characterized by generalized Gaussian distributions or experimentally-defined regions of inter... by Benjamin L. Hanson (11173296)

“…<p>This article presents a continuous-time framework for modeling the evolution of a probability density function (PDF) summarizing the region of interest (ROI) during the search for a stochastically-moving, statistically stationary target. …”

LDD microtubule-based nucleation naturally results in a local microtubule density-dependent fraction of microtubule-based nucleation. by Marco Saltini (22209094)

“…<p>Fraction of microtubule-based nucleation as a function of (A) local and (B) global microtubule density (<i>ρ</i>) during a single simulation run. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>raw_data/glasgow_open_built/glasgow_open_built_areas.shp</code> - Grid defining sampling points</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python svi_module/get_svi_data.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata (IDs, coordinates)</li><li><code>svi_module/svi_data/images/</code> - Downloaded street view images</li></ul><h3>Step 2: Predict Perceptions</h3><p dir="ltr">Use pre-trained deep learning models to predict perceptual qualities (safety, beauty, liveliness, etc.) from street view images.…”

Transcription factor of Key regulator genes. by Kankana Bhattacharjee (20623639)

“…Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. …”

Gene-drug interaction of the key regulators. by Kankana Bhattacharjee (20623639)

“…Our analysis revealed 33 key regulators were predominantly enriched in neuroactive ligand-receptor interaction, Cell adhesion molecules, Leukocyte transendothelial migration pathways; positive regulation of cell proliferation, positive regulation of protein kinase B signaling biological functions; G-protein beta-subunit binding, receptor binding molecular functions etc. …”

PhD Proposal Defense: Threshold-based Association Analysis Frameworks for Continuous Variables by Md Mahin (21434966)

“…Norma associates point-wise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. …”

PhD Defense: Threshold-based Association Analysis Frameworks for Continuous Variables by Md Mahin (21434966)

“…Norma associates point-wise functions with each variable-e.g., a function that returns poverty rates for each location in New Mexico. …”

Artificial Neural Network Model for Predicting the Viscosity of Crosslinked Polyacrylamide and Polyethylenimine Polymer Gel for Oilfield Water Control by S M Safayet Aziz (20832236)

“…The model was trained using the Levenberg-Marquardt algorithm. The hidden layer uses the tangent sigmoid (Tansig) activation function, and the output layer employs a linear (Purelin) activation function. …”

k-means based load estimation of domestic smart meter measurements by Meng Cheng (19655023)

“…The developed algorithm applies cluster centres - of previously clustered load profiles - and distance functions to estimate missing and future measurements. …”

DataSheet1_Identification of novel markers for neuroblastoma immunoclustering using machine learning.zip by Longguo Zhang (20105238)

“…Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.…”