The order quantity and profit of the supply chain under different unit production costs. by Minchao Zheng (20558832)

Data Sheet 4_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Data Sheet 2_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Data Sheet 3_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…</p>Methods<p>Here, we conducted a comprehensive analysis of large-scale genomic datasets, including from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”

Pan-cancer expression characteristics of SEPN1 and its association with the TME. by Zisong Wang (8815727)

Molecular replacement (MR) solution and electron density maps of HEWL at various input CTDDs. by Ki Hyun Nam (5813819)

Overview of the research process. by Pedro Fong (2378413)

“…Food and Drug Administration (FDA)-approved drug that can bind to the Ca_v3.1 T-type calcium channel. We used the automated docking suite GOLD v5.5 with the genetic algorithm to simulate molecular docking and predict the protein-ligand binding modes, and the ChemPLP empirical scoring function to estimate the binding affinities of 2,115 FDA-approved drugs to the human Ca_v3.1 channel. …”

Schematic representation of the relationship between expected profits of the retailer and supplier. by Minchao Zheng (20558832)

Table1_Integrating genomics and AI to uncover molecular targets for mRNA vaccine development in lupus nephritis.xlsx by Lisha Mou (151003)

Image1_Integrating genomics and AI to uncover molecular targets for mRNA vaccine development in lupus nephritis.tif by Lisha Mou (151003)

Data analyzed for the article of <b>Evaluating photoplethysmography-based pulsewave parameters and composite scores for assessment of cardiac function: A comparison with echocardio... by Kulin (20907929)

“…Concurrently, echocardiographic parameters were derived by averaging the data from 1-3 heartbeats, allowing for a direct comparison of cardiac function assessments between the two techniques, by the following. …”

Comparison of experimental and simulated data across frequency differences and tactile Conditions, with audio and tactile input spread profiles. by Farzaneh Darki (22076051)

“…<b>C-D:</b> The -dependent profiles for the auditory input spread and tactile input spread (modeled as exponential decays) were derived using an optimization algorithm minimizing the mean squared error between experimental and computational data from Experiment 1. …”

Table 6_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Table 2_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Table 3_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Table 4_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Table 5_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.xlsx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. By intersecting MDGs with pain-related gene sets and conducting multi-step regression modeling, we established a five-gene prognostic signature (PSMB8/COL17A1/BICC1/CTRC/TRIP13). …”

Analysis of novel miRNAs in <i>E. histolytica</i> EVs. by Barbara Honecker (10328267)

“…<p><b>(A)</b> Comparison of the number of miRNAs detected in <i>Eh</i>A1 compared with <i>Eh</i>B2 EVs, based on <i>de novo</i> miRNA prediction using BrumiR algorithm version 3.0 [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0012997#pntd.0012997.ref031" target="_blank">31</a>] (n = 3 for each clone). …”

Schematic diagram of RNNs. by Ting Wang (16292)

“…Then, the sparrow search algorithm in artificial intelligence algorithm is taken to optimize the parameter search of the recurrent neural network and automatically extract the target scene. …”

Comparison of ablation test results. by Ting Wang (16292)

“…Then, the sparrow search algorithm in artificial intelligence algorithm is taken to optimize the parameter search of the recurrent neural network and automatically extract the target scene. …”