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

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

Data Sheet 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

Table 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.xlsx by Xiaopeng Zhan (4170574)

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

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

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

Image 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

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

“…Finally, we experimentally assessed the functional role of these genes by examining their expression in oxaliplatin-resistant cell lines and by performing gene knockdown experiments in colorectal cancer cells to measure subsequent changes in oxaliplatin sensitivity.…”

DataSheet1_Equivalent of distribution network with distributed photovoltaics for electromechanical transient study based on user-defined modeling.ZIP by Zhe Jiang (6903)

DataSheet1_Equivalent of distribution network with distributed photovoltaics for electromechanical transient study based on user-defined modeling.ZIP by Zhe Jiang (6903)

A secondary structure model of the effects of <i>Cis</i> mutation on splicing efficiency. by Idan Frumkin (3122832)

Methodology block diagram. by Gahao Chen (21688843)

“…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

DataSheet_1_Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, an... by Fang Zhang (197215)

“…</p>Methods<p>CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. …”

Table 1_Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.xlsx by Yan Wang (15435)

“…This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.</p>Methods<p>A content-based filtering algorithm was implemented to predict drug sensitivity. …”

Table 2_Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.xlsx by Yan Wang (15435)

“…This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.</p>Methods<p>A content-based filtering algorithm was implemented to predict drug sensitivity. …”

Table 3_Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.xlsx by Yan Wang (15435)

“…This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.</p>Methods<p>A content-based filtering algorithm was implemented to predict drug sensitivity. …”

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”