Stability of cancer clustering results with different feature filters. by Teemu J. Rintala (19333003)

Cancer subtype agreement with different pathway sets. by Teemu J. Rintala (19333003)

The process of optimizing RPGA by Q-Learning. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Xi’an metro line 2 disruption stations. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

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. …”

May 1st −7st Metro Line 2 OD Statistics Table. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Table 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

“…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …”

Table 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

“…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …”

Image 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …”

Image 3_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …”

Image 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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

“…Subsequent PFS analysis narrowed this set to four key genes (AXDND1, BAMBI, MAPK8IP2, and BMP7) that were significantly associated with patient survival following oxaliplatin-based therapy. External validation confirmed that different combinations of these four genes consistently and robustly predicted oxaliplatin sensitivity. …”

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. …”