Variables name and variable measurement. by Jinyuan Li (5927267)

AI KRegression by CHIN-CHENG CHU (20718575)

Decrease in loss during the training process. by Muhammad Idrees (92773)

Comparison of different inputs. by Mengyao Zhou (9266132)

“…We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. …”

Comparison of different P-values. by Mengyao Zhou (9266132)

“…We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. …”

Comparison of different weights. by Mengyao Zhou (9266132)

“…We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. …”

Top 10 score drug combinations. by Mengyao Zhou (9266132)

“…We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. …”

An illustration for the lineages identification procedure. Left panel: Each leaf represents a cell, characterized by its measured set of barcodes, denoted as . Each individual barc... by Nava Leibovich (12908477)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Idarubicin-loaded chitosan nanobubbles to improve survival and decrease drug side effects in hepatocellular carcinoma by Monica Mossenta (4276213)

Pathologically confirmed colon lesions. by Sarah E. Copeland (13951506)

“…<div><p>Mitotic Arrest Deficient 1 (gene name <i>MAD1L1</i>), an essential component of the mitotic spindle assembly checkpoint, is frequently overexpressed in colon cancer, which correlates with poor disease-free survival. …”

Presents the bibliometric search results. by Yan She (8639796)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Modeling results of 8-week-old female C57BL/6J mice. by Yan She (8639796)

Sexual maturity status of female C57BL/6J Mice at different ages. by Yan She (8639796)

Flow chart of data screening and analysis. by Yan She (8639796)

Paeameter ranges and optimal values. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Datasets used in the study area. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Evaluation of the improved random forest model. by Zhen Zhao (159931)

“…Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”