Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

“…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”

Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

“…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”

Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

“…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”

Noninvasive Diagnosis of Early-Stage Chronic Kidney Disease and Monitoring of the Hemodialysis Process in Clinical Practice via Exhaled Breath Analysis Using an Ultrasensitive Flex... by Xin Zhao (71840)

“…With the assistance of a pattern recognition algorithm , the early diagnosis of CKD was achieved by the sensor, with PCA being used due to sensor’s cross-sensitivity to CKD biomarkers. …”

Discovering two KTx rejection subtypes. by Xihao Shen (20347942)

The structural mutation of neuroevolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The genome coding scheme. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The analysis of feature importance. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

S1 Data - by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The structure of the data sample. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The genome recombination of neuroevolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The principle of sample data augmentation. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The speciation of NANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

Merged dataset with analysis of variance and visualization after removal of batch effects for GSE183272 and GSE236374. by Wenlong Kuang (20485167)

Table 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.docx by Yuxuan Li (1674307)

Image 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.tif by Yuxuan Li (1674307)

Profile of dysregulated proteins in SCA1 cells containing polyQ inclusions. by Aimilia-Christina Vagiona (17584758)