Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

Nomenclature table. by Jianhua He (341366)

“…The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. …”

Homomorphic binary tree. by Jianhua He (341366)

“…The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. …”

Time taken to simulate running 30 switch cycles. by Jianhua He (341366)

“…The wavelet reconstruction algorithm can simulate all kinds of fast changes in the actual working process more accurately and compress irrelevant information while retaining key signal features, so as to optimize the simulation performance of the model. …”

DataSheet1_Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification.ZIP by Guili Ding (16701822)

“…First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. …”

Table3_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.DOCX by Tao Wang (12008)

Image2_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.TIF by Tao Wang (12008)

Table1_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.DOCX by Tao Wang (12008)

Image3_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.TIF by Tao Wang (12008)

Table2_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.DOCX by Tao Wang (12008)

Image1_Comprehensive analysis of nine m7G-related lncRNAs as prognosis factors in tumor immune microenvironment of hepatocellular carcinoma and experimental validation.TIF by Tao Wang (12008)

Data_Sheet_1_A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials.PDF by Thierry Nieus (165355)

Image 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 6_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 4_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Image 3_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 8_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Data Sheet 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”