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Clustering Effect on Cancer Molecular Subtype Classification
Mehwish Wahid Khan
, Muhammad Shahzad
, Iqra Akram
, Ghufran Ahmed
,
Shahid Hussain
, Muhammad Abdul Basit ur Rahim
School of Engineering (Behrend)
Research output
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Computer Science
Classification Models
100%
Dimensional Feature Space
100%
Classification Accuracy
100%
Gene Expression Data
100%
Computational Process
100%
Comparison Result
100%
Deep Learning Model
100%
Artificial Intelligence
100%
Deep Learning Method
100%
Cluster Centroid
100%
Keyphrases
Cluster Effect
100%
Classification Accuracy
33%
Healthcare
33%
Colorectal Cancer
33%
Personalized Medicine
33%
Performance Improvement
33%
Classification Model
33%
Order of Accuracy
33%
Artificial Intelligence
33%
Healthcare Applications
33%
Result Comparison
33%
Gene Expression Data
33%
Human Brain Function
33%
Molecular Subtypes
33%
High-dimensional Feature Space
33%
Computational Process
33%
Biological Insight
33%
Deep Learning Model
33%
Deep Learning
33%
Cancer Heterogeneity
33%
Cluster Centroid
33%
Cancer Classification
33%
Transcriptomic Data
33%
Biochemistry, Genetics and Molecular Biology
Transcriptomics
100%
Subtyping
100%
Gene Expression Data
100%
Brain Function
100%
Artificial Intelligence
100%
Mathematics
Deep Learning Method
100%
Dimensional Feature Space
50%
Clustering
50%
Human Brain
50%
Centroid
50%