The performance of various classifiers on Dataset-1.

The performance of various classifiers on Dataset-1.

<p>Initially, the dataset is transformed into a reduced latent space <i>z</i> using the proposed encoder <i>E</i>, after which classification is performed in this reduced space.</p>

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Bibliographic Details
Main Author:	Ma Yinghua (22446698) (author)
Other Authors:	Ahmad Khan (6067526) (author), Yang Heng (10181637) (author), Fiaz Gul Khan (22446701) (author), Afnan Aldhahri (22446704) (author), Iftikhar Ahmed Khan (5679773) (author)
Published:	2025
Subjects:	Pharmacology Science Policy Biological Sciences not elsewhere classified xlink "> early preserving global information triplet margin loss composite loss function proposed model combines omics data provides omics data integration downstream classification tasks analyzing omics data preventing information loss enhance precision medicine end contrastive manner accurate cancer detection contrastive adversarial encoder omics data information loss adversarial loss precision medicine model achieved classification accuracy weighted combination vision transformer utilizing self single type results show paper proposes often results limited perspective latent space findings demonstrate feature redundancy f1 score eliminating redundancy effective treatment comprehensive understanding class classifications cancer research
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