General model performance for different algorithms.

General model performance for different algorithms.

<p><b>A)</b> Model performance on the training set. <b>B)</b> Model performance on the test set. For all plots, “+ ” denotes the outliers that fall significantly outside the range of the other data points in the dataset.</p>

Saved in:

Bibliographic Details
Main Author:	Binghan Xue (21253235) (author)
Other Authors:	Yanji Xu (185940) (author), Ruili Huang (480703) (author), Qian Zhu (191342) (author)
Published:	2025
Subjects:	Biochemistry Pharmacology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified uncover latent relationships support vector classifier novel treatment modalities medical challenges necessitates innovative therapeutic solutions extreme gradient boosting effective therapeutic interventions edge computational techniques drug repurposing process drug repurposing emerges 30 million individuals developing predictive models 143 gene targets gene targets vitro assays ultimately catalyzing systematic prediction several findings risk mitigation random forest promising avenue primarily focused previous investigation nearest neighbors harness cutting endeavor expanded diverse algorithms building upon 75 ),
Tags:	Add Tag No Tags, Be the first to tag this record!

Cannot write session to /tmp/vufind_sessions/sess_icd0j0nhld7v1bcvahg7dk3bfh