Predicting Drug-Target Interactions With Multi-Information Fusion

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and prote...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of biomedical and health informatics 2017-03, Vol.21 (2), p.561-572
Hauptverfasser:	Peng, Lihong, Liao, Bo, Zhu, Wen, Li, Zejun, Li, Keqin
Format:	Artikel
Sprache:	eng
Schlagworte:	Adrenergic receptors Algorithms Atropine Chemicals Computational Biology - methods Computer applications Data integration Diffusion tensor imaging Drug discovery Drug Discovery - methods Drug similarity Drugs drug–target interaction (DTI) Humans Lagrange multiplier local correlations among labels of samples Machine Learning Mathematical models multi-information fusion Multisensor fusion Olanzapine Organic chemistry Pharmaceutical Preparations - chemistry Pharmaceutical Preparations - metabolism Principal Component Analysis Principal components analysis Protein engineering Proteins Proteins - chemistry Proteins - metabolism Receptors Receptors (physiology) robust PCA Robustness semi-supervised learning similarities among samples Similarity target similarity
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!