scholarly journals Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 7381-7393 ◽  
Author(s):  
Turki Turki ◽  
Zhi Wei ◽  
Jason T. L. Wang
Keyword(s):  
Multiple Myeloma ◽  
Transfer Learning ◽  
Drug Sensitivity ◽  
Learning Approaches ◽  
Drug Sensitivity Prediction

scholarly journals A clinically relevant in vivo zebrafish model of human multiple myeloma to study preclinical therapeutic efficacy

Blood ◽  
2016 ◽  
Vol 128 (2) ◽  
pp. 249-252 ◽  
Author(s):  
Jianhong Lin ◽  
Weihong Zhang ◽  
Jian-Jun Zhao ◽  
Ariel H. Kwart ◽  
Chun Yang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Therapeutic Efficacy ◽  
Drug Sensitivity ◽  
Xenograft Model ◽  
Zebrafish Model ◽  
Zebrafish Larvae ◽  
Key Points ◽  
Drug Sensitivity Prediction ◽  
Human Multiple Myeloma

Key Points MM cell lines and primary MM cells can be engrafted and grown in vivo in Casper zebrafish larvae. The zebrafish MM in vivo xenograft model can be used as a pretreatment drug-sensitivity prediction platform for MM patients.

scholarly journals Application of transfer learning for cancer drug sensitivity prediction

2018 ◽  
Vol 19 (S17) ◽  
Author(s):  
Saugato Rahman Dhruba ◽  
Raziur Rahman ◽  
Kevin Matlock ◽  
Souparno Ghosh ◽  
Ranadip Pal
Keyword(s):  
Transfer Learning ◽  
Drug Sensitivity ◽  
Cancer Drug ◽  
Drug Sensitivity Prediction

scholarly journals Deciphering the signaling network of breast cancer improves drug sensitivity prediction

Cell Systems ◽  
2021 ◽  
Author(s):  
Marco Tognetti ◽  
Attila Gabor ◽  
Mi Yang ◽  
Valentina Cappelletti ◽  
Jonas Windhager ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Sensitivity ◽  
Signaling Network ◽  
Drug Sensitivity Prediction

scholarly journals A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144490 ◽  
Author(s):  
Saad Haider ◽  
Raziur Rahman ◽  
Souparno Ghosh ◽  
Ranadip Pal
Keyword(s):  
Random Forests ◽  
Drug Sensitivity ◽  
Drug Sensitivity Prediction

scholarly journals A Meta-Learning Approach for Custom Model Training

2019 ◽  
Vol 33 ◽  
pp. 9937-9938
Author(s):  
Amir Erfan Eshratifar ◽  
Mohammad Saeed Abrishami ◽  
David Eigen ◽  
Massoud Pedram
Keyword(s):  
Transfer Learning ◽  
Large Data ◽  
Learning Approaches ◽  
Target Task ◽  
Training Approach ◽  
Starting Point ◽  
Meta Learning ◽  
Future Task ◽  
Model Training ◽  
Training Examples

Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. when there are only a few classes and training examples available in the target task), meta-learning approaches that optimize for future task learning have outperformed the typical transfer approach of initializing model weights from a pretrained starting point. But as we experimentally show, metalearning algorithms that work well in the few-class setting do not generalize well in many-shot and many-class cases. In this paper, we propose a joint training approach that combines both transfer-learning and meta-learning. Benefiting from the advantages of each, our method obtains improved generalization performance on unseen target tasks in both few- and many-class and few- and many-shot scenarios.

scholarly journals Feature selection strategies for drug sensitivity prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Krzysztof Koras ◽  
Dilafruz Juraeva ◽  
Julian Kreis ◽  
Johanna Mazur ◽  
Eike Staub ◽  
...  
Keyword(s):  
Feature Selection ◽  
Drug Sensitivity ◽  
Selection Strategies ◽  
Drug Sensitivity Prediction
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