activity
20202023
most citedA cross-study analysis of drug response prediction in cancer cell lines

9 citations · 27 across the 5 of their papers we have counts for

collaborators

6 papers

q-bio.QM2023

Influencing factors on false positive rates when classifying tumor cell line response to drug treatment

Priyanka Vasanthakumari, Thomas Brettin, Yitan Zhu +6

Informed selection of drug candidates for laboratory experimentation provides an efficient means of identifying suitable anti-cancer treatments. The advancement of artificial intel…

cs.AI2023★ 8 cited

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…

q-bio.QM2022★ 2 cited

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Alexander Partin, Thomas Brettin, Yitan Zhu +7

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually bett…

q-bio.QM2021★ 9 cited

A cross-study analysis of drug response prediction in cancer cell lines

Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…

q-bio.QM2020

Learning Curves for Drug Response Prediction in Cancer Cell Lines

Alexander Partin, Thomas Brettin, Yvonne A. Evrard +9

Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As…

q-bio.QM2020★ 8 cited

Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response

Yitan Zhu, Thomas Brettin, Yvonne A. Evrard +6

Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant…