9 citations · 31 across the 10 of their papers we have counts for
11 papers
Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling
Vincent Lavelle, Yitan Zhu, Kaitlyn Marlor +2
Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs. How…
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
Alexander Partin, Priyanka Vasanthakumari, Oleksandr Narykov +17
Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question,…
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data
Jamie C. Overbeek, Alexander Partin, Thomas S. Brettin +21
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL)…
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses
Tianshu Feng, Rohan Gnanaolivu, Abolfazl Safikhani +7
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes m…
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…
Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Alexander Partin, Thomas S. Brettin, Yitan Zhu +4
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predi…
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…
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…
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…
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…
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
Austin Clyde, Tom Brettin, Alexander Partin +6
By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance dru…