Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery

no code implementations • 31 Oct 2022 • Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin, Da Shi, Brian J. Bennion, Xiaohua Zhang, Felice C. Lightstone

In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at drug discovery.

Drug Discovery Uncertainty Quantification

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

no code implementations • 18 Apr 2021 • Fangfang Xia, Jonathan Allen, Prasanna Balaprakash, Thomas Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith Cohn, James Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric Stahlberg, Justin M. Wozniak, Hyunseung Yoo, George Zaki, Yitan Zhu, Rick Stevens

To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI.

BIG-bench Machine Learning Drug Response Prediction

Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency

no code implementations • 30 Jan 2019 • Ya Ju Fan, Jonathan E. Allen, Sam Ade Jacobs, Brian C. Van Essen

With the trained autoencoder, we generate latent representations of a small dataset, containing pairs of normal and cancer cells of various tumor types.

Dimensionality Reduction

Autoencoder Node Saliency: Selecting Relevant Latent Representations

1 code implementation • 21 Nov 2017 • Ya Ju Fan

The autoencoder is an artificial neural network model that learns hidden representations of unlabeled data.