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Abstract We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammalian cells in real time through the collection of electrical impedance data, has historically been used to study one cell line at a time. However, we show that if applied to data from multiple cell lines, ECIS can be used to classify unknown or potentially mislabeled cells, factors which have previously been associated with the reproducibility crisis in the biological literature. We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Most notably, our analysis enriches the current field by making use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency; using classification methods to distinguish multiple cell lines, rather than simple statistical tests that compare only two cell lines; and assessing a range of features derived from ECIS data based on their classification performance. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample predictive accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.
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This content will become publicly available on November 1, 2026
SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models
Abstract Synergistic drug combinations are often used to treat cancer. Experimental exploration of all possibilities is expensive. Deep learning (DL) offers a potential alternative for predicting drug pair synergy in specific cell lines. However, current methods often suffer from data leakage and lack systematic ablation studies. We propose SynVerse, a comprehensive evaluation framework featuring four data-splitting strategies to assess DL model generalizability and three ablation studies: module-based, feature shuffling, and a novel network-based approach to disentangle factors influencing performance. We evaluated sixteen models incorporating eight drug- and cell line-specific features, five preprocessing techniques, and two encoders. Our analysis revealed that no model outperformed a baseline using one-hot encoding. Biologically meaningful drug or cell line features and drug-drug interactions did not drive predictive performance. All models showed poor generalization to unseen drugs and cell lines. SynVerse highlights the need for substantial improvements before computational predictors can reliably support experimental and clinical settings.
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- Award ID(s):
- 2233967
- PAR ID:
- 10679434
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Briefings in Bioinformatics
- Volume:
- 26
- Issue:
- 6
- ISSN:
- 1467-5463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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