Biology subjects

Ramsundar, B.

Publications and source records attributed to Ramsundar, B..

4 recordsLinked to original sources

bioRxiv2026

Towards a Physiological Scaling Law: Model Quality vs. Cohort Size for Stochastic Sequence Data

Scaling laws help determine the optimal data size for training large models but are established in domains where the target is deterministic. Physiological signals are different: heartbeat sequences are stochastic, so part of the error is irreducible even with large amounts of data. Metrics such as MAE do not account for non-deterministic behavior, and therefore assessing scaling requires evaluating distributional calibration (measuring how well predicted probability densities capture true conditional characteristics). We formulate a scaling law metric(n) = E + A n- and evaluate it with five metrics: accuracy (MAE, RMSE), distributional calibration (KS distance, goodness-of-fit), and training objective (negative log loss) using a neural temporal point process trained on a cohort of four-ECG datasets. The law fits all five metrics. While point accuracy is near saturation at n = 183, KS distance and goodness-of-fit improve by 6% and 12% respectively when extrapolated to 10,000 subjects, showing that scaling decisions in stochastic domains must be guided by distributional calibration rather than point accuracy.

physiology↗

bioRxiv2026

Lognormal Neural Point Process Models for Interpretable Heartbeat Dynamics

Neural temporal point processes (NTPPs) are powerful tools for modeling sequences of timestamped events with statistical temporal structure. Density-based NTPPs, in particular, are an interesting opportunity to merge the universal function approximation capability of neural networks with a defined statistical model in a way that has many potential applications. We demonstrate one such application to heartbeat dynamics, a physiologic point process. We specifically apply a lognormal mixture NTPP to compute instantaneous estimates of the mean and standard deviation of beat-to-beat intervals. We compare our results to the state of art (Barbieri et al.) point process model for heartbeat dynamics, which uses a more physiologically rigorous inverse Gaussian model. We find that the NTPP model maintains reasonable accuracy while improving upon robustness to noise.

physiology↗

bioRxiv2026

Evaluating Long-Range Temporal Structure in Foundation Model-Based Forecasts of Heartbeat Dynamics

We examine the long-range temporal structure of forecasts produced by Time-Series Foundation Models (TSFMs) on heartbeat dynamics using the MIT-BIH Normal Sinus Rhythm Database (NSRDB). Our findings indicate that these models do not adequately capture long-range dependencies, as reflected in growing errors in RR-interval predictions over longer forecast horizons. Code is available at https://github.com/SubramanianLab/ecg-tsfm-benchmark.

bioinformatics↗

bioRxiv2026

ProteomeScan: A Toolkit For Target Validation By Proteome-Wide Docking And Analysis

The problem of identifying which protein target a potential drug-like molecule interacts with is crucial for both the study of existing drugs and the design of new therapeutic compounds. Despite the importance of target identification, existing computational approaches remain limited in terms of speed, accuracy, and protein target coverage. We introduce ProteomeScan, a large-scale, gene-driven computational toolkit for systematic proteome-wide scanning to uncover hidden or previously uncharacterized protein-ligand interactions. ProteomeScan leverages cloud-scale high performance computing to perform extensive molecular docking simulations across the human proteome to rank candidate targets based on binding affinities. After filtering promiscuous targets, we found that ProteomeScan ranks known target significantly better than a random baseline for a set of control compounds. Furthermore, we performed physical analyses of predicted binding modes for both promiscuous and known protein-ligand binding pairs to validate that ProteomeScan identifies interactions with valid binding pockets. In addition, we conducted experiments using mutant variants of proteins to study how mutations affect binding behavior. We have open sourced the core ProteomeScan algorithm as part of the DeepChem ecosystem to enhance transparency and reproducibility. Author summary

bioinformatics↗