publications

2026

1. bioRxiv

   Tangerine: A Python framework for dynamic gene regulation analysis from transcriptomic time series

   Tanmayee Narendra and Gabriele Schweikert

   bioRxiv, 2026

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   Motivation Time-series single-cell transcriptomics enables the study of dynamic gene regulation. However, standard computational tools frequently aggregate temporal data into static, dense topologies, obscuring the precise regulatory rewiring that drives developmental transitions. Further, navigating the inherent noise of statistical inference without losing biological interpretability remains an important bottleneck. Results We present Tangerine, a Python framework for the dynamic reconstruction and interactive exploration of time-varying gene regulatory networks. Tangerine integrates time-constrained metacell aggregation with regularized linear modelling and non-parametric correlation to infer dynamic topologies. To solve the interpretability gap, it features a browser-based visual analytics engine. Tangerine empowers researchers to track macroscopic gene module evolution, interactively filter effect sizes, and link topological rewiring directly to raw transcriptomic evidence. Availability and implementation Tangerine is implemented in Python and Plotly Dash. The code is available on Github at https://github.com/ntanmayee/tangerine.

2025

1. ICML

   Causal discovery from conditionally stationary time series

   Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra, and 4 more authors

   In Forty-second International Conference on Machine Learning, 2025

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   Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully observed variables and/or data from stationary time-series. We develop a causal discovery approach to handle a wide class of nonstationary time series that are conditionally stationary, where the nonstationary behaviour is modeled as stationarity conditioned on a set of latent state variables. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies, with provable identifiablity for the state-dependent causal structures. Empirical experiments on nonlinear particle interaction data and gene regulatory networks demonstrate SDCI’s superior performance over baseline causal discovery methods. Improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method and motivate the use of causality-driven methods for forecasting.

2. NAR Genomics & Bioinformatics

   Towards personalized epigenomics: learning shared chromatin landscapes and joint de-noising of histone modification assays

   Tanmayee Narendra, Giovanni Visonà, Crhistian de Jesus Cardona, and 2 more authors

   NAR Genomics and Bioinformatics, 2025

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   Epigenetic mechanisms enable cellular differentiation and the maintenance of distinct cell types. They enable rapid responses to external signals through changes in gene regulation and their registration over longer time spans. Consequently, the chromatin landscape, which is the overall organization and biochemical state of chromatin, exhibits both cell-type and individual specificity and contributes to phenotypic diversity. Genomic distributions of chromatin features are typically measured using chromatin immunoprecipitation sequencing and related methods. However, these measurements are subject to substantial biases introduced by the chromatin landscape itself. Here, we introduce DecoDen, which uses measurements of several different histone modifications, to simultaneously learn shared chromatin landscapes while de-biasing individual measurement tracks. We demonstrate DecoDen’s effectiveness on an integrative analysis of histone modification patterns across multiple tissues in personal epigenomes. DecoDen is available at https://github.com/ntanmayee/decoden.

2023

1. Nat Comm

   Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning

   Alex Hawkins-Hooker, Giovanni Visonà, Tanmayee Narendra, and 3 more authors

   Nature communications, 2023

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   Epigenetic modifications are dynamic mechanisms involved in the regulation of gene expression. Unlike the DNA sequence, epigenetic patterns vary not only between individuals, but also between different cell types within an individual. Environmental factors, somatic mutations and ageing contribute to epigenetic changes that may constitute early hallmarks or causal factors of disease. Epigenetic modifications are reversible and thus promising therapeutic targets for precision medicine. However, mapping efforts to determine an individual’s cell-type-specific epigenome are constrained by experimental costs and tissue accessibility. To address these challenges, we developed eDICE, an attention-based deep learning model that is trained to impute missing epigenomic tracks by conditioning on observed tracks. Using a recently published set of epigenomes from four individual donors, we show that transfer learning across individuals allows eDICE to successfully predict individual-specific epigenetic variation even in tissues that are unmapped in a given donor. These results highlight the potential of machine learning-based imputation methods to advance personalized epigenomics.

2021

1. US Patent

   Online forum automated feedback generator

   Tanmayee Narendra, Tarun Tater, Srikanth Govindaraj Tamilselvam, and 1 more author

   2021

   US Patent 10,938,752

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   Embodiments describe an approach for automatically generating feedback for an online forum. Embodiments determine if a user is using a solution to a problem, wherein the solution is posted on an online forum, and responsive to determining the user is using the solution, capturing environment information associated with the user’s computing device. Additionally, embodiments determine if the solution solved the problem, and responsive to determining the solution solved the problem, automatically generate feedback associated with the solution, wherein the feedback comprises the environment information and information detailing that the solution solved the problem.

2019

1. BPM Forum

   Counterfactual reasoning for process optimization using structural causal models

   Tanmayee Narendra, Prerna Agarwal, Monika Gupta, and 1 more author

   In International Conference on Business Process Management, 2019

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   Business processes are complex and involve the execution of various steps using different resources that can be shared across various tasks. Processes require analysis and process owners need to constantly look for methods to improve process performance indicators. It is non-trivial to quantify the improvement of a proposed change, without implementing or conducting randomized controlled trials. In several cases, the cost and time for implementing and evaluating the benefits of these changes are high. To address this, we propose a principled framework using Structural Causal Models which formally codify existing cause-effect assumptions about the process, control confounding and answer “what if” questions with observational data. We formally define an end to end methodology which takes process execution logs and specified BPMN model as inputs for structural causal model discovery and for performing counterfactual reasoning. We show that exploiting the process specification for causal discovery automatically ensures the inclusion of subject matter expertise, and also provides an effective computational methodology. We illustrate the effectiveness of our approach by answering intervention and counterfactual questions on example process models.

2018

1. arXiv

   Explaining deep learning models using causal inference

   Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy, and 1 more author

   arXiv preprint arXiv:1811.04376, 2018

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   Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex. In order to establish trust for their widespread commercial use, it is important to formalize a principled framework to reason over these models. In this work, we use ideas from causal inference to describe a general framework to reason over CNN models. Specifically, we build a Structural Causal Model (SCM) as an abstraction over a specific aspect of the CNN. We also formulate a method to quantitatively rank the filters of a convolution layer according to their counterfactual importance. We illustrate our approach with popular CNN architectures such as LeNet5, VGG19, and ResNet32.