Subham Choudhury

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

This paper presents a framework for adapting generative neural networks to build kinetic models for different organisms and physiological contexts by exploring latent space.

Impact of phylogeny on the inference of functional sectors from protein sequence data

The paper evaluates how phylogeny affects the identification of functional sectors in protein sequences and shows that two methods, conservation and ICOD, are robust to phylogeny and provide complementary insights.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

This paper introduces RENAISSANCE, a generative machine learning framework that accurately characterizes intracellular metabolic states by parameterizing kinetic models using omics data and experimental observations.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

This paper introduces REKINDLE, a deep learning framework that generates kinetic models of metabolic networks with dynamic properties, demonstrating its efficiency and versatility through three studies.