Sita Sirisha Madugula

TileBac provides a benchmark dataset for segmenting bacterial cell envelopes in ultralow-dose cryoEM images, showing foundation models outperform convolutional neural networks.

This paper evaluates segmentation models for low- and ultralow-dose cryoEM images, finding that model choice significantly affects downstream quantitative measurements.

The paper identifies tumor-suppressive miRs and their oncogenic gene targets that may drive progression from MASLD to HCC.

The paper develops a facility-scale infrastructure to standardize and analyze large-scale microscopy datasets for machine learning, enabling reproducible and scalable workflows.

This paper develops an AI-driven workflow for automated analysis of bacterial ultrastructure in cryoEM data, enabling rapid and accurate quantification of membrane thickness, flagella length, and bacterial arrangement.

This paper uses machine learning to identify allosteric networks in Cas9, revealing key residues and an electrostatic valley that stabilize its structure and specificity.

CasGen generates novel Cas9 and Cas12 proteins using a transformer-based model with margin-based regularization, achieving structural similarity and functional integrity.

The paper uses machine learning to identify distinguishing features of Cas9 and Cas12 proteins, finding specific tripeptides important for their function.

This paper develops two deep learning models for classifying CRISPR-Cas proteins, achieving high accuracy in distinguishing CAS from non-CAS and sub-classifying CAS9 and CAS12.

NRPreTo predicts Nuclear Receptor proteins and their subfamilies using enhanced features, achieving high performance and identifying 59 novel human NRs.