5 papers
MERLIN-SUITE: Probabilistic modular GRN inference from multi-omics data integrating regulatory priors and transcription factor activity
Suvojit Hazra, Marina Kotvanova, Kirstan Gimse +1
Accurately reconstructing gene regulatory networks (GRNs) is essential for understanding transcriptional processes in development and disease. MERLIN-SUITE (https://github.com/Roy-…
scMTNI: Leveraging cellular trajectory and context to infer dynamic GRNs from single-cell multi-omics data
Suvojit Hazra, Chandrani Kumari, Sushmita Roy
Transcriptional gene regulatory networks (GRNs) depict the directed relationships between regulators and target genes, determining gene expression patterns in a cell-type-specific…
Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks
Junha Shin, Spencer Halberg-Spencer, Yuda Liu +3
Gene regulatory networks (GRNs) define the regulatory relationships among molecules such as transcription factors, chromatin remodelers, and target genes. GRNs play a critical role…
The Local Subtraction Approach For EEG and MEG Forward Modeling
Malte B. Höltershinken, Pia Lange, Tim Erdbrügger +7
EDIT: A revised version of this article has been published in the SIAM Journal on Scientific Computing, see https://epubs.siam.org/doi/full/10.1137/23M1582874. In the revised versi…
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting
Serbülent Ãnsal, Sinem Ãzdemir, Bünyamin Kasap +4
In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings.…