most citedFrom Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations

Souptik Sen, Raneen Younis, Zahra Ahmadi

Multimodal learning seeks to integrate information across diverse sensory sources, yet current approaches struggle to balance cross-modal generalizability with modality-specific st…

cs.LG2026

Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning

Abeer Mostafa, Raneen Younis, Zahra Ahmadi

Spatio-temporal processes often exhibit highly heterogeneous and non-intuitive responses to localized disruptions, limiting the effectiveness of conventional message passing approa…

cs.CL20261 cited

From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation

Raneen Younis, Suvinava Basak, Lukas Chavez +1

The rapid growth of biomedical literature and curated databases has made it increasingly difficult for researchers to systematically connect biomarker mechanisms to actionable drug…

cs.LG2026

Orthogonalized Multimodal Contrastive Learning with Asymmetric Masking for Structured Representations

Carolin Cissee, Raneen Younis, Zahra Ahmadi

Multimodal learning seeks to integrate information from heterogeneous sources, where signals may be shared across modalities, specific to individual modalities, or emerge only thro…

cs.CV2025

Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning

Raneen Younis, Louay Hamdi, Lukas Chavez +1

Whole-slide images are central to digital pathology, yet their extreme size and scarce annotations make self-supervised learning essential. Masked Autoencoders (MAEs) with Vision T…