activity
20232026
most citedSpectral Manifold Harmonization for Graph Imbalanced Regression

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

collaborators

6 papers

cs.LG2026

Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models

Brenda Nogueira, Gisela A. Gonzalez-Montiel, Nitesh V. Chawla +1

Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative mod…

cs.HC2025

From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews

Brenda Nogueira, Werner Geyer, Andrew Anderson +4

Large Language Models (LLMs) are increasingly embedded in academic writing practices. Although numerous studies have explored how researchers employ these tools for scientific writ…

cs.LG2025

SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression

Brenda Nogueira, Gisela A. Gonzalez-Montiel, Meng Jiang +2

Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average error minimization approaches underp…

cs.LG20251 cited

Spectral Manifold Harmonization for Graph Imbalanced Regression

Brenda Nogueira, Gabe Gomes, Meng Jiang +2

Graph-structured data is ubiquitous in scientific domains, where models often face imbalanced learning settings. In imbalanced regression, domain preferences focus on specific targ…

cs.SI2023

Dynamics of Fisheries in the Azores Islands: A Network Analysis Approach

Brenda Nogueira, Ana Torres, Nuno Moniz +1

In the context of the global seafood industry, the Azores archipelago (Portugal) plays a pivotal role due to its vast maritime domain. This study employs complex network analysis t…

cs.LG2023

Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores

Brenda Nogueira, Gui M. Menezes, Nuno Moniz +1

Fishery analysis is critical in maintaining the long-term sustainability of species and the livelihoods of millions of people who depend on fishing for food and income. The fishing…