Breast cancer, the most diagnosed cancer among women, demands accurate diagnosis for effective treatment. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) provides detailed spatial insights into tissue characteristics, making it essential for tumor analysis. However, segmenting circumscribed mass and diffuse non-mass lesions remains a challenge, as existing solutions often rely on single-context images or multiscale approaches that overlook the imaged structural organization of breast anatomy. To address these gaps, we propose VENUS, a multiscale image and feature attention-based network for breast tumor segmentation in DCE-MRI. Inspired by physicians’ image inspection routine, it utilizes a multiscale encoder with Convolutional Feature Fusion Blocks utilizing early fusion to combine full-breast views with detailed single-breast zoom-ins and improve cross-context semantic modeling ability. A novel attention-based decoder with Attention Gating enhances skip connections by prioritizing critical features for accurate reconstruction. Experiments reveal significant performance gains of 11.05% and 15.07% Dice Similarity Coefficient over single-context state-of-the-art methods on clinical and public DCE-MRI datasets, respectively.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.

MOTIVATION: Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets. This is all the more important, since experimental investigation of potential gene candidates is an expensive task, thus not always a feasible option. On the other hand, many sources of biological information exist beyond the interactome and an important research direction is the design of effective techniques for their integration. RESULTS: In this work, we introduce the Biological Random Walks (BRW) approach for disease gene prioritization in the human interactome. The proposed framework leverages multiple biological sources within an integrated framework. We perform an extensive, comparative study of BRW's performance against well-established baselines. AVAILABILITY AND IMPLEMENTATION: All codes are publicly available and can be downloaded at https://github.com/LeoM93/BiologicalRandomWalks. We used publicly available datasets, details on their retrieval and preprocessing are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candidates by interactome-based algorithms, but that appear to be involved in/or potentially related to breast cancer, according to a functional analysis based on recent literature.