When designing and analyzing a study, researchers must navigate a large space of methodological decisions, or “researcher degrees of freedom.” If these choices are not preregistered or transparently reported, they can increase the risk of inflated false-positive rates and exaggerated effect sizes, undermining scientific credibility. Drawing on psychology research that characterizes these degrees of freedom, we create a protocol for scoring how hypotheses are reported in the HCI literature (ReportDoF). We manually apply ReportDoF to 100 hypotheses from HCI texts authored between 2015-2025, including both preregistrations and papers. Based on this experience, we contribute an LLM workflow and proof-of-concept interactive interface (RDoFlow) that applies ReportDoF to new texts, enabling large-scale analysis of the composition and quality of reported analysis specifications. For example, RDoFlow reveals that HCI research more frequently tests multiple dependent variables for a single hypothesis than psychology research does—a practice that increases the risk of false positives.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

At present, accurate detection of small objects in an aerial imagery remains a challenge in remote sensing due to limited pixel resolution, background clutter, and scale variations. To address these issues for high-precision detection in a complex remote sensing scene, we propose a novel detection framework based on RepViT Dynamic Fusion and YOLOv11, termed RDF-YOLO. The RDF-YOLO brings in two core innovations:(1) a Dynamic Scale RepViT module that integrates lightweight Transformer operations into the backbone to enhance global context modeling and semantic discrimination under noisy conditions, and (2) a dynamic fusion module that incorporates spatially aware dilated convolutions and channel-adaptive fusion strategies to enable flexible, scale-aware feature interaction. Extensive experiments on the challenging AI-TOD dataset show that the RDF-YOLO outperforms state-of-the-art methods by substantial margins. In particular, the RDF-YOLO improves AP50:95 by 6.9% and AP50by 8.6% over the YOLOv11 baseline and on small-object metrics, including APvt, APt, and APs. These results verify the effectiveness of the RDF-YOLO architecture for robust and efficient detection of small objects in remote sensing imagery. The source code is available at https://github.com/AssiiKk/RDF-YOLO.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

Understanding complex diseases hinges on a profound understanding of intricate biomolecular interactions unfolding within a complex, multidimensional landscape, challenging traditional methods to extract meaningful insights. While multi-omics networks capture the richness of biological data, providing a basis for predicting relationships between biomolecules and various phenotypic traits of complex diseases, their inherent complexity limits their predictive power. To address this challenge, we introduce a novel pipeline that leverages the power of Graph Neural Networks (GNNs) to extract and integrate meaningful information from multi-omics networks. By generating informative node embeddings and seamlessly incorporating them into the original subject-level data, our approach captures both local and global network dependencies, leading to substantial improvements in disease prediction accuracy. The proposed pipeline optimizes the embedding generation process for the specific prediction task, enabling the model to learn task-relevant representations. Through rigorous experimentation, we demonstrate the superior performance of our approach, surpassing existing methods by a substantial margin on nine real-world multi-omics datasets. With remarkable increases in accuracy ranging approximately from 8% to 10% over the best-performing baseline, particularly when the multi-omics networks are moderately dense, striking a balance between capturing complex relationships and avoiding excessive noise. Our findings underscore the potential of GNNs to significantly improve disease prediction by effectively extracting and representing knowledge embedded within multi-omics networks.

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY : This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/ .

MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.