Explore the vast range of titles available.

The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity. A whole-genome alignment of 240 phylogenetically diverse species of eutherian mammal—including 131 previously uncharacterized species—from the Zoonomia Project provides data that support biological discovery, medical research and conservation.

A new collection highlights the role of co-production in strengthening health systems

Doreen Tembo and colleagues argue that small changes as well as larger system-wide changes can strengthen citizens’ contribution to knowledge in health research

Background Knowledge discovery in scientific literature is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential to employ automated methods for knowledge extraction and processing. Finding the right balance between the level of supervision and the effectiveness of models poses a significant challenge. While supervised techniques generally result in better performance, they have the major drawback of demanding labeled data. This requirement is labor-intensive, time-consuming, and hinders scalability when exploring new domains. Methods and Results In this context, our study addresses the challenge of identifying semantic relationships between biomedical entities (e.g., diseases, proteins, medications) in unstructured text while minimizing dependency on supervision. We introduce a suite of unsupervised algorithms based on dependency trees and attention mechanisms and employ a range of pointwise binary classification methods. Transitioning from weakly supervised to fully unsupervised settings, we assess the methods’ ability to learn from data with noisy labels. The evaluation on four biomedical benchmark datasets explores the effectiveness of the methods, demonstrating their potential to enable scalable knowledge discovery systems less reliant on annotated datasets. Conclusion Our approach tackles a central issue in knowledge discovery: balancing performance with minimal supervision which is crucial to adapting models to varied and changing domains. This study also investigates the use of pointwise binary classification techniques within a weakly supervised framework for knowledge discovery. By gradually decreasing supervision, we assess the robustness of these techniques in handling noisy labels, revealing their capability to shift from weakly supervised to entirely unsupervised scenarios. Comprehensive benchmarking offers insights into the effectiveness of these techniques, examining how unsupervised methods can reliably capture complex relationships in biomedical texts. These results suggest an encouraging direction toward scalable, adaptable knowledge discovery systems, representing progress in creating data-efficient methodologies for extracting useful insights when annotated data is limited.

Replication of research findings is critical to the progress of scientific understanding. Accordingly, most scientific journals require authors to report experimental procedures in sufficient detail for independent researchers to replicate their work. To what extent do research reports in the functional neuroimaging literature live up to this standard? The present study evaluated methods reporting and methodological choices across 241 recent fMRI articles. Many studies did not report critical methodological details with regard to experimental design, data acquisition, and analysis. Further, many studies were underpowered to detect any but the largest statistical effects. Finally, data collection and analysis methods were highly flexible across studies, with nearly as many unique analysis pipelines as there were studies in the sample. Because the rate of false positive results is thought to increase with the flexibility of experimental designs, the field of functional neuroimaging may be particularly vulnerable to false positives. In sum, the present study documented significant gaps in methods reporting among fMRI studies. Improved methodological descriptions in research reports would yield significant benefits for the field. ► This study evaluated methods reporting practices across 241 recent fMRI articles. ► Few studies reported sufficient methodological detail for independent replication. ► Methods were highly variable across studies, increasing the risk of false positives. ► Widespread adoption of reporting guidelines would improve fMRI research.

Background There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery. Main body We propose a novel biomedical neural search service called ‘VAIV Bio-Discovery’, which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25. Conclusion As a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and easy to miss knowledge necessary to advance their research. LBD can facilitate hypothesis testing and generation and thus accelerate scientific progress. Neural networks have demonstrated improved performance on LBD-related tasks but are yet to be applied to it. We propose four graph-based, neural network methods to perform open and closed LBD. We compared our methods with those used by the state-of-the-art LION LBD system on the same evaluations to replicate recently published findings in cancer biology. We also applied them to a time-sliced dataset of human-curated peer-reviewed biological interactions. These evaluations and the metrics they employ represent performance on real-world knowledge advances and are thus robust indicators of approach efficacy. In the first experiments, our best methods performed 2-4 times better than the baselines in closed discovery and 2-3 times better in open discovery. In the second, our best methods performed almost 2 times better than the baselines in open discovery. These results are strong indications that neural LBD is potentially a very effective approach for generating new scientific discoveries from existing literature. The code for our models and other information can be found at: https://github.com/cambridgeltl/nn_for_LBD.

Document-level biomedical relation extraction (BioDocuRE) is essential for biomedical knowledge discovery, as many factual relationships between biomedical entities span multiple sentences or even the entire document. Despite recent advances, existing approaches often overlook the comprehensive integration of external domain knowledge and fail to fully exploit the rich multi-granular structural and contextual information inherent in biomedical documents, thereby limiting their reasoning capacity and extraction accuracy for complex, long-range relations. Here, we introduce KnowFDI, a novel framework for document-level biomedical relation extraction that systematically combines local and global contextual information, explicit document structural features (including inter-sentence relations via bridge nodes), and external entity-centric domain knowledge. KnowFDI leverages a pre-trained language model for contextual encoding and employs multi-view representation learning augmented by a two-stage channel-wise attention fusion module to dynamically integrate these diverse information sources. Furthermore, descriptive knowledge from external biomedical knowledge bases is incorporated to enrich entity semantics and enhance relation inference robustness. We evaluate KnowFDI on two widely-used benchmarks, CDR and GDA. Experimental results demonstrate that KnowFDI achieves state-of-the-art performance, with significant and consistent improvements in overall and particularly inter-sentence relation extraction tasks, outperforming previous methods. Ablation studies confirm the necessity and combined efficacy of our hierarchical information fusion design and external knowledge integration, highlighting their crucial roles in deciphering complex document-level dependencies.

Co-production needs to become an integral part of the training and funding of researchers to ensure research meets everyone’s needs, argue David Beran and colleagues

Compared to traditional intermediate concepts, specific bioinformatics entities are more informative and higher directional. This study is based on the BITOLA system and combines bioinformatics methods to determine the intermediate concept which is key to improve efficiency of Literature-based Knowledge Discovery, proposes the concept of “Swanson framework + Bioinformatics”, and conducts practice of Literature-based Knowledge Discovery to improve the scientificity and efficiency of research and development. Firstly, detected the disease related genes (i.e. differentially expressed genes) according to the results of gene functional analysis as intermediate concepts to carry out Literature-based Knowledge Discovery. Taking the disease “Autism Spectrum Disorder (ASD)” as an example, the potential “disease-drug” association was predicted, and the predicted drugs were verified from the perspective of bioinformatics. Two drugs potentially associated with ASD were found: Fish oil and Forskolin, which were closely related to ASD in bioinformatics analysis results and literature verification. The two “disease-drug” association results showed better scientificity. The BIOINF-ABC + model improves the accuracy of calculations by 76% compared to using the BITOLA system alone. In addition, it also shows high accuracy and credibility in literature verification. The BIOINF-ABC + model based on the “Swanson framework + Bioinformatics” has good practicality, applicability, and accuracy in conducting “disease-drug” association prediction in the biomedical field, and can be used for mining “disease-drug” relationships.