BACKGROUND: Personalized and precision medicine aim to identify predictive biomarkers from patient-specific proteogenomic profiles and to uncover tailored therapeutic strategies by targeting deregulated proteins driving disease phenotypes. METHODS: To address these challenges, we developed MultiOmicsXplorer, a freely available and interactive R-based Shiny application designed to facilitate the exploration and analysis of multi-omics cancer datasets with a user-friendly interface. At its core, MultiOmicsXplorer builds on our recently developed SignalingProfiler pipeline to extract protein activities from proteogenomic data, thereby reducing data complexity and dimensionality while enabling mechanistic hypothesis generation. RESULTS: The application integrates two core functionalities. The first, OncoXplorer, enables the systematic inference and comparison of protein activities from 1492 samples corresponding to approximately 1000 patients across ten cancer types, leveraging harmonized datasets from the CPTAC portal to provide a functional and mechanistic interpretation of multi-layered data. Importantly, beyond activity estimation, it allows for comparative analysis of transcriptomic, proteomic, and phosphoproteomic data. The second module, Extract Protein Activity from Your Data, enables users to infer the activity of kinases, phosphatases, and transcription factors from custom multi-modal datasets. CONCLUSIONS: Overall, MultiOmicsXplorer facilitates the exploration and interpretation of CPTAC multi-omics cancer datasets, supporting comparative analyses and protein activity inference within a unified and user-friendly environment.

SUMMARY: We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. AVAILABILITY AND IMPLEMENTATION: NetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons, and deposited in Zenodo at https://doi.org/10.5281/zenodo.14719118.

SUMMARY: SIGNORApp is a Cytoscape 3 (3.8 and later) application that provides access to causal interactions annotated in the SIGNOR resource. The application builds networks that can be represented as weighted, signed, directed graphs, where nodes are interacting biological entities and edges represent causal interactions captured by expert curators from experiments reported in peer reviewed journals. Users can query the SIGNOR dataset with (i) single or multiple entity name(s) or identifier(s) and optionally they may require to include in the output network their interacting partners, (ii) browse pathways that are annotated in the SIGNOR resource and (iii) extract the entire causal interactome. The app offers two visualizations modes: one only displaying entity interactions and a second emphasizing the post-translational modifications occurring as a consequence of the interaction. In addition, users can click on nodes and edges to access entity and interaction annotations. Causal information is available for three model organisms: Homo sapiens, Mus musculus and Rattus norvegicus. AVAILABILITY AND IMPLEMENTATION: SIGNORApp has been developed for Cytoscape 3 (3.8 and later) in the Java programming language. The latest source code and the plugin can be found at: https://github.com/SIGNORcysAPP/signor-app and https://apps.cytoscape.org/apps/signorapp, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. RESULTS: Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. AVAILABILITY AND IMPLEMENTATION: The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. RESULTS: Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. AVAILABILITY AND IMPLEMENTATION: PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested. RESULTS: A User Advisory Group (UAG) actively participated in the IAT design and assessment. The task focused on gene normalization (identifying gene mentions in the article and linking these genes to standard database identifiers), gene ranking based on the overall importance of each gene mentioned in the article, and gene-oriented document retrieval (identifying full text papers relevant to a selected gene). Six systems participated and all processed and displayed the same set of articles. The articles were selected based on content known to be problematic for curation, such as ambiguity of gene names, coverage of multiple genes and species, or introduction of a new gene name. Members of the UAG curated three articles for training and assessment purposes, and each member was assigned a system to review. A questionnaire related to the interface usability and task performance (as measured by precision and recall) was answered after systems were used to curate articles. Although the limited number of articles analyzed and users involved in the IAT experiment precluded rigorous quantitative analysis of the results, a qualitative analysis provided valuable insight into some of the problems encountered by users when using the systems. The overall assessment indicates that the system usability features appealed to most users, but the system performance was suboptimal (mainly due to low accuracy in gene normalization). Some of the issues included failure of species identification and gene name ambiguity in the gene normalization task leading to an extensive list of gene identifiers to review, which, in some cases, did not contain the relevant genes. The document retrieval suffered from the same shortfalls. The UAG favored achieving high performance (measured by precision and recall), but strongly recommended the addition of features that facilitate the identification of correct gene and its identifier, such as contextual information to assist in disambiguation. DISCUSSION: The IAT was an informative exercise that advanced the dialog between curators and developers and increased the appreciation of challenges faced by each group. A major conclusion was that the intended users should be actively involved in every phase of software development, and this will be strongly encouraged in future tasks. The IAT Task provides the first steps toward the definition of metrics and functional requirements that are necessary for designing a formal evaluation of interactive curation systems in the BioCreative IV challenge.

BACKGROUND: The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data. RESULTS: The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms. CONCLUSION: The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.

BACKGROUND: Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. RESULTS: A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%. CONCLUSIONS: The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.