Bioinformatics reveals the potential mechanisms and biomarkers of necroptosis in neuroblastoma.

Background: Neuroblastoma (NB) is a malignant tumor primarily found in children, presenting significant challenges in its development and prognosis. The role of necroptosis in the pathogenesis of NB has been acknowledged as crucial for treatment. This study aimed to investigate the key genes and functional pathways associated with necroptosis, as well as immune infiltration analysis, in NB. Furthermore, we aimed to evaluate the diagnostic significance of these genes for prognostic assessment and explore their potential immunological characteristics. Methods: The NB dataset (GSE19274, GSE73517, and GSE85047) was obtained from the Gene Expression Omnibus (GEO) database, and genes associated with necroptosis were collected from GeneCards and previous literature. First, we conducted differential expression analysis and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We employed gene set enrichment analysis (GSEA) to identify overlapping enriched functional pathways from the NB dataset. In addition, we constructed a protein-protein interaction (PPI) network, predicting relevant microRNAs (miRNAs) and transcription factors (TFs), as well as their corresponding drug predictions. Furthermore, the diagnostic value was assessed using receiver operating characteristic (ROC) curves. Finally, an immune infiltration analysis was performed. Results: We identified six necroptosis-related differentially expressed genes (NRDEGs) closely associated with necroptosis in NB. They were enriched in Tuberculosis, Apoptosis-multiple species, Salmonella infection, legionellosis, and platinum drug resistance. GSEA and PPI network analyses, along with mRNA-drug interaction network, revealed 38 potential drugs corresponding to BIRC2, CAMK2G, CASP3, and IL8. ROC curve analysis showed that in GSE19274, FLOT2 with area under the ROC curve (AUC) of 0.850 and DAPK1 with AUC of 0.789. Conclusions: Our study elucidates the key genes and functional pathways associated with necroptosis in NB, offering valuable insights to enhance our comprehension of the pathogenesis of NB, and improve prognosis assessment.

Online Mendelian Inheritance in Animals (OMIA): a genetic resource for vertebrate animals.

Online Mendelian Inheritance in Animals (OMIA) is a freely available curated knowledgebase that contains information and facilitates research on inherited traits and diseases in animals. For the past 29 years, OMIA has been used by animal geneticists, breeders, and veterinarians worldwide as a definitive source of information. Recent increases in curation capacity and funding for software engineering support have resulted in software upgrades and commencement of several initiatives, which include the enhancement of variant information and links to human data resources, and the introduction of ontology-based breed information and categories. We provide an overview of current information and recent enhancements to OMIA and discuss how we are expanding the integration of OMIA into other resources and databases via the use of ontologies and the adaptation of tools used in human genetics.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

Transcriptome Profiling of Gene Expression in Atlantic Salmon (Salmo salar) at Early Stage of Development.

For Atlantic salmon development, the most critical phase is the early development stage from egg to fry through alevin. However, the studies investigating the early development of Atlantic salmon based on RNA-seq are scarce and focus only on one stage of development. Therefore, using the RNA-seq technology, the assessment of different gene expressions of various early development stages (egg, alevin, and fry) was performed on a global scale. Over 22 GB of clean data was generated from 9 libraries with three replicates for each stage with over 90% mapping efficiency. A total of 5534 genes were differentially expressed, among which 19, 606, and 826 genes were specifically expressed in each stage, respectively. The transcriptome analysis showed that the number of differentially expressed genes (DEGs) increased as the Atlantic salmon progressed in development from egg to fry stage. In addition, gene ontology enrichment demonstrated that egg and alevin stages are characterized by upregulation of genes involved in spinal cord development, neuron projection morphogenesis, axonogenesis, and cytoplasmic translation. At the fry stage, upregulated genes were enriched in the muscle development process (muscle cell development, striated muscle cell differentiation, and muscle tissue development), immune system (defense response and canonical NF-kappaB signal transduction), as well as epidermis development. These results suggest that the early development of Atlantic salmon is characterized by a dynamic shift in gene expression and DEGs between different stages, which provided a solid foundation for the investigation of Atlantic salmon development.

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.

BACKGROUND: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. CLINICALTRIALS: gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance. RESULTS: In this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, the Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate's accuracy and 90.0% on top 10 candidate's accuracy. CONCLUSION: This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.

Comprehensive microarray analysis of severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub genes, and their related non-coding RNAs.

INTRODUCTION: Preeclampsia (PE) is a serious pregnancy-related complication caused by high blood pressure in pregnant women. The severe form has more devastating effects. According to the growing evidence, the placenta is a crucial component in the pathogenesis of PE, and eliminating it will alleviate symptoms. METHODS: GEO's severe preeclampsia placenta microarray datasets; GSE147776, GSE66273, GSE102897, and GSE10588, were chosen to identify differentially expressed genes (DEGs) in different biological pathways. The analysis of hub genes and related non-coding RNAs was done as well. RESULTS: A total of 347 DEGs with adj p-value <0.05 and ǀlog2FoldChangeǀ> 0.5 were discovered between severe PEs and healthy pregnancies, including 204 over-expressed genes and 143 under-expressed genes. The MCC method identified ISG15, IFI44L, MX2, OAS2, MX1, FN1, LDHA, ITGB3, TKT, HK2 genes as the top ten hub genes. Interactions between hub genes and noncoding RNAs were also conducted. The most enriched pathways were as follows; HIF-1 signaling pathway; Pathways in cancer; Alanine, aspartate and glutamate metabolism; Arginine biosynthesis; Human papillomavirus infection; Glycolysis/Gluconeogenesis; Central carbon metabolism in cancer; Valine, leucine and isoleucine degradation; Cysteine and methionine metabolism; and Galactose metabolism. DISCUSSION: This is a secondary data analysis conducted on severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub-genes, and related noncoding RNAs. Functional studies are crucial to understanding the precise role of these genes in the pathogenesis of PE. Also, accepting a gene as a diagnostic or prognostic marker for early diagnosis and management of PE requires multiple lines of evidence.

A new ontology for numerical cognition: Integrating evolutionary, embodied, and data informatics approaches.

Numerical cognition is a field that investigates the sociocultural, developmental, cognitive, and biological aspects of mathematical abilities. Recent findings in cognitive neuroscience suggest that cognitive skills are facilitated by distributed, transient, and dynamic networks in the brain, rather than isolated functional modules. Further, research on the bodily and evolutionary bases of cognition reveals that our cognitive skills harness capacities originally evolved for action and that cognition is best understood in conjunction with perceptuomotor capacities. Despite these insights, neural models of numerical cognition struggle to capture the relation between mathematical skills and perceptuomotor systems. One front to addressing this issue is to identify building block sensorimotor processes (BBPs) in the brain that support numerical skills and develop a new ontology connecting the sensorimotor system with mathematical cognition. BBPs here are identified as sensorimotor functions, associated with distributed networks in the brain, and are consistently identified as supporting different cognitive abilities. BBPs can be identified with new approaches to neuroimaging; by examining an array of sensorimotor and cognitive tasks in experimental designs, employing data-driven informatics approaches to identify sensorimotor networks supporting cognitive processes, and interpreting the results considering the evolutionary and bodily foundations of mathematical abilities. New empirical insights on the BBPs can eventually lead to a revamped embodied cognitive ontology in numerical cognition. Among other mathematical skills, numerical magnitude processing and its sensorimotor origins are discussed to substantiate the arguments presented. Additionally, an fMRI study design is provided to illustrate the application of the arguments presented in empirical research.

Feasibility of disease terminology systems for mapping orphan conditions and therapeutic indications of designated orphan medicines in the European Union.

BACKGROUND: In the European Union, rare diseases are defined as diseases that affect maximum 5 in 10,000 citizens. These diseases are typically associated with a high unmet medical need. To stimulate development and authorisation of medicines for rare diseases ('orphan conditions'), the European Commission (EC) can grant orphan designations. In order to enable systematic evaluation and communication of the diseases for which designated orphan medicines have (not) been developed and authorised, we aimed to investigate the feasibility of important disease terminology systems for mapping orphan conditions and therapeutic indications. METHODS: We selected all designated orphan medicines that were authorised by the EC during 2022-2023 from the EC's Union Register of medicinal products. For these medicines, we extracted orphan conditions and associated therapeutic indications at initial marketing authorisation. The orphan conditions and separate elements of therapeutic indications such as target disease or condition, severity criteria and target population were assessed for availability in six major disease terminology systems: ICD-10, ICD-11, MedDRA, MeSH, Orphanet nomenclature of rare diseases, and SNOMED CT. Descriptive statistics were used to describe the ability of each disease terminology system to map orphan conditions and elements of therapeutic indications. RESULTS: During 2022-2023, 37 designated orphan medicines were authorised that were designated for 40 orphan conditions (of which 37 unique) and granted 39 therapeutic indications (of which 37 unique). Overall, SNOMED CT covered most descriptions of orphan conditions (33/37, 89%) and target diseases or conditions within therapeutic indications (28/37, 76%). However, when allowing descriptions to be partly included and/or complemented by additional words, SNOMED CT, the Orphanet nomenclature, ICD-11 and MedDRA all had high coverage (92-97%). Other elements than target diseases or conditions within therapeutic indications were mostly lacking. CONCLUSIONS: Regulatory data concerning orphan conditions and therapeutic indications of designated orphan medicines seem to be best covered by SNOMED CT. However, which disease terminology system best facilitates systematic evaluation and communication about development and authorisation of designated orphan medicines also dependents on the specific use case. Given the frequent use of SNOMED CT in healthcare settings, it may also facilitate interoperability between regulatory and healthcare data, while for example ICD-11 may be better suited to generate statistics concerning drug development for rare diseases.

Transcriptional reprogramming post-peripheral nerve injury: A systematic review.

Neuropathic pain is characterised by periodic or continuous hyperalgesia, numbness, or allodynia, and results from insults to the somatosensory nervous system. Peripheral nerve injury induces transcriptional reprogramming in peripheral sensory neurons, contributing to increased spinal nociceptive input and the development of neuropathic pain. Effective treatment for neuropathic pain remains an unmet medical need as current therapeutics offer limited effectiveness and have undesirable effects. Understanding transcriptional changes in peripheral nerve injury-induced neuropathy might offer a path for novel analgesics. Our literature search identified 65 papers exploring transcriptomic changes post-peripheral nerve injury, many of which were conducted in animal models. We scrutinize their transcriptional changes data and conduct gene ontology enrichment analysis to reveal their common functional profile. Focusing on genes involved in 'sensory perception of pain' (GO:0019233), we identified transcriptional changes for different ion channels, receptors, and neurotransmitters, shedding light on its role in nociception. Examining peripheral sensory neurons subtype-specific transcriptional reprograming and regeneration-associated genes, we delved into downstream regulation of hypersensitivity. Identifying the temporal program of transcription regulatory mechanisms might help develop better therapeutics to target them effectively and selectively, thus preventing the development of neuropathic pain without affecting other physiological functions.

Concretizing plan specifications as realizables within the OBO foundry.

BACKGROUND: Within the Open Biological and Biomedical Ontology (OBO) Foundry, many ontologies represent the execution of a plan specification as a process in which a realizable entity that concretizes the plan specification, a "realizable concretization" (RC), is realized. This representation, which we call the "RC-account", provides a straightforward way to relate a plan specification to the entity that bears the realizable concretization and the process that realizes the realizable concretization. However, the adequacy of the RC-account has not been evaluated in the scientific literature. In this manuscript, we provide this evaluation and, thereby, give ontology developers sound reasons to use or not use the RC-account pattern. RESULTS: Analysis of the RC-account reveals that it is not adequate for representing failed plans. If the realizable concretization is flawed in some way, it is unclear what (if any) relation holds between the realizable entity and the plan specification. If the execution (i.e., realization) of the realizable concretization fails to carry out the actions given in the plan specification, it is unclear under the RC-account how to directly relate the failed execution to the entity carrying out the instructions given in the plan specification. These issues are exacerbated in the presence of changing plans. CONCLUSIONS: We propose two solutions for representing failed plans. The first uses the Common Core Ontologies 'prescribed by' relation to connect a plan specification to the entity or process that utilizes the plan specification as a guide. The second, more complex, solution incorporates the process of creating a plan (in the sense of an intention to execute a plan specification) into the representation of executing plan specifications. We hypothesize that the first solution (i.e., use of 'prescribed by') is adequate for most situations. However, more research is needed to test this hypothesis as well as explore the other solutions presented in this manuscript.

A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy.

BACKGROUND: Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems. METHOD: This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient's phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface. RESULTS: Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively). CONCLUSIONS: Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.

Weighted single-step genome-wide association study to reveal new candidate genes for productive traits of Landrace pig in Korea.

The objective of this study was to identify genomic regions and candidate genes associated with productive traits using a total of 37,099 productive records and 6,683 single nucleotide polymorphism (SNP) data obtained from five Great-Grand-Parents (GGP) farms in Landrace. The estimated of heritabilities for days to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) were 0.49, 0.49, 0.56, and 0.23, respectively. We identified a genetic window that explained 2.05%-2.34% for each trait of the total genetic variance. We observed a clear partitioning of the four traits into two groups, and the most significant genomic region for AGE and ADG were located on the Sus scrofa chromosome (SSC) 1, while BF and EMA were located on SSC 2. We conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), which revealed results in three biological processes, four cellular component, three molecular function, and six KEGG pathway. Significant SNPs can be used as markers for quantitative trait loci (QTL) investigation and genomic selection (GS) for productive traits in Landrace pig.

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).

A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data.

Aim: Head and Neck squamous cell carcinoma (HNSCC) is the second most prevalent cancer in Pakistan. Methods: Gene expression data from TCGA and GETx for normal genes to analyze Differentially Expressed Genes (DEGs). Data was further investigated using the Enrichr tool to perform Gene Ontology (GO). Results: Our analysis identified most significantly differentially expressed genes and explored their established cellular functions as well as their potential involvement in tumor development. We found that the highly expressed Keratin family and S100A9 genes. The under-expressed genes KRT4 and KRT13 provide instructions for the production of keratin proteins. Conclusion: Our study suggests that factors such as poor oral hygiene and smokeless tobacco can result in oral stress and cellular damage and cause cancer.

The Cancer Genome Atlas (TCGA) holds vast cancer data processed with powerful computers and cloud tech. This sparks new bioinformatics for better cancer diagnosis, treatment, and prevention. In Southeast Asia, Head and Neck Squamous Cell Carcinoma (HNSCC) is prevalent. We used TCGA and GETx data to study gene expression. High-expression Keratin and S100A9 genes fight cellular damage under stress, while under-expressed KRT4 and KRT13 genes shape cell structure. Poor oral care and smokeless tobacco could induce cell damage, sparking cancer mutations. Unveiling HNSCC mechanisms may guide targeted treatments and preventive strategies.

Scientific plurality and amyotrophic lateral sclerosis (ALS): A philosophical and historical perspective on Charcot's texts.

The history of amyotrophic lateral sclerosis (ALS)-also known as Charcot's disease, Lou Gehrig's disease, and motor neuron disease (MND)-freezes the texts of the scientist and physician Jean-Martin Charcot in a hagiographic narrative describing a brilliant discovery, based on the anatomo-clinical method. This narrative is often used by biologists and physicians as a reference point. This article shows that the use of the hagiographic register faces limitations. In particular, it obscures points of interest from Charcot's texts on ALS, such as the epistemological and ontological implications of scientific plurality in medicine. Although Charcot recognized the importance of scientific plurality in medicine, he prioritized the approaches and conferred the most important epistemic authority on clinical and pathological observations. In his view, animal modeling remains secondary to the understanding of disease. The concept of ALS and its diagnostic operability are the result of symptoms and lesions. By studying the past, we can highlight the specific features of the present. Today, although the ALS concept retains its diagnostic and clinical relevance, it is increasingly called into question in etiological and mechanistic research. Despite these differences, Charcot's reflections are a reminder of the importance of theoretical thinking on scientific plurality, all the more so today in the context of ALS research, in which combining different approaches is increasingly valued to understand the phenotypic and genetic heterogeneity of ALS.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Chemical entity normalization for successful translational development of Alzheimer's disease and dementia therapeutics.

BACKGROUND: Identifying chemical mentions within the Alzheimer's and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection. RESULTS: There were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For the CRAFT corpus, our method outperformed baselines (maximum 78.4%) with a 91.17% accuracy. For our Alzheimer's and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal. CONCLUSION: Use of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer's and dementia.

Cultivation driven transcriptomic changes in the wild-type and mutant strains of Rhodospirillum rubrum.

Purple photosynthetic bacteria (PPB) are versatile microorganisms capable of producing various value-added chemicals, e.g., biopolymers and biofuels. They employ diverse metabolic pathways, allowing them to adapt to various growth conditions and even extreme environments. Thus, they are ideal organisms for the Next Generation Industrial Biotechnology concept of reducing the risk of contamination by using naturally robust extremophiles. Unfortunately, the potential of PPB for use in biotechnology is hampered by missing knowledge on regulations of their metabolism. Although Rhodospirillum rubrum represents a model purple bacterium studied for polyhydroxyalkanoate and hydrogen production, light/chemical energy conversion, and nitrogen fixation, little is known regarding the regulation of its metabolism at the transcriptomic level. Using RNA sequencing, we compared gene expression during the cultivation utilizing fructose and acetate as substrates in case of the wild-type strain R. rubrum DSM 467T and its knock-out mutant strain that is missing two polyhydroxyalkanoate synthases PhaC1 and PhaC2. During this first genome-wide expression study of R. rubrum, we were able to characterize cultivation-driven transcriptomic changes and to annotate non-coding elements as small RNAs.

[Advances in bioinformatics-based protein function prediction].

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

Erratum: Artificial intelligence based data curation: enabling a patient-centric European health data space.

[This corrects the article DOI: 10.3389/fmed.2024.1365501.].