PotatoG-DKB: a potato gene-disease knowledge base mined from biological literature.

Background: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered across numerous publications. For researchers, obtaining relevant knowledge by reading and organizing a large body of literature is a time-consuming and labor-intensive process. Therefore, systematically extracting and organizing the relationships between potato genes and diseases from the literature to establish a potato gene-disease knowledge base is particularly important. Unfortunately, there is currently no such gene-disease knowledge base available. Methods: In this study, we constructed a Potato Gene-Disease Knowledge Base (PotatoG-DKB) using natural language processing techniques and large language models. We used PubMed as the data source and obtained 2,906 article abstracts related to potato biology, extracted entities and relationships between potato genes and related disease, and stored them in a Neo4j database. Using web technology, we also constructed the Potato Gene-Disease Knowledge Portal (PotatoG-DKP), an interactive visualization platform. Results: PotatoG-DKB encompasses 22 entity types (such as genes, diseases, species, etc.) of 5,206 nodes and 9,443 edges between entities (for example, gene-disease, pathogen-disease, etc.). PotatoG-DKP can intuitively display associative relationships extracted from literature and is a powerful assistant for potato biologists and breeders to understand potato pathogenesis and disease resistance. More details about PotatoG-DKP can be obtained at https://www.potatogd.com.cn/.

Towards safer tower crane operations: An innovative knowledge-based decision support system for automated safety risk assessment.

INTRODUCTION: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved. METHOD: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system's capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors. RESULTS: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations. PRACTICAL APPLICATIONS: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation.

Network meta-analysis on the mechanisms underlying alcohol augmentation of acute pancreatitis and diabetes type II.

BACKGROUND: Pancreatitis is a severe inflammatory pathology that occurs from pancreatic duct and exocrine acinar injury, leading to improper secretion of digestive enzymes, auto-digestion of the pancreas, and subsequent inflammation. Clinical reports show that 60%-90% of pancreatitis patients have a history of chronic alcohol use. More recent studies reveal that exocrine pancreas disorders like acute pancreatitis can precede diabetes type II onset, though mechanisms are not yet fully known. This study identified molecules and key signaling pathways underlying alcohol-induced acute pancreatitis and their effects on diabetes type II onset. METHODS: Data on human peripheral blood samples with or without acute pancreatitis were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (accession number GSE194331). Acute pancreatitis-mediated differentially expressed genes (DEGs) were generated from GSE194331 using CLC Genomics Workbench 12. Molecules associated with ethanol (EtOH), acute pancreatitis, and diabetes type II were collected from QIAGEN Knowledge Base (QKB). The relationship between the molecules and signaling pathways associated with EtOH, acute pancreatitis, or diabetes type II was examined using various Ingenuity Pathway Analysis (IPA) tools. RESULTS: Our investigation showed that acute pancreatitis-mediated DEGs were closely associated with EtOH by revealing that EtOH-induced acute pancreatitis appears to lead to the onset of diabetes type II. We found that diabetes type II onset was mediated by pro-inflammatory and metabolic mechanisms underlying EtOH-induced acute pancreatitis, involving increased expression of cytokines including macrophage migration inhibitory factor (MIF), and decreased expression of hormones such as insulin. CONCLUSIONS: Exposure to alcohol may promote diabetes type II by affecting the activity of key inflammatory and metabolic mediators involved in acute pancreatitis. These findings call for further investigation into the role of pro-inflammatory and metabolic mediators like resistin, IL-6, and insulin in EtOH-induced diabetes type II associated with acute pancreatitis pathologies.

A smart system for student performance assessment (SPA).

The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.

BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care. OBJECTIVE: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care. METHODS: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms. Bibliographic records from their inception to 2022 were retrieved from PubMed/MEDLINE (n=206), Embase (n=101), and ACM Digital Library (n=377), followed by eligibility screening and literature review. The eligibility criteria include empirical studies that (1) developed or tested AI methods, (2) developed or tested CDSS or CDSS components, and (3) focused on pregnancy care. Data of studies used for review and appraisal include title, abstract, keywords, MeSH terms, full text, and supplements. Publications with ancillary information or overlapping outcomes were synthesized as one single study. Reviewers independently reviewed and assessed the quality of selected studies. RESULTS: We identified 30 distinct studies of 684 studies from their inception to 2022. Topics of clinical applications covered AI-augmented CDSS from prenatal, early pregnancy, obstetric care, and postpartum care. Topics of CDSS functions include diagnostic support, clinical prediction, therapeutics recommendation, and knowledge base. CONCLUSIONS: Our review acknowledged recent advances in CDSS studies including early diagnosis of prenatal abnormalities, cost-effective surveillance, prenatal ultrasound support, and ontology development. To recommend future directions, we also noted key gaps from existing studies, including (1) decision support in current childbirth deliveries without using observational data from consequential fetal or maternal outcomes in future pregnancies; (2) scarcity of studies in identifying several high-profile biases from CDSS, including social determinants of health highlighted by the American College of Obstetricians and Gynecologists; and (3) chasm between internally validated CDSS models, external validity, and clinical implementation.

Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exception-tolerant hierarchical knowledge bases (i.e., knowledge bases, where rule-based knowledge is represented on several levels of abstraction), privacy concerns have not been addressed extensively in this context yet. However, privacy plays an important role, especially for medical applications. METHODS: When parts of the original dataset can be restored from a learned knowledge base, there may be a practically and legally relevant risk of re-identification for individuals. In this paper, we study privacy issues of exception-tolerant hierarchical knowledge bases which are learned from data. We propose approaches for determining and eliminating privacy issues of the learned knowledge bases. RESULTS: We present results for synthetic as well as for real world datasets. CONCLUSION: The results show that our approach effectively prevents privacy breaches while only moderately decreasing the inference quality.

Knowledge Base Prototype Creating with Using Interdisciplinary Metathesaurus.

This article presents our experience in development an ontological model can be used in clinical decision support systems (CDSS) creating. We have used the largest international biomedical terminological metathesaurus the Unified Medical Language System (UMLS) as the basis of our model. This metathesaurus has been adapted into Russian using an automated hybrid translation system with expert control. The product we have created was named as the National Unified Terminological System (NUTS). We have added more than 33 million scientific and clinical relationships between NUTS terms, extracted from the texts of scientific articles and electronic health records. We have also computed weights for each relationship, standardized their values and created symptom checker in preliminary diagnostics based on this. We expect, that the NUTS allow solving task of named entity recognition (NER) and increasing terms interoperability in different CDSS.

Development of Unified Terminological System Based on UMLS: Experience and Perspectives.

This article presents experience in construction the National Unified Terminological System (NUTS) with an ontological structure based on international Unified Medical Language System (UMLS). UMLS has been adapted and enriched with formulations from national directories, relationships, extracted from the texts of scientific articles and electronic health records, and weight coefficients.

A Framework to Support the Standardized Management of Real-World Data.

Relying on our experience on the development of data registration and management systems for clinical and biological data coming from patients with hematological malignancies, as well as on the design of strategies for data collection and analysis to support multi-center, clinical association studies, we designed a framework for the standardized collection and transformation of clinically relevant real-world data into evidence, to meet the challenges of gathering biomedical data collected during daily clinical practice in order to promote basic and clinical research.

Design and Development of a Fully Intelligent Nursing Information System Based on International Standard Nursing Terminology.

OBJECTIVE: Design and develop a Clinical Care Classification (CCC) nursing information system aligned with nursing terminology CCC, emphasizing standard procedures and a responsibility-based nursing model to enhance efficiency and quality of care. METHODS: Conduct thorough investigation into clinical nursing informatics needs, analyze existing system shortcomings, utilize Microsoft.net for development, integrate standard nursing procedures and clinical operating protocols into system functions. Structure database based on bed characteristics, implant CCC Nursing Terminology and clinical nursing knowledge base. RESULTS: Successfully design and develop CCC Nursing Information System featuring patient list, nurse assignment, nursing evaluation, diagnosis, goals, plan, interventions, special care, shift handover, record query, workload statistics, and intelligent guidance based on patient assessment and nursing elements. CONCLUSION: The CCC Nursing Information System advances standard nursing procedures in clinical practice, promoting standardization and responsibility-based holistic care. It harnesses big data to enhance system intelligence.

An ontology-based tool for modeling and documenting events in neurosurgery.

BACKGROUND: Intraoperative neurophysiological monitoring (IOM) plays a pivotal role in enhancing patient safety during neurosurgical procedures. This vital technique involves the continuous measurement of evoked potentials to provide early warnings and ensure the preservation of critical neural structures. One of the primary challenges has been the effective documentation of IOM events with semantically enriched characterizations. This study aimed to address this challenge by developing an ontology-based tool. METHODS: We structured the development of the IOM Documentation Ontology (IOMDO) and the associated tool into three distinct phases. The initial phase focused on the ontology's creation, drawing from the OBO (Open Biological and Biomedical Ontology) principles. The subsequent phase involved agile software development, a flexible approach to encapsulate the diverse requirements and swiftly produce a prototype. The last phase entailed practical evaluation within real-world documentation settings. This crucial stage enabled us to gather firsthand insights, assessing the tool's functionality and efficacy. The observations made during this phase formed the basis for essential adjustments to ensure the tool's productive utilization. RESULTS: The core entities of the ontology revolve around central aspects of IOM, including measurements characterized by timestamp, type, values, and location. Concepts and terms of several ontologies were integrated into IOMDO, e.g., the Foundation Model of Anatomy (FMA), the Human Phenotype Ontology (HPO) and the ontology for surgical process models (OntoSPM) related to general surgical terms. The software tool developed for extending the ontology and the associated knowledge base was built with JavaFX for the user-friendly frontend and Apache Jena for the robust backend. The tool's evaluation involved test users who unanimously found the interface accessible and usable, even for those without extensive technical expertise. CONCLUSIONS: Through the establishment of a structured and standardized framework for characterizing IOM events, our ontology-based tool holds the potential to enhance the quality of documentation, benefiting patient care by improving the foundation for informed decision-making. Furthermore, researchers can leverage the semantically enriched data to identify trends, patterns, and areas for surgical practice enhancement. To optimize documentation through ontology-based approaches, it's crucial to address potential modeling issues that are associated with the Ontology of Adverse Events.

Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts.

OBJECTIVES: Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology. MATERIALS AND METHODS: Tables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB. RESULTS: There was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information. DISCUSSION: It is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity. CONCLUSION: The classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.

Span-based few-shot event detection via aligning external knowledge.

Few-shot Event Detection (FSED) aims to identify novel event types in new domains with very limited annotated data. Previous PN-based (Prototypical Network) joint methods suffer from insufficient learning of token-wise label dependency and inaccurate prototypes. To solve these problems, we propose a span-based FSED model, called SpanFSED, which decomposes FSED into two subprocesses, including span extractor and event classifier. In span extraction, we convert sequential labels into a global boundary matrix that enables the span extractor to acquire precise boundary information irrespective of label dependency. In event classification, we align event types with an outside knowledge base like FrameNet and construct an enhanced support set, which injects more trigger information into the prototypical network of event prototypes. The superior performance of SpanFSED is demonstrated through extensive experiments on four event detection datasets, i.e., ACE2005, ERE, MAVEN and FewEvent. Access to our code and data is facilitated through the following link: .

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

Network meta-analysis of the molecular mechanisms and signaling pathways underlying alcohol-induced thymic atrophy.

BACKGROUND: Thymic atrophy is characterized by loss of thymocytes, destruction of thymic architecture, and a subsequent decrease in naïve T cells with compromised immunity. Thymic atrophy occurs during aging. Environmental factors including alcohol misuse also induce thymic atrophy. Despite the link between alcohol misuse and thymic atrophy, the underlying mechanism is understudied. We aimed to identify molecules and signaling pathways that underly alcohol-induced thymic atrophy during aging. METHODS: F344 rats were given 3-day binge-ethanol (4.8 g/kg/day; 52% w/v; i.g.) and the thymus was collected and weighed. Molecular mechanisms underlying ethanol-induced thymic atrophy were investigated by network meta-analysis using the QIAGEN Ingenuity Pathway Analysis (IPA). The molecules associated with ethanol were identified from the QIAGEN Knowledge Base (QKB) and those associated with thymic atrophy were identified from QKB and Mouse Genome Informatics (MGI). Aging-mediated Differential Expression Genes (DEGs) from mouse thymocytes were obtained from the Gene Expression Omnibus (GEO) database (GSE132136). The relationship between the molecules and associated signaling pathways were studied using IPA. RESULTS: Binge-ethanol decreased thymic weight in F344 rats. Our meta-analysis using IPA identified molecules commonly shared by ethanol and thymic atrophy through which simulation with ethanol increased thymic atrophy. We then obtained aging-mediated DEGs from the atrophied thymocytes. We found that ethanol contributed to thymic atrophy through modulation of the aging-mediated DEGs. Our network meta-analysis suggests that ethanol may augment thymic atrophy through increased expression of cytokines (e.g., IL-6, IL-17A and IL-33) along with their regulators (e.g., STAT1 and STAT3). CONCLUSIONS: Exposure to alcohol may augment thymic atrophy by altering the activity of key inflammatory mediators, such as STAT family members and inflammatory cytokines. These findings provide insights into the signaling pathways and upstream regulators that underly alcohol-induced thymic atrophy during aging, suggesting that alcohol consumption could prepone thymic atrophy.

ColPTMScape: An open access knowledge base for tissue-specific collagen PTM maps.

Collagen is a key component of the extracellular matrix (ECM). In the remodeling of ECM, a remarkable variation in collagen post-translational modifications (PTMs) occurs. This makes collagen a potential target for understanding extracellular matrix remodeling during pathological conditions. Over the years, scientists have gathered a huge amount of data about collagen PTM during extracellular matrix remodeling. To make such information easily accessible in a consolidated space, we have developed ColPTMScape (https://colptmscape.iitmandi.ac.in/), a dedicated knowledge base for collagen PTMs. The identified site-specific PTMs, quantitated PTM sites, and PTM maps of collagen chains are deliverables to the scientific community, especially to matrix biologists. Through this knowledge base, users can easily gain information related to the difference in the collagen PTMs across different tissues in different organisms.

Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology.

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.

BACKGROUND: Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems. RESULTS: We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average. CONCLUSIONS: We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.