Artificial Intelligence And Machine Learning Research Articles

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И МАШИННОЕ ОБУЧЕНИЕ В ПЕДИАТРИИ - СОВРЕМЕННЫЕ ИССЛЕДОВАНИЯ И НЕИЗВЕДАННЫЕ ГОРИЗОНТЫ

The scientific article examines the implementation of digital technologies in the field of medicine. This review summarizes current data on the use of artificial intelligence and machine learning (AI-ML) in pediatrics based on global research. Currently, machine learning helps create models for predicting the severity of the condition in children with bronchiolitis, neonatal sepsis, bacterial infections, necrotizing enterocolitis, for screening autism, and internalizing disorders. The review highlights the variety of algorithms, analyzes the main methods with algorithms used in the development of artificial intelligence, and their application depending on specific tasks and requirements. As a result of the work, the relevance and necessity of using intelligent technologies in the modern world was determined. It was also found that despite the serious difficulties in implementing AI-ML systems, the prospects for their use encourage the search for solutions to overcome any obstacles. Highly qualified specialists from different parts of the world are constantly working on the development of this area.

Artificial Intelligence and Machine Learning in Ocular Oncology, Retinoblastoma (ArMOR): Experience with a Multiracial Cohort.

Background: The color variation in fundus images from differences in melanin concentrations across races can affect the accuracy of artificial intelligence and machine learning (AI/ML) models. Hence, we studied the performance of our AI model (with proven efficacy in an Asian-Indian cohort) in a multiracial cohort for detecting and classifying intraocular RB (iRB). Methods: Retrospective observational study. Results: Of 210 eyes, 153 (73%) belonged to White, 37 (18%) to African American, 9 (4%) to Asian, 6 (3%) to Hispanic races, based on the U.S. Office of Management and Budget's Statistical Policy Directive No.15 and 5 (2%) had no reported race. Of the 2473 images in 210 eyes, 427 had no tumor, and 2046 had iRB. After training the AI model based on race, the sensitivity and specificity for detection of RB in 2473 images were 93% and 96%, respectively. The sensitivity and specificity of the AI model were 74% and 100% for group A; 88% and 96% for group B; 88% and 100% for group C; 73% and 98% for group D, and 100% and 92% for group E, respectively. Conclusions: The AI models built on a single race do not work well for other races. When retrained for different races, our model exhibited high sensitivity and specificity in detecting RB and classifying RB.

‘Texting Scarlatti’: unlocking a standard edition with a digital toolkit

Editing musical texts requires a great deal of artistic, intellectual and commercial investment but, once published, an edition tends to become inert, a form of musical still life. ‘Texting Scarlatti’ explores ways of unlocking the value of a standard edition using a variety of digital tools. For as long as the editorial process in music begins and ends with analogue graphical notation, a digital music edition will inevitably be hybrid to some extent. But our project examines each stage in that process from input to output, showing how scholarly insight, performance-based musicology, data science, HCI-inspired crowdsourcing techniques and information systems can together form the basis of an open, rich digital edition for future development. Key outputs will be an extremely large machine-readable dataset consisting of more than 310,000 bars of music, together with structured, searchable apparatus for each of the 555 sonatas in the Scarlatti canon. Musicology has been a notable absentee from recent developments in artificial intelligence and machine learning (AIML) and there is a great deal of catching-up to do. In our conclusion, we urge more scholars and publishers to make similarly large, and largely hidden, datasets more openly available for machine learning and suggest some practical steps to facilitate this.

Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction

BackgroundResearch comparing artificial intelligence and machine learning (AI/ML) methods with classical statistical methods applied to large population health databases is limited. ObjectivesThis retrospective cohort study aimed to compare the predictive performance of AI/ML algorithms against conventional multivariate logistic regression models using linked health administrative data. MethodsUsing Ontario's population health databases, we created a cohort of residents of the city of Ottawa, Ontario, who underwent a PCR test for COVID-19 between March 10, 2020, and May 13, 2021. Using demographic, socio-economic and health data (including COVID-19 PCR test results and available, symptom data), we developed predictive models for the purpose of COVID-19 case identification using the following approaches: classical multivariate logistic regression (LR); deep neural network (DNN); random forest (RF); and gradient boosting trees (GBT). Model performance comparisons were made using the area under the curve (AUC) swarm plot for 10-fold cross-validation. ResultsThe cohort consisted of n = 351,248 Ottawa residents tested for COVID-19 during the study period. Among whom, a total of n = 883,879 unique COVID-19 tests were performed (2.6 % positive test results). Inclusion of COVID-19 symptoms data in the analysis improved model performance and variable predictive value across all tested models (p < 0.0001), with the 10-fold cross-validation AUC increasing to near or over 0.7 in all models when symptoms data were included. In various pairwise comparisons, the GBT method had the highest predictive ability (AUC = 0.796 ± 0.017), significantly outperforming multivariate logistic regression and the other AI/ML approaches. ConclusionsConventional multivariate regression-based models are better than some and worse than other machine learning algorithms to provide good predictive accuracy in a moderate dataset with a reasonable number of features. However, whenever possible, the AI/ML GBT approach should be considered.

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and case study on stomatal patterning.

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Enhancing Customer Engagement through Marketing 5.0: An Empirical Analysis

This study explores the impact of Marketing 5.0 techniques—Artificial Intelligence and Machine Learning (AIML), Big Data Analytics (BDA), Internet of Things (IoT), Augmented Reality and Virtual Reality (ARVR), Blockchain Technology (BT), and Voice Search Optimization (VSO)—on customer engagement (CE). The research employs a regression model with a sample of 250 respondents to examine the correlations and significance between these technologies and CE.The analysis reveals that AIML, BT, and ARVR significantly enhance CE, with AIML and BT showing the strongest positive correlations. Conversely, VSO is associated with a negative impact on CE. The regression model demonstrates an adjusted R-squared value of 0.489, indicating that these predictors explain a substantial portion of the variability in CE. ANOVA results confirm the model's statistical significance, emphasizing the importance of AIML, BT, and ARVR in improving customer interaction and brand involvement. The findings highlight the transformative potential of Marketing 5.0 technologies in delivering personalized and immersive experiences that drive customer loyalty and business growth in a competitive digital landscape. Organizations should strategically integrate AIML, BT, and ARVR into their marketing efforts to enhance customer engagement and gain a competitive edge.

P19-70 Gaps and needs analysis, barriers, opportunities and drivers for implementation of Artificial Intelligence and Machine Learning (AI/ML) tools in regulatory risk assessment

P19-70 Gaps and needs analysis, barriers, opportunities and drivers for implementation of Artificial Intelligence and Machine Learning (AI/ML) tools in regulatory risk assessment

Artificial intelligence in cardiovascular medicine: clinical applications.

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Applied artificial intelligence for global child health: Addressing biases and barriers.

Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.

Trust criteria for artificial intelligence in health: normative and epistemic considerations

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that...

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

MT37 FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape from 1995 to 2023

MT37 FDA-Approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An Updated Landscape from 1995 to 2023

AI/ML-derived mechanistically interpretable whole-genome biomarkers of patient survival in pre-treatment primary neuroblastoma tumors and whole blood.

10043 Background: Prediction, understanding, and management, of neuroblastoma (NBL) outcomes, from spontaneous regression to relapse and death, remain limited, and rely mostly on the International Neuroblastoma Staging System (INSS) stage, age, and the one-gene test for MYCN amplification. The entire multi-ome affects every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) cannot discover effective biomarkers from real-life, small-cohort, multi-omic, and noisy data. Methods: We use our AI/ML to identify two whole-genome biomarkers from open-source pre-treatment NBL patient-matched primary tumor and whole blood DNA copy numbers, and primary tumor mRNA expression. Our data-agnostic, unsupervised algorithms extend the mathematics that underlies quantum mechanics to overcome the limitations of typical AI/ML [1,2]. Our platform- and reference genome-agnostic biomarkers in astrocytoma, including glioblastoma, and lung, ovarian, and uterine adenocarcinomas, were mathematically discovered, and computationally and experimentally validated, repeatedly, in federated, imbalanced datasets from as few as 50–100 patients [3]. Results: We discover the biomarkers in “skinny” datasets of minimally preprocessed ≈3M-bin whole-genome sequences from tumors and blood of a set of 101 patients. Blindly, i.e., label-free, we separate both biomarkers from the normal X chromosome-number variation, demonstrating sex-agnostic learning. Combined, the biomarkers are statistically independent of all standard-of-care indicators, with the univariate Cox hazard ratio of 4.0 (log-rank P-value=2.3×10–5) within the 95% confidence intervals of the bivariate ratios. The risk that the tumor’s whole genome confers upon outcome, as is reflected by the bivariate ratios, is greater than that conferred by all standard-of-care indicators, including DNA ploidy, the Children’s Oncology Group (COG) risk, and the mitosis-karyorrhexis index (MKI), except for histopathology. We validate the biomarkers in ≈10K-bin target-capture sequences of a mutually-exclusive set of 419 patients, demonstrating generalizability as well as site-, platform-, and protocol-agnostic transfer learning. At 73–80% concordance, in both the tumor and blood profiles of both the discovery and validation sets, the biomarkers combined are more accurate than MYCN. We show that the biomarkers identify known and previously unrecognized disease mechanisms and druggable gene alterations, including co-amplification of MYCN with genes encoding for extra-embryonic transcripts, as well as the hijacking of embryonic development toward aneuploidy, which can spontaneously regress via embryonic self-correction. Conclusions: Our AI/ML is uniquely suited for personalized medicine. 1. doi: 10.1063/1.5099268 2. doi: 10.1073/pnas.0530258100 3. doi: 10.1063/1.5142559.

Prospective validation from a retrospective trial that validated an AI/ML-derived whole-genome biomarker as the most accurate and precise predictor of survival and response to treatment in glioblastoma.

e14028 Background: For 70 years, the best indicator of glioblastoma (GBM) survival has remained age at diagnosis. Factors across the entire genome affect every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) would require 3B-patient training sets to generate predictive models from the whole 3B-nucleotide genome. As a result, all other attempts to associate a tumor’s DNA copy-number alterations (CNAs) with the patient’s outcome failed. Methods: A genome-wide pattern of DNA CNAs in primary GBM tumors was recently validated in a retrospective clinical trial as the most accurate and precise predictor of survival and response to treatment [doi: 10.1063/1.5142559 ]. Applicable to the general population, this biomarker, the first to encompass the whole genome, and biomarkers in lung, nerve, ovarian, and uterine cancers, were repeatedly identified in open-source datasets from as few as 50–100 patients by using our data-agnostic unsupervised AI/ML, which extends the mathematics of quantum mechanics to overcome the limitations of typical AI/ML [doi: 10.1063/1.5099268 , 10.1073/pnas.0530258100 ]. Results: At 75–95% concordance, our biomarker is more accurate than and independent of age and all other indicators, including the one-gene tests for MGMT, IDH1, and TERT. Platform- and reference genome-agnostic, the biomarker’s &gt;99% precision is greater than the community consensus of &lt;70% reproducibility. It describes disease mechanisms and identifies drug targets and combinations of targets to sensitize tumors to treatment. Now, in follow-up results from the trial we, first, show correct prospective prediction of the outcome of the five of the 79 patients who were alive four years earlier, at the time of first results (log-rank P-value=3.9×10–2). Two patients, who were predicted to have shorter survival, lived less than five years from diagnosis, whereas of the three patients predicted to have longer survival, one lived more than five, and the remaining two are alive &gt;11.5, years from diagnosis. Second, we demonstrate 100%-precise clinical prediction for the 59/79 patients with remaining tumor DNA, by using whole-genome sequencing in a Clinical Laboratory Improvement Amendments (CLIA)/College of American Pathologists (CAP) laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the biomarker’s univariate Cox hazard ratio of 4.2 and Kaplan-Meier median survival difference of 2.25 years (log-rank P-value=6.0×10–4), is greater than that conferred by the patient’s Karnofsky performance score and access to chemotherapy, and the tumor’s percent resection, and is surpassed only by the patient’s access to radiotherapy. Conclusions: This is a proof of principle that our AI/ML is uniquely suited for personalized medicine.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery.

The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.

ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology

The objective of this Technical Opinion Paper (TOP) is to provide an overview of the research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear Systems) research group at the North Carolina State University. We will discuss the connections between our research with the key items outlined in the Virtual Special Issues (VSI) Nuclear Fission Technology (NFT) series. NFT is crucial for the global goal of decarbonization, but as outlined by the VSI framework, significant challenges remain that inhibit NFT profitability. Our research addresses some of these challenges, centered around improving the simulation of complex nuclear systems with Artificial Intelligence and Machine Learning (AI/ML). The ARTISANS group specializes in many aspects of applications of AI/ML to nuclear engineering, whose potential impact on NFT continues to grow as AI/ML technologies improve.

Updates to the Alliance of Genome Resources central infrastructure.

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.