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PubMed1978

A survey of computer search service costs in the academic health sciences library.

The Norris Medical Library, University of Southern California, has recently completed an extensive survey of costs involved in the provision of computer search services beyond vendor charges for connect time and printing. In this survey costs for such items as terminal depreciation, repair contract, personnel time, and supplies are analyzed. Implications of this cost survey are discussed in relation to planning and price setting for computer search services.

California↗
PubMed1979

Government regulations and other influences on the medical use of computers.

This paper presents points brought out in a panel discussion held at the 12th Hawaiian International Conference on System Sciences, January 1979. The session was attended by approximately two dozen interested parties from various segments of the academic, government, and health care communities. The broad categories covered include the specific problems of government regulations and their impact on specific clinical information systems installed at The University of Texas Health Science Center at Dallas, opportunities in a regulated environment, problems in a regulated environment, vendor-related issues in the marketing and manufacture of computer-based information systems, rational approaches to government control, and specific issues related to medical computer science.

Computers↗
PubMed2025

Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

The study of cancer-associated microbiomes has gained significant attention in recent years, spurred by advances in high-throughput sequencing and metagenomic analysis. Microbiome research holds promise for identifying noninvasive biomarkers and possibly new paradigms for cancer treatment. In this review, we explore the key computational challenges and opportunities in analyzing cancer-associated microbiomes (in tumor/normal tissues and other body sites, e.g., gut, oral, and skin), focusing on sequencing-driven strategies and associated considerations for taxonomic and functional characterization. The discussion covers the strengths and limitations of current analysis tools for identifying contamination, determining compositional bias, and resolving species and strains, as well as the statistical, metabolic, and network inferences that are essential to uncover host-microbiome interactions. Several key considerations are required to guide the choice of databases used for metagenomic analysis in such studies. Recent advances in spatial and single-cell technologies have provided insights into cancer-associated microbiomes, and Artificial Intelligence-driven protein function prediction might enable rapid advances in this field. Finally, we provide a perspective on how the field can evolve to manage the ever-growing size of datasets and generate robust and testable hypotheses. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗
PubMed1992

The integrated health-care system: reflection and projection.

This paper presents a personal view of the development of integrated, comprehensive health-care systems in the United States. The influence of Federal legislation is described, beginning with the 1950-60s policy objective of Hill-Burton Program administrators to create a number of community-based regional medical centers, each consisting of a range of health services organized by and around community hospitals. Later variations of the concept appeared in such programs as Medicare-Medicaid, Comprehensive Health Planning, the Regional Medical Programs, and the new Agency for Health Care Policy and Research. Based on the cumulative experience of the past, the economic, professional and social climate of the present, with its increasing involvement of the patient/payer/consumer in decisions, and the enhanced inter-organizational coordination emerging from technologies of computers and communication science, the goal of creating comprehensive integrated systems as conceived in the 1950s and 60s may finally be achieved in the 1990s, but in a different form from that envisioned earlier. By judicious exploitation of computer and communication capabilities and the massive knowledge bases evolving from research, the way is eased for patient-centered integration and coordination of services without demanding integration in the sense of ownership or formal control of all the providers within a central organization.

Comprehensive Health Care↗
PubMed2026

Integrative Multiomics and Drug Sensitivity Profiling Reveal Potential Biomarkers and Therapeutic Strategies in Pediatric Solid Tumors.

UNLABELLED: Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors-such as high-risk group 3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas, and high-grade gliomas-and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities and biomarkers and to unravel molecular mechanisms associated with drug response profiles for clinical translation. In this study, we performed a multiomics analysis using drug sensitivity profiles, as well as genomic and transcriptomic data, of 81 pediatric solid tumor samples. The integrative analysis suggested two multiomics signatures associated with drug sensitivity. One signature distinguished neuroblastoma samples with sensitivity to navitoclax, a BCL2 family inhibitor. A second signature was specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation that displayed high expression of MGAM, PTPN14, STAT4, and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis suggested possible molecular interactions between MEK inhibitors and the SIX1 mutation in Wilms tumor samples. In conclusion, the integration of drug sensitivity profiling and multiomics data revealed potential biomarkers that may be associated with drug sensitivity in pediatric solid tumors. Patient-specific causal interaction network analysis further elucidated the interaction between inhibitors and signature biomarkers, providing insights that may inform clinical translation. SIGNIFICANCE: The combination of multiomics analysis and drug sensitivity profiling identified two signatures related to drug sensitivity in pediatric solid tumors, contributing to the advancement of functional precision medicine and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗
PubMed2025

Modeling Early-Onset Cancer Kinetics Reveals Changes in Underlying Risk and the Impact of Population Screening.

UNLABELLED: Recent studies have reported increases in early-onset cancer cases (diagnosed less than 50 years of age) and raised questions about whether the increase is related to earlier diagnosis from nonspecific medical tests as reflected by decreasing tumor-size-at-diagnosis (apparent effects) or actual increases in underlying cancer risk (true effects), or both. The classic Multistage Clonal Expansion (MSCE) model assumes cancer detection at the first malignant cell's emergence, although later modifications have included lag-times or stochasticity in detection to represent the delay in tumor detection. In this study, we introduced an approach to explicitly incorporate tumor-size-at-diagnosis in the MSCE framework accounting for improvements in cancer detection over time to distinguish between apparent and true increases in early-onset cancer incidence. The model was structurally identifiable and provided better parameter estimation than the classic model. The model was applied to colorectal, breast, and thyroid cancers to examine changes in cancer risk while accounting for detection improvements over time in three representative birth cohorts (1950-1954, 1965-1969, and 1980-1984). The analyses suggested accelerated carcinogenic events and shorter mean sojourn times (the average time from the first malignant cell emergence to cancer detection) in more recent cohorts. Furthermore, using this model to examine the screening impact on the incidence of breast and colorectal cancers, for which both have established screening protocols, provided results that align with well-documented differences in screening effects between these cancers. These findings underscore the importance of incorporating tumor-size-at-diagnosis in cancer modeling and support true increases in early-onset cancer risk in recent years for breast, colorectal, and thyroid cancers. SIGNIFICANCE: A model of early-onset cancer trends that distinguishes true risk from detection effects accurately captures cancer kinetics, trends in cancer progression, and the impact of screening, which could inform cancer prevention strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗
PubMed2026

Practicing Data Science in Interactive Notebooks.

The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.

Software↗
PubMed2025

Transcriptome-wide analysis reveals sequence selection to avoid mRNA aggregation in E. coli.

The stability of RNA base pairing and its limited four-letter code create an intrinsic potential for promiscuous RNA-RNA interactions. In vitro, such interactions drive RNA to self-assemble into aggregates. This raises a fundamental unanswered question: within a confined cellular volume at physiological mRNA abundances, how much aggregation would arise from sequence-encoded chemistry alone? Here, we establish this baseline with large-scale kinetic simulations of the E. coli transcriptome. Our simulations reveal that sequence-encoded base-pairing energetics is sufficient to generate a dynamic network of large aggregates, organized by long, multivalent mRNA hubs. Strikingly, evolutionary analysis shows that native E. coli sequences exhibit clear signatures of selection to counteract this propensity: they fold more stably, minimize unstructured regions, and form weaker intermolecular contacts than dinucleotide-preserving controls. These findings demonstrate that maintaining transcriptome solubility has been a significant, previously unrecognized constraint shaping genome evolution, and provide a new lens to interpret cellular RNA management.

Biological Sciences (Biophysics and Computational ↗
PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗
PubMed2025

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

Algorithms↗
PubMed1975

[Intranarcotic infusion therapy -- a computer interpretation using the program package SPSS (Statistical Package for the Social Sciences)].

In a retrospective 18-month study the infusion therapy applied in a great anesthesia institute is examined. The data of the course of anesthesia recorded on magnetic tape by routine are analysed for this purpose bya computer with the statistical program SPSS. It could be proved that the behaviour of the several anesthetists is very different. Various correlations are discussed.

Adolescent↗
PubMed1978

Management data for collection analysis and development.

Sound management data are needed to evaluate the collections of health sciences libraries. This study reports the utilization of computer data bases to compare the libary collections of The University of Texas Health Science Center at San Antonio. The University of Texas Medical Branch, and the National Library of Medicine's CATLINE data base. The imprint dates of the records of two libraries are compared to measure acquisitions rates. Subject profiles for the Q and W classes demonstrate the similarity of the collections. Reasons for the variances are considered.

Analysis of Variance↗
PubMed1978

The interdisciplinary approach to laboratory medicine.

The clinical laboratory is a melting pot of diverse scientific experiences, perspectives, and approaches, all directed to the solution of particular medical problems. Integration of separate disciplines and areas of expertise is involved at serveral different levels of laboratory medicine. At the outset, one sees that any one area of the laboratory must depend upon all other laboratory areas for the proper interpretation of its data. Stated another way, all laboratory disciplines are involved in the integrated functioning of each individual area. Examination of the origin of analytical concepts fundamental to procedures and instruments utilized in the clinical laboratory leads one to realize that all areas of science contribute to laboratory medicine.

Clinical Laboratory Techniques↗