SEARCH · Search PubMed

Results for “Data analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

PubMed1977

Computer processing of fatty acid analysis data.

A Fortran computer program for the processing of fatty acid data from the anlysis of fats and oils by gas-liquid chromatography is described. The analytical method and calculations are described primarily for the analysis of butterfat and margaine fat, but with minor changes could be adapted to suit other fats and oils. The program computes the concentration of each fatty acid as weight % and mole %, the percentage glycerol, the theoretical iodine value, and other relevant combinations of fatty acids.

Chemical Phenomena↗

PubMed1976

Insulin kinetics after portal and peripheral injection of [125I] insulin. I. Data analysis and modeling.

The kinetics of insulin are commonly investigated by intravenous administration of labeled hormone, whereas native insulin is removed by the liver to some extent before mixing in the systemic circulation. A mathematical model has been developed which makes it possible to interpret the experimental data obtained by peripheral plasma sampling after portal and peripheral injection of the tracer. Equations are given that allow for the computation of metabolic clearance rate, initial distribution volume, production rate, and body mass of insulin. It is demonstrated that hepatic extraction can be calculated from the difference between the clearance rate values obtained after portal and peripheral injection of the tracer; an estimate of total hepatic catabolism is also derived. The assumptions and limitations underlying this mathematical analysis are discussed.

Infusions, Parenteral↗

PubMed2026

Relationship Between Number of Acute Pancreatitis Episodes and Risk of New-onset Diabetes in the U.S.: A Real-world Data Analysis.

INTRODUCTION: Acute pancreatitis (AP) is a common inflammatory disorder that is associated with increased risk for diabetes mellitus (DM). It remains unclear whether recurrent acute pancreatitis (RAP) is associated with further increased risk of incident DM. This study aims to investigate the association between RAP and incident DM using real-world data. METHODS: We conducted a retrospective cohort study using the MerativeTM MarketScan® claims database (2016-2023), identifying patients with AP and no prior history of DM at baseline. The primary exposure of interest, RAP, was defined as one or more episodes of AP occurring ≥90 days after the index AP diagnosis, whereas one episode of AP referred to a single episode of AP (SAP) with no subsequent recurrence within 90 days following the index event. A multivariable stratified Cox proportional hazards regression models were used to determine the association between RAP and incident DM, identified using ICD-10 codes. RESULTS: In total, 16,184 individuals with AP (mean [SD] age: 45.8 [12.3]) contributed 40,712 person-years of follow-up, during which 1,477 incident cases of DM were documented. Individuals with RAP had an increased risk of incident DM compared with those with a SAP(adjusted HR, 1.92; 95% CI, 1.61-2.29). The risk increased significantly with the frequency of RAP. In comparing the modifying effect of patient demographics and comorbidities, a stronger association between RAP and incident DM was observed in females (adjusted HR, 2.44; 95% CI, 1.87-3.19) than in males (adjusted HR, 1.64; 95% CI, 1.30-2.07; Pinteraction=0.03). Also, stronger associations were observed among younger patients (18-46 y) (adjusted HR=2.56; 95% CI, 1.97-3.31) and among non-tobacco abuse (adjusted HR=2.19; 95% CI, 1.81-2.65), with significant interactions for all comparisons (Pinteraction<0.05. CONCLUSIONS: In this real-world study, RAP was associated with an increased risk of incident DM. Our findings highlight an opportunity for glycemic monitoring and proactive management of patients with RAP to mitigate their risk of developing DM.

AP↗

PubMed2026

Scale reliant mixed effects models enhance microbiome data analysis.

Linear models, including those used for differential abundance analyses, are frequently used in microbiome research to assess how experimental conditions (e.g., disease state or age) affect microbial abundance. Linear mixed-effects models (MEMs) extend linear models to accommodate complex designs, such as longitudinal sampling or hierarchical study structures. However, when applied to microbiome data, existing MEM approaches suffer from high false positive and false negative rates because sequence counts are compositional - they reflect relative rather than absolute abundances. Current methods attempt to overcome this limitation through normalization, but these approaches rely on strong, often unrealistic assumptions about the unmeasured biological scale (e.g., total microbial load). Here we introduce scale-reliant mixed-effects models (SR-MEM), which extend our earlier scale-reliant inference framework by explicitly modeling uncertainty in the unmeasured scale via user-defined probability distributions. By treating scale as a latent variable rather than fixing it through normalization, SR-MEM enables robust inference for complex experimental designs. SR-MEM can incorporate external scale measurements (e.g., flow cytometry, qPCR) or leverage scale information from independent studies to further improve inference. Across simulations and multiple real-world case studies, SR-MEM consistently controls the false discovery rate while maintaining comparable or higher power than standard approaches relying on normalization or bias correction. In reanalyses of published datasets, SR-MEM yields results that are more reproducible across studies and more consistent with known biological and pharmacological effects. SR-MEM provides a principled and practical framework for mixed-effects modeling of microbiome sequence count data in the presence of unmeasured biological scale. By avoiding normalization-based assumptions and instead propagating scale uncertainty through inference, SR-MEM improves error control and reproducibility in longitudinal and hierarchical studies. An accessible implementation is provided in the ALDEx3 R package.

Microbiota↗

PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗

PubMed1976

[On-line, high-speeded data analysis of cardiovascular function using hybrid systems (author's transl)].

In recent years application of computer techniques to various problems in biology has increased. In the field of anesthesia, where rapid changes appearing in a short time must be followed, the conventional, mannual methods are not applicable. So we have developed on-line computer analytical methods of cardiovascular function. Hybrid systems are the preferred methods in many biological problems, either because of efficiency and lower cost, or because of real time, on-line, closed-loop capabilities for direct use during experiments. As a computer output we used a graphic display computer and cathod ray tube devices. We applied these systems to analize and calculate cardiac work with other circulatory parameters. We got a ventricular function curve in visible form on CRT within 30 seconds after introducing the signals of left ventricular work and ventricular end-diastolic pressure into the analog computer. It was also useful to calculate vascular input impedence and myocardial maximal velocity of shortening. Finally causes of input errors in these analytical methods were discussed.

Animals↗

PubMed1978