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PubMed2002

Kinetic impact of presystemic intestinal metabolism on drug absorption: experiment and data analysis for the prediction of in vivo absorption from in vitro data.

Orally administered drugs suffer from attack by metabolic enzymes not only in the liver, but also in the gastrointestine during the absorption process across the intestinal tissue. Although kinetic study on hepatic metabolism has been done well, the intestinal metabolism has not been well focused on compared with hepatic metabolism. In order to emphasize the role of intestinal metabolism in drug absorption and bioavailability, I have reviewed the experimental methods for intestinal absorption and metabolism, and the data analysis. Since Klippert et al. reported the prediction of intestinal first-pass effect of phenacetin in the rat from enzyme kinetic data in 1982, several reports have showed a good prediction, but others have not. Although intestinal absorption is an integrated process of transport (transporters) and metabolism (metabolic enzymes), most of the researchers missed the pathway of intestinal drug absorption and applied the kinetic model effective on only systemic metabolism to presystemic intestinal metabolism for their analysis of intestinal metabolism of orally administered drugs. A kinetic model, which incorporated factors of membrane transport, metabolic activity and protein binding, was structured to compare the equations in the reported models. In conclusion, we need more studies including kinetic modeling and experiments to understand the impact of intestinal metabolism on drug absorption. That knowledge must lead to the construction of ADME in silico (e-ADME).

Journal Article↗

PubMedDate not supplied

Designing for social data analysis.

The NameVoyager, a Web-based visualization of historical trends in baby naming, has proven remarkably popular. We describe design decisions behind the application and lessons learned in creating an application that makes do-it-yourself data mining popular. The prime lesson, it is hypothesized, is that an information visualization tool may be fruitfully viewed not as a tool but as part of an online social environment. In other words, to design a successful exploratory data analysis tool, one good strategy is to create a system that enables "social" data analysis. We end by discussing the design of an extension of the NameVoyager to a more complex data set, in which the principles of social data analysis played a guiding role.

Computer Graphics↗

PubMed2004

A multivariate gait data analysis technique: application to knee osteoarthritis.

Modern gait analysis is a powerful non-invasive tool for calculating the mechanical factors involved in pathological processes such as knee osteoarthritis (OA). Although very accurate measurements can be made, the clinical applicability and widespread use of gait analysis have been hindered by a lack of appropriate data analysis techniques for reducing and analysing the resulting large volumes of highly correlated gait data. This paper introduces a multidimensional gait data analysis technique that simultaneously considers multiple time-varying and discrete measures, exploiting the correlation structure between and within the measures. The multidimensional analysis technique was used to detect discriminatory mechanical features of knee OA gait patterns that involved interacting changes in several gait measures, at specific time portions of the gait cycle. The two most discriminatory features described a dynamic alignment difference and a loading response difference with knee OA.

Aged↗

PubMed2006

[Design and application of biological rhythm automatic monitor and data analysis system].

OBJECTIVE: To design and develop a biological rhythm monitor and data analysis system for studying biological rhythm in animals under different conditions. METHODS: The system was a distributed digital control system consisting of a computer and zeitgeber generators. Acquisition of data on animal activity and generation of zeitgeber were modularized to meet the multiple requirements of experiment. System functions and animal adaptability to this system were detected by monitoring the rhythm of mouse activities under different light-dark cycles. RESULT: The animal experiment showed that the functions of this system, including data acquisition, data communication, light regulation and data analysis, were qualified for application. There was no apparent malfunction. CONCLUSION: The system is safe, reliable and easy to operate. The system could be a very useful system to study biological rhythm in animals.

Animals↗

PubMed2026

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

Graph Neural Networks↗

PubMed1996

Sparfloxacin for the treatment of community-acquired pneumonia: a pooled data analysis of two studies.

A pooled data analysis of two double-blind studies encompassing 1137 episodes of community-acquired pneumonia in hospitalised adults, of which 560 were treated with sparfloxacin and 577 were randomised to comparator antibacterial agents (amoxycillin/clavulanic acid, erythromycin or amoxycillin administered at reference dosages), was performed. The global efficacy rate at the end of treatment in evaluable patients treated with sparfloxacin was 88.3% compared with 84.1% in those who received comparator antibacterial agents. This analysis verified the efficacy of this new aminofluoroquinolone, given orally once daily, in the treatment of community acquired pneumonia. The overall outcome favoured sparfloxacin for use in the empirical treatment of community-acquired pneumonia.

Adult↗

PubMed1995

Casuistry, care, compassion, and ethics data analysis.

Traditional approaches to descriptive ethics data analysis do not explicate the nature of ethical dilemmas in critical care and emergency nursing. Using sensitizing concepts of causes of nurse compassion, 168 stories of ethical dilemmas were categorized. They were analyzed from the theoretical perspective of an ethic of care using casuistry. Paradigm cases for each sensitizing concept were constructed. The nature of ethical dilemmas is in the disparity between actual and ideal nursing practice.

Adult↗

PubMed1983

[Study of critical levels of 3 antistreptococcal antibodies by data analysis].

Prinqual, a new statistical data-analysis method was used to determine the critical titers of antistreptolysin O (ASLO), anti-DNase B (ADB) and antistreptokinase (ASK) in the sera of 104 patients with suspected streptococcal infection and 121 blood-donors. The whole range from pathological to normal was thus covered, thereby avoiding bias concerning the proportion of normal titers in a control population. Another advantage of the method was that the titers of the 3 antibodies were considered simultaneously and the semi-quantitative results of serological tests were avoided. It appeared from this study that the upper limits of normal values were: ASLO, 100 U; ADB, 240 U and ASK, 40 U. The first probably pathological values were: ASLO, 150 U; ADB, 480 U and ASK, 320 U. These results are compared with those available in the literature.

Antibodies, Bacterial↗

PubMed1984

An evaluation of three statistics of structured exploratory data analysis.

The power of structured exploratory data analysis (SEDA) to discriminate among major genic, polygenic, and nongenetic determination of phenotypes was investigated using computer simulation. Three classes of SEDA indices (the major gene index, the offspring between parents function, and the midparent-child correlation coefficient) were evaluated. These three statistics, in combination, were reasonably sensitive in detecting the presence of a major locus and in discriminating between phenotypes with genetic effects and those with no genetic component. However, they were unable to discriminate between major genic and polygenically determined phenotypic models.

Chromosome Mapping↗

PubMed1996

Test of linear trend in eigenvalues of a covariance matrix with application to data analysis.

Principal component analysis and factor analysis are the most widely used tools for dimension reduction in data analysis. Both methods require some good criterion to judge the number of dimensions to be kept. The classical method focuses on testing the equality of eigenvalues. As real data hardly have this property, practitioners turn to some ad hoc criterion in judging the dimensionality of their data. One such popular method, the 'scree test' or 'scree plot' as described in many texts and statistical programs, is based on the trend in eigenvalues of sample covariance (correlation) matrix. The principal components or common factors corresponding to eigenvalues which exhibit a slow linear decrease are discarded in further data analysis. This paper develops a formal statistical test for the 'scree plot'. A special case of this test is the classical test for equality of eigenvalues which has been suggested in several texts as the criterion to decide the number of principal components to retain. Comparisons between equality of eigenvalues and the slow linear decrease in eigenvalues on some classical examples support the hypothesis of slow linear decrease. A physical background to such a phenomenon is also suggested.

Analysis of Variance↗

PubMed2005

From targets to leads: the importance of advanced data analysis for decision support in drug discovery.

Lead discovery is a complex process that is intimately linked to chemistry, but which is also increasingly driven by biological sciences. In an industrial pharmaceutical research environment the process is defined by highly automated technologies for target identification and validation, compound library screening, and compound efficacy assessment. The huge volumes and complex dependencies of data produced by such large-scale experiments have led to a reassessment of data analysis processes, resulting in the development of novel data analysis strategies tailored to drug discovery. In this review, recent progress in data-driven research applications is reported, focusing on the use and processing of transcriptomics, proteomics and high-throughput screening data. The successful application of specialized data analysis procedures in many companies is discussed, which has resulted in significant improvements in decision-making processes for progressing therapeutic targets to promising leads.

Animals↗

PubMed2005

Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential.

BACKGROUND: The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630-631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676-5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. RESULTS: We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. CONCLUSION: Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.

Databases, Genetic↗

PubMed1994

[Integrated and automated data analysis for neuronal activation studies using positron emission tomography: methodology and applications].

A data analysis method was developed for neuronal activation studies using [15O]water positron emission tomography (PET). The method consists of several procedures including intra-subject head motion correction (co-registration), detection of the mid-sagittal plane of the brain, detection of the intercommissural (AC-PC) line, linear scaling and non-linear warping for anatomical standardization, pixel-by-piexl statistical analysis, and data display. All steps are performed in three dimensions and are fully automated. Each step was validated using a brain phantom, computer simulations, and data from human subjects, demonstrating accuracy and reliability of the procedure. The method was applied to human neuronal activation studies using vibratory and visual stimulations. The method detected significant blood flow increases in the primary sensory cortices as well as in other regions such as the secondary sensory cortex and cerebellum. The proposed method should enhance application of PET neuronal activation studies to the investigation of higher-order human brain functions.

Adult↗

PubMed2005

Predictive neural networks for gene expression data analysis.

Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This paper presents a systematic approach for learning and extracting rule-based knowledge from gene expression data. A class of predictive self-organizing networks known as Adaptive Resonance Associative Map (ARAM) is used for modelling gene expression data, whose learned knowledge can be transformed into a set of symbolic IF-THEN rules for interpretation. For dimensionality reduction, we illustrate how the system can work with a variety of feature selection methods. Benchmark experiments conducted on two gene expression data sets from acute leukemia and colon tumor patients show that the proposed system consistently produces predictive performance comparable, if not superior, to all previously published results. More importantly, very simple rules can be discovered that have extremely high diagnostic power. The proposed methodology, consisting of dimensionality reduction, predictive modelling, and rule extraction, provides a promising approach to gene expression analysis and disease understanding.

Animals↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMedDate not supplied

A simple approach using Bouwer and Rice's method for slug test data analysis.

Slug test data obtained from tests performed in an unconfined aquifer are commonly analyzed by graphical or numerical approaches to determine the aquifer parameters. This paper derives three fourth-degree polynomials to represent the relationship between Bouwer and Rice's coefficients and the ratio of the screen length to the radius of the gravel envelope. A numerical approach using the nonlinear least squares and Newton's method is used to determine hydraulic conductivity from the best fit of the slug test data. The method of nonlinear least squares minimizes the sum of the squares of the differences between the predicted and observed water levels inside the well. With the polynomials, the hydraulic conductivity can be obtained by simply solving the nonlinear least squares equation by Newton's method. A computer code, SLUGBR, was developed from the derived polynomials using the proposed numerical approach. The results of analyzing two slug test datasets show that SLUGBR can determine hydraulic conductivity with very good accuracy.

Data Collection↗

PubMed1994

[Classification, a suitable method for nursing research--practical advice for data collection and data analysis].

Arranging subjective data in rank order is a simple method for making comparisons of judgements and evaluating them. Since it is difficult to find practical instructions for this which can be easily understood, this paper uses data obtained in a study of patients' opinions about their privacy. Results are analysed, presented and explained. The method is appropriate for a wide range of research in which individuals are asked for opinions.

Data Collection↗

PubMed2006

[Microarray data analysis].

Microarrays might be used for future diagnostic and prognostic purposes. High-density oligonucleotide arrays are promising in this respect. The microarray data consist of intensity files, which are transformed into expression matrices by the application of several mathematical modifications. However, pitfalls regarding data analysis seem to be a critical factor for the impact of this new technology. This article focuses on the data analysis, from raw data file to marker gene lists used to retrieve knowledge about underlying biological processes.

Gene Expression Profiling↗