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PubMed1978

A fast evaluation of diffusion effects on bound enzyme activity.

As the kinetic behavior of bound enzymes is frequently affected by substrate diffusion between the bulk solution and the catalytic sites, a fast and simple method is proposed to detect and, subsequently, to remove diffusion effects on measured enzymic activities. The procedure makes use of the effectiveness factor concept and essentially involves the direct determination on two diagrams of the magnitude of both external and internal diffusion limitations. It requires a prior estimation of the volume and external surface area of the matrix, of the substrate external transport coefficient and internal diffusivity, and of the intrinsic Michaelis constant of the bound enzyme. However, it does not necessitate the knowledge of the quantity of bound enzyme. The two basic graphs have been calculated for Michaelis-Menten kinetics. They can also be used to evaluate diffusional effects on two-substrate reactions, as illustrated with previously published data.

Diffusion↗

PubMed2026

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗

PubMed2026

Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.

MOTIVATION: Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e. without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism. RESULTS: We use these overlaps to construct de novo assembly graphs with an existing assembler, miniasm, off-the-shelf. To our knowledge, these are the first de novo assemblies ever constructed directly from raw signals without basecalling. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 5.01× and up to 23.10×) and reduces peak memory usage (on average by 5.74× and up to by 22.00×) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that around one-third of Rawsamble's overlapping pairs are also found by minimap2. We find that when we use overlapping reads from Rawsamble, we can construct unitigs that are (i) as accurate as those built from minimap2's overlaps and (ii) up to half a chromosome in length (e.g. 2.3 million bases for E. coli). AVAILABILITY AND IMPLEMENTATION: Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Nanopores↗

PubMed1992

A review of pressure reduction device studies.

When considering patient morbidity, mortality and costs, pressure sore formation is a major health care concern. An adjunct to the nursing care plan for prevention and treatment is the selection of a pressure reduction/relief device. This review of four pressure reduction device studies is intended to increase the vascular nurse's knowledge base concerning pressure reduction device efficacy. Each study is reviewed and graphed to identify like patterns within each study. Foam is judged to be least effective. Air cell and air mattress overlays are found to be intermediate devices and the low air loss beds appear to be the most effective. Few of the tested devices reduced pressure below 32mmHg at the heel site, leaving the heel at risk for breakdown.

Bedding and Linens↗

PubMed1991

Knowledge-based simulation of DNA metabolism: prediction of enzyme action.

We have developed a knowledge-based simulation of DNA metabolism that accurately predicts the actions of enzymes on DNA under a large number of environmental conditions. Previous simulations of enzyme systems rely predominantly on mathematical models. We use a frame-based representation to model enzymes, substrates and conditions. Interactions between these objects are expressed using production rules and an underlying truth maintenance system. The system performs rapid inference and can explain its reasoning. A graphical interface provides access to all elements of the simulation, including object representations and explanation graphs. Predicting enzyme action is the first step in the development of a large knowledge base to envision the metabolic pathways of DNA replication and repair.

Adenosine Monophosphate↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗

PubMed2024

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Stomach Neoplasms↗

PubMed1992

A robust and efficient automated docking algorithm for molecular recognition.

A completely automated method is described for determining the most likely mode of binding of two (macro)molecules from the knowledge of their three-dimensional structures alone. The method is based on well-known graph theoretical techniques and has been used successfully to determine and rationalize the binding of a number of known macromolecular complexes. In this article we present results for a special case of the general molecular recognition problem--given the information concerning the particular atoms involved in the binding for one of the molecules, the algorithm can correctly identify the corresponding (contacting) atoms of the other molecule. The approach used can be easily extended to the general molecular recognition problem and requires the extraction of maximal common subgraphs. In these studies the docking of the macromolecules was achieved without the aid of computer graphics or other visual aids. The algorithm has been used to determine the correct mode of binding of a protein antigen to an antibody in approximately 100 min on a DEC micro VAX 3600.

Algorithms↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed1992

Natural language processing and semantical representation of medical texts.

For medical records, the challenge for the present decade is Natural Language Processing (NLP) of texts, and the construction of an adequate Knowledge Representation. This article describes the components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community's AIM programme. They are: a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural Language Generator and, in addition, a Translator, a Diagnosis Encoding System and a Literature Indexing System. Taking advantage of a closed domain of knowledge, defined around a medical specialty, a method called proximity processing has been developed. In this situation no parser of the initial text is needed, and the system is based on semantical information of near words in sentences. The benefits are: easy implementation, portability between languages, robustness towards badly-formed sentences, and a sound representation using conceptual graphs.

Abstracting and Indexing↗

PubMedDate not supplied

[An educational management system for the postgraduate training of physician-radiologists].

The paper is devoted to the development of a system of program-oriented postgraduate education of radiologists based methodologically on acquiring professional knowledge and skills. Educational goals were adapted for each subject area. Matrix analysis and plotting of a logical structure graph were used for a choice of the final goals of education. The subject matter is in full accord with the educational goals, based upon the qualification characteristics of radiologists. The developed and published methodological materials make it possible to control the students' activities during extracurricular training and practical work in x-ray units and at seminars. Directed text control is used for the estimation of efficacy and correction of education.

Education, Medical, Continuing↗

PubMed1990

[Quality control and optimization of therapeutic activity with the use of thermoanalytic methods].

Thermogravimetry, differential scanning calorimetry, thermomicroscopy, transparency measure permit drug quality control: knowledge of solvated or unsolvated mole, thermal stability, identification of crystalline form (polymorphism) quick determination of total impurity with graphs. These methods can be used for therapeutic activity optimization of very weakly soluble drugs: research of the polymorph showing the best kinetic dissolution, establishment of phase diagram drug/inert substance by example for determination invariant composition particularly eutectic for which the drug solubility is increased.

Calorimetry, Differential Scanning↗

PubMed1989

[Relevance of the Periotest as a function of root shape and cross-section].

In the laboratory extracted teeth were provided with an artificial periodontal ligament. Subsequently periotest values were measured around the complete circumference of the crown. When plotted in a graph the relationship between root cross-section and damping value can be demonstrated. At the present level of knowledge only comparative measurements, in particular in teeth with low periotest values, seem reliable.

Humans↗

PubMed1979

[Carl Heinrich Stratz (1858 to 1924), one of the first researchers in growth and development--chronological tables of his life and work (author's transl)].

Review of education, life and work of C.H. Stratz. First a clinical gynecologist and always earning his living from this profession, he was interested in the problems of somatic and regional anthropology. After 1900 he became more and more engaged in observations of the child's development from the new-born to the adolescent. His publications, especially his graphs and pictures of morphological changes in the childs body and the signs of puberty, have the following years deeply influenced our knowledge on "orthology" within the medical problems of youth.

Anthropology, Physical↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed1979

Thermal conduction effects in human skin.

To determine the maximum permissible temperature any material may attain without causing pain or burn on contact with bare skin, over 2000 observations were made of pain threshold during contact with materials at elevated temperatures. Six materials were used representing the full range of thermal properties from good conductors to good insulators. Time to pain threshold was converted to time to threshold blister on the basis of the relationship between pain and burn established earlier for radiant and for convective heating. Calculated times to blister were used to predict the material temperatures causative of "touch-burn". Experimentally produced threshold blisters at the predicted temperature-times verified the predictions. Graphs and equations were generated for determining safe temperatures for any material in contact with bare skin for 1-5 s solely from a knowledge of its thermal properties. Conversely, the thermal inertia (k rho c) of the optimal material for a specific use and skin contact can be predicted from a knowledge of the maximum material temperature and length of contact time anticipated.

Aerospace Medicine↗

PubMed1990

Proposed methodology for knowledge acquisition: a study on congenital heart disease diagnosis.

This paper proposes a methodology for knowledge acquisition (KA) from multiple experts, in an attempt to elicit the heuristic rules followed by the physician in diagnosing twelve frequently occurring congenital heart diseases (CHD). Twenty-two pediatric cardiologists and twenty-three general cardiologists were interviewed with this technique; 274 interviews were conducted, 169 with the 22 experts, 105 with the 23 non-experts. A graph formalism was employed to represent their reasoning model, leading to the construction of a "mean reasoning model" for each diagnosis, separately for experts and non-experts. The results indicate that experts, compared to non-experts, tend to build knowledge representation models (KRM) that are smaller and less complex. Qualitative differences in information utilization between the two groups were also observed. Entropy analysis suggests a greater objectivity and cohesion of the experts' model.

Algorithms↗