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PubMed2026

Metabolism pathway-based subtyping in pancreatic adenocarcinoma: an integrated study by bulk RNA-sequence and machine learning algorithms.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is highly aggressive, and its tumor microenvironment has significant metabolic and immune microenvironment complexity and genomic instability. In this study, by integrating the metabolic pathway activity score and clinical data, we constructed a novel risk assessment model to reveal the unique biological behavior and clinical significance behind different PAAD subtypes. METHODS: In this study, the transcriptome and clinical data of TCGA and GSE57495 databases were integrated to explore the interaction between metabolic pathways. Based on unsupervised clustering analysis of pathway activity and survival prognosis, patients with PAAD were classified into metabolic subtypes with significant prognostic differences. Subsequently, we assessed the heterogeneity of these subtypes in terms of clinical outcomes, genomic characteristics, and immune microenvironment composition. Based on the differentially expressed genes (DEGs) among metabolic subtypes, a clinical prognostic risk model and nomogram were constructed, which were double-validated by GSE57495-independent cohort and GSE57495 + TCGA-PAAD combined cohort. Finally, the correlations between risk scores (RSs) and signaling pathway activity and tumor immune microenvironment characteristics were evaluated. RESULTS: Based on metabolic pathway correlation and prognostic information, 240 patients in the TCGA-PAAD and GSE57495 datasets were divided into three subgroups. There were significant differences between subgroups in gene expression, pathway activity, clinical prognosis, and immune infiltration characteristics among the subtypes. Using machine learning algorithms, an RS model was constructed from DEGs among the subgroups, with the random forest method showing the best performance. A nomogram integrating the RS and clinical indicators demonstrated excellent predictive accuracy for 1-, 3-, and 5-year survival rates, confirming the RS as an independent prognostic factor. High- and low-risk groups exhibited significant differences in immune infiltration, pathway activity, and gene mutations. Drug sensitivity analysis showed that the high-risk group was more sensitive to AZD6244, ABT737, and other drugs. CONCLUSION: This study stratified patients with PAAD into three subgroups based on metabolic pathways and prognostic information, revealing significant differences in clinical outcomes, immune characteristics, and genetic mutations. The robust RS model developed from these findings demonstrated strong predictive power for patient survival and identified promising therapeutic strategies, providing valuable insights for advancing precision medicine in PAAD.

immune microenvironment↗
PubMed2025

MIA-Jet: Multi-scale Identification Algorithm of Chromatin Jets.

The mammalian genome is organized into large-scale chromosome territories, compartments, domains, and at the smallest scale, chromatin loops and stripes. The newest element is a chromatin jet, a diffused line perpendicular to the main diagonal in the Hi-C contact map, which was reported in quiescent mammalian lymphocytes supporting a two-sided symmetric cohesin loop extrusion model. A similar structure is observed in Repli-HiC data, where relatively thin and straight chromatin fountains indicate coupling of DNA replication forks. However, the precise biological implications of these jet-like structures are unknown due to the limitations in computational methods. We developed MIA-Jet, a multi-scale ridge detection algorithm that can accurately detect jets of variable lengths, widths, and angles. When tested on Hi-C, Repli-HiC, ChIA-PET, ChIA-Drop, and Micro-C data in mouse, human, roundworm, and zebrafish cells, MIA-Jet outperformed existing methods. In human cells, jets were enriched in cohesin loading sites and early replication initiation zones. Applying MIA-Jet to Hi-C data generated from protein-degraded cells revealed that jets are dependent on cohesin but not YY1, and jet signals are strengthened after depleting WAPL. We envision MIA-Jet to be broadly applicable to any 3D genome mapping data, thereby providing new insights into the functional roles of chromatin jets.

3D genome mapping↗
PubMed1979

Counterregulatory system in an artificial endocrine pancreas. Glucose infusion algorithm.

An artificial endocrine pancreas has been developed by adding a glucose infusion system to the artificial beta cell system. In this computer algorithm, glucose is infused on the basis of proportional and derivative actions to blood glucose concentration with the time delay constant between blood withdrawal and initiation of glucose infusion. When glucose was infused on the basis of proportional action with a 20-minute time delay, the prompt restoration to normoglycemia from the hypoglycemic state with a smaller amount of glucose was shown in depancreatized dogs. These results indicate that the artificial endocrine pancreas thus prepared is not only a useful device to safely control blood glucose, but also is an efficient research tool for the analysis of the blood glucose regulatory mechanism.

Animals↗
PubMedDate not supplied

Computer algorithm for electron beam treatment planning.

A computer algorithm has been developed for electron beam treatment planning. The method uses a limited amount of stored experimental dose distribution data, performs interpolation, and stores generated beam information on an optimized fan line-depth line grid. Experimental verification of the computer program showed agreement within +/-5% for beam generation and air gap correction.

Computers↗
PubMed2026

Integrating Optical Genome Mapping into the Genetic Diagnostic Algorithm: Clinical Utility in Unresolved Autosomal Recessive Disorders from a Large Cohort.

INTRODUCTION: The identification of precise genetic etiologies is indispensable for the clinical management of monogenic disorders. However, conventional diagnostic methods and exome sequencing (ES) frequently fail to identify complex structural variations (SVs), leaving the genetic basis unexplained in approximately 30-60% of suspected cases. Optical genome mapping (OGM) emerges as a high-resolution technology capable of detecting cryptic SVs inaccessible to standard methodologies. METHODS: In this study, we evaluated the clinical utility of integrating OGM into the diagnostic algorithm for unresolved monogenic diseases. Following negative or inconclusive results from standard ES pipelines, OGM was applied to a targeted subset of patients (n = 7) selected from a comprehensive clinical cohort of 1,257 individuals with suspected genetic disorders. RESULTS: The integration of OGM identified candidate SVs that may represent the second allelic alteration in two distinct cases; however, confirmation through parental segregation analysis remains pending. Specifically, OGM identified an intronic insertion in the TTLL5 gene and a deletion in a putative regulatory region approximately 400 kb upstream of the NMNAT1 gene, both of which were missed by prior diagnostic testing. CONCLUSION: Our findings suggest that OGM has potential value in investigating the missing heritability of autosomal recessive disorders. By detecting candidate SVs invisible to conventional methods, OGM may warrant consideration as a complementary diagnostic approach following inconclusive ES; however, larger cohorts and confirmatory functional studies are needed to establish its clinical utility.

Autosomal recessive disorders↗
PubMed1978

A preliminary study in the detection of cardiac disorders via phase-invariant signature algorithm of ECG.

The present paper presents a simple and unambiguous computer-assisted method for the detection of cardiac disorders by processing the electrocardiogram. A new technique "Phase Invariant Signature Algorithm (PISA)", has been described, which is useful in the detection of cardiac abnormalities by investigating certain statistical properties of the measured waveform of ECG in a "phase-locked" fashion. This method of detection of abnormalities in the heart was used in the changes in the hypoxic action potential and in the ECG due to ionic changes and ischemia in the heart of cats and dogs. The signature of the normal ECG or action potential was straight-line. Any change in the waveform of ECG or action potential was detected as spikes in the signature. This method of detection of cardiac abnormalities does not require a priori reference to any standard ECG. The results indicate that this new method is capable of detecting cardiac disorders at an early stage and hence a higher sensitivity than the presently available type of ECG analysis. This method is in the developmental stage and further studies are being carried out.

Animals↗
PubMed1977

A physician extender training program based on clinical algorithms.

The ability of physician extenders to provide patient care in a variety of settings has been reported widely. Less attention has been paid to training programs, especially those of short duration, for physician extenders. A three-month training program for physician extenders at a large teaching hospital was based on teaching the skills needed to run 39 clinical algorithms. The course content and the methods used to test the students during the three months may prove of value to those preparing similar programs.

California↗
PubMedDate not supplied

Computer analysis of atypical urothelial cells. I. Classification by supervised learning algorithms.

Computer discrimination of atypical (ATY) urothelial cells from the urinary sediment by means of supervised learning algorithms discoled that these cells form a distinct, although ill-defined, family of cells which differs from normal (NEG) and malignant (POS) cell groups. The clinical significance of this observation must await long-term clinical follow-up. The possibility of issuing computer displays on individual patients with possible diagnostic and prognostic implications is discussed.

Aged↗
PubMed1979

Myocardial perfusion imaging using thallium-201: a new algorithm for calculation of background activity.

A method is presented for calculating a background image to be subtracted from TI-201 myocardial perfusion images. The method was derived from experimental measurements of background components in which hearts of animals injected with TI-201 were replaced with hearts from nonradioactive animals. The algorithm generates a background image that accounts for TI-201 activity in surrounding tissues and within the cardiac chamber. Comparison of the computer-generated background images with background images of the experimental models showed a mean difference of about 3% (range 1-6%). Clinical images using this method of background generation and subtraction are presented.

Animals↗
PubMedDate not supplied

Error measures for objective assessment of scene segmentation algorithms.

Scene segmentation is an important element in pattern recognition problems. Previous efforts to evaluate and compare scene segmentation procedures have been largely subjective. Quantitative error measures would facilitate objective comparison of scene segmentation algorithms. A theoretical discussion leading to a new generalized quantitative error measure, G2, based on comparison of both pixel class proportions and spatial distributions of "true" and test segmentations, is presented. This error measure was tested on 14 manual segmentations and 40 gynecologic cytology specimens segmented with five different scene segmentation techniques. Results indicate that G2 seems to have the desirable properties of correlation with human observation, categorization of error allowing for weighting, invariance with picture size and ease of computation necessary for a useful scene segmentation error measure.

Autoanalysis↗
PubMed1979

New control algorithms via brain theory.

New concepts and viewpoints are needed to develop control algorithms for large scale systems. It is hypothesized that brain functions in human beings clearly demonstrates the existence of superior techniques for controlling complex systems with multiple objectives. The theory of compacta is investigated as a basis for learning, memory, and perception.

Brain↗
PubMed1977

Counting methods (EM algorithm) in human pedigree analysis: linkage and segregation analysis.

The likelihood of human pedigree data can be written in such a form as to allow the computation of derivatives. This is done for various parameters in linkage and segregation analysis. The equations for the maximum likelihood estimates are represented in a particularly appealing form which allows iterative solutions. This process is an extension to pedigress of Smith's (1957) counting methods. All these procedures belong to a general class of MLE methods for incomplete data called EM algorithms (Dempster et al. 1976).

Gene Frequency↗
PubMed2026

Alternative genetic codes in bacteria and archaea identified with a fast k-mer-based algorithm.

The genetic code is conserved across all domains of life and is often described as universal. Nevertheless, many exceptions to the "universal" code have now been documented, most of these through manual or semiautomated inspection of highly conserved genes. Modern bioinformatics tools improved our ability to find alternative genetic codes but remain computationally expensive, preventing widespread use on thousands of new species identified by sequencing environmental samples. Here, I report a >100-fold accelerated method for inferring the genetic code directly from assembled genomes and apply it to thousands of previously uncharacterized assemblies from archaea and bacteria. I describe three candidate genetic code variations, one of which, an alternative genetic code used by a family of Asgard archaea, is a unique example of sense codon reassignments for this domain. Identifying genetic code variations is important for understanding evolution of the standard code and improving accuracy of protein databases and open reading frame identification.

Genetic Code↗
PubMed2025

Effectidor II: a pan-genomic AI-based algorithm for the prediction of type III secretion system effectors.

MOTIVATION: Type III secretion systems are used by many Gram-negative bacteria to inject type 3 effectors (T3Es) directly into eukaryotic cells, promoting disease or provoking immune response. Because of these opposing evolutionary forces, T3E repertoires often vary within taxonomic groups. Identifying the full effector gene repertoire in genomes of related individuals is crucial for determining core and specialized effectors, understanding the disease dynamics, and developing appropriate management strategies against pathogens. It can also help uncover novel T3Es that have recently emerged in a population. Our previously published Effectidor web server successfully addressed the challenge of identifying T3Es in a single bacterial genome. Here, we enriched the web server with various novel capabilities, including the identification of T3Es from multiple genome sequences simultaneously. RESULTS: We present Effectidor II, a web server that relies on machine learning to predict T3E-encoding genes within bacterial pan-genomes. We demonstrate the benefit of learning based on features extracted from the entire sequences comprising the pan-genome and report a novel T3E discovered by it in Xanthomonas euroxanthea. AVAILABILITY AND IMPLEMENTATION: Effectidor II is available at: https://effectidor.tau.ac.il and the source code is available at: https://github.com/naamawagner/Effectidor. A stand-alone version of Effectidor II is available at: https://github.com/naamawagner/Effectidor/tree/StandAlone. The source code for the standalone version and the data used in this work are also provided in https://doi.org/10.5281/zenodo.15081636.

Type III Secretion Systems↗
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↗