Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Machine learning model”

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.

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

At least 577 records · Page 32Linked to original sources
PubMed2026

Discovering hidden candidate plastic-degrading enzymes: Combined multi-omics and machine learning strategy.

Plastic pollution poses a major threat to the stability of natural ecosystems as well as human health. Microbial enzymes have long been considered a potential resource for targeted biodegradation but, except for a few successful cases, the discovery of efficient enzymes has proved challenging. Aiming to accelerate the process, we propose an approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms. Tested on a dataset of over 10,000 microbial proteins, ranking models consistently prioritize known plastic-degrading enzymes, achieving an area under the cumulative distribution function curve above 0.96, with leave-one-family-out cross-validation indicating that performance is largely retained across protein families. As a case study, this work focuses on mixed microbial cultures exposed for extended periods to polyethylene, polyethylene terephthalate, and polyurethane substrates. The prevalent species after selective enrichment were functionally characterized, finding Rhodococcus aetherivorans as the most relevant species in two of the five cultures under investigation. Among the top-ranked proteins, several have high structural similarity with known enzymes despite not being identified by sequence similarity search. Moreover, according to metatranscriptomics results, several of these enzymes were found to be expressed at the same level or above that of annotated enzymes, suggesting that they may have functional relevance. Overall, this work highlights the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.

Biodegradation, Environmental↗
PubMed2026

PMGen: from peptide-MHC structure prediction to peptide generation.

MOTIVATION: Accurate structural modeling of peptide-major histocompatibility complex (pMHC) complexes is essential for structure-driven immunotherapy design, yet current prediction tools suffer from narrow class coverage, restricted peptide lengths, insufficient accuracy, and a lack of built-in structure-aware peptide sampling. Consequently, most mimotope and altered peptide ligand designs rely solely on sequence substitution, leaving spatial and biophysical insights from pMHC structures largely unexploited. RESULTS: We introduce peptide-MHC generator (PMGen), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC Class I and II. PMGen enforces anchor constraints within AlphaFold2 through two complementary strategies, initial guess and template engineering, achieving state-of-the-art structural fidelity without model fine-tuning. On a comprehensive benchmark, PMGen outperforms all existing methods, yielding median peptide-core Cα RMSDs of 0.62 Å for MHC-I and 0.33 Å for MHC-II. We show that PMGen can recover incorrectly predicted anchor positions and that AlphaFold pLDDT scores enable sequence-independent binding-core identification. Applied to a published neoantigen/wild-type pair, PMGen accurately captures mutation-induced conformational changes. Beyond structure prediction, we show that ProteinMPNN sampling on PMGen-predicted backbones yields higher affinity peptides while preserving the parental 3D conformation. Using PMGen to generate 63 817 high-confidence pMHC structures as training data, we further improve ProteinMPNN's peptide sequence recovery from 0.14 to 0.64 on a test set of 85 unseen MHC-I alleles, highlighting the value of accurate predicted structures for downstream machine learning tasks. AVAILABILITY AND IMPLEMENTATION: PMGen is freely available at https://github.com/soedinglab/PMGen, with an interactive Colab notebook at https://colab.research.google.com/github/soedinglab/PMGen/blob/master/colab.ipynb.

Peptides↗
PubMed2025

FOSB is a key factor in the genetic link between inflammatory bowel disease and acute myocardial infarction: multiple bioinformatics analyses and validation.

BACKGROUND: Inflammatory Bowel Disease (IBD), which includes Crohn's disease and ulcerative colitis, is associated with an increased risk of Acute Myocardial Infarction (AMI). The genetic mechanisms underlying this link are not well understood. METHODS: We downloaded IBD and AMI-related microarray datasets from the NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed using enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning techniques, including LASSO, random forest, and Boruta, were employed to screen for hub genes. These genes were validated through qRT-PCR and Western blotting. Single-cell sequencing was used to confirm findings. Additionally, potential therapeutic targets were identified using the Connectivity Map (CMap) database. RESULTS: Five key hub genes-THBD, FOSB, ADGPR3, IL1R2, and PLAUR-were identified as significantly involved in both IBD and AMI pathogenesis. A diagnostic model for AMI constructed using these hub genes demonstrated high predictive accuracy. Single-cell sequencing analysis and several potential drugs targeting these hub genes were identified, offering new therapeutic avenues. CONCLUSION: This study highlights the crucial role of FOSB and other hub genes in the comorbidity of IBD and AMI. The findings provide novel insights for early diagnosis and potential therapeutic strategies, emphasizing the importance of further investigation into these genetic links.

Humans↗
PubMed2026

mmContext: an open framework for multimodal contrastive learning of omics and text data.

SUMMARY: Multimodal approaches are increasingly leveraged for integrating omics data with textual biological knowledge. Yet there is still no accessible, standardized framework that enables systematic comparison of omics representations with different text encoders within a unified workflow. We present mmContext, a lightweight and extensible multimodal embedding framework built on top of the open-source Sentence Transformers library. The software allows researchers to train or apply models that jointly embed omics and text data using any numeric representation stored in an AnnData.obsm layer and any text encoder available in Hugging Face. mmContext supports integration of diverse biological text sources and provides pipelines for training, evaluation, and data preparation. We train and evaluate models for a RNA-Seq and text integration task, and demonstrate their utility through zero-shot classification of cell types and diseases across four independent datasets. By releasing all models, datasets, and tutorials openly, mmContext enables reproducible and accessible multimodal learning for omics-text integration. AVAILABILITY AND IMPLEMENTATION: Pretrained checkpoints and full source code for our custom MMContextEncoder are available on Hugging Face huggingface.co/jo-mengr. The Python package github.com/mengerj/mmcontext provides the model implementation and training and evaluation scripts for custom training. The releases for the publication can be accessed via zenodo: adata_hf_datasets: doi.org/10.5281/zenodo.19185217 and mmContext: doi.org/10.5281/zenodo.19185493.

Computational Biology↗
PubMed2004

Evolving rule-based systems in two medical domains using genetic programming.

OBJECTIVE: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. MATERIAL: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. METHODS: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. RESULTS: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. CONCLUSION: The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Aphasia↗
PubMed2001

Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models.

After introducing the fundamentals of BYY system and harmony learning, which has been developed in past several years as a unified statistical framework for parameter learning, regularization and model selection, we systematically discuss this BYY harmony learning on systems with discrete inner-representations. First, we shown that one special case leads to unsupervised learning on Gaussian mixture. We show how harmony learning not only leads us to the EM algorithm for maximum likelihood (ML) learning and the corresponding extended KMEAN algorithms for Mahalanobis clustering with criteria for selecting the number of Gaussians or clusters, but also provides us two new regularization techniques and a unified scheme that includes the previous rival penalized competitive learning (RPCL) as well as its various variants and extensions that performs model selection automatically during parameter learning. Moreover, as a by-product, we also get a new approach for determining a set of 'supporting vectors' for Parzen window density estimation. Second, we shown that other special cases lead to three typical supervised learning models with several new results. On three layer net, we get (i) a new regularized ML learning, (ii) a new criterion for selecting the number of hidden units, and (iii) a family of EM-like algorithms that combines harmony learning with new techniques of regularization. On the original and alternative models of mixture-of-expert (ME) as well as radial basis function (RBF) nets, we get not only a new type of criteria for selecting the number of experts or basis functions but also a new type of the EM-like algorithms that combines regularization techniques and RPCL learning for parameter learning with either least complexity nature on the original ME model or automated model selection on the alternative ME model and RBF nets. Moreover, all the results for the alternative ME model are also applied to other two popular nonparametric statistical approaches, namely kernel regression and supporting vector machine. Particularly, not only we get an easily implemented approach for determining the smoothing parameter in kernel regression, but also we get an alternative approach for deciding the set of supporting vectors in supporting vector machine.

Algorithms↗
PubMed2025

Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Selection, Genetic↗
PubMed1995

[Ventilators for anesthesia. Models available in France. Criteria for choice].

This update article discusses the criteria for the choice of an anaesthetic machine and provides a short analysis of the main components of the models commercialized in France in 1994. The following items are considered: the design of the machine, the fresh gas delivery system, the anaesthesia breathing system(s), the ventilator and the waste gas scavenging system, the monitors associated with the machine and other criteria such as facility of learning to run the machine and of its daily use, ease of "in-house" maintenance and quality of after-sales service, cost of the machine and of its use (driving gas, disposable equipment).

Anesthesia, Inhalation↗
PubMed2004

Onvergence and application of online active sampling using orthogonal pillar vectors.

The analysis of convergence and its application is shown for the Active Sampling-at-the-Boundary method applied to multidimensional space using orthogonal pillar vectors. Active learning method facilitates identifying an optimal decision boundary for pattern classification in machine learning. The result of this method is compared with the standard active learning method that uses random sampling on the decision boundary hyperplane. The comparison is done through simulation and application to the real-world data from the UCI benchmark data set. The boundary is modeled as a nonseparable linear decision hyperplane in multidimensional space with a stochastic oracle.

Algorithms↗
PubMed2026

Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.

Cancer of Unknown Primary (CUP) presents substantial diagnostic and therapeutic challenges owing to its heterogeneous nature and the absence of an identifiable primary tumor site. This review provides a structured search of the pathogenesis, epidemiological characteristics, and limitations of traditional diagnostic and therapeutic approaches for CUP, with an emphasis on the evolution of Tissue of Origin (TOO) identification techniques. Recent advances in precision medicine have accelerated the development of machine learning-based TOO identification tools, representing a paradigm shift in CUP diagnostics. Deep learning (DL) algorithms that integrate multi-omics data (such as genomics and transcriptomics) with clinical features have markedly enhanced the accuracy of tracing tumor origin, and artificial intelligence (AI) driven TOO models are increasingly being incorporated into clinical practice, offering new insights for pathological diagnosis, treatment selection, and prognostic evaluation. Nevertheless, several challenges remain, including issues of data standardization, model generalizability, and interpretability. Ethical considerations related to data privacy, algorithmic fairness, and clinical implementation also warrant careful attention. Future research should focus on establishing standardized multi-center databases, developing more interpretable AI models, and fostering multidisciplinary collaborative strategies for CUP management. Through continued refinement of technical solutions and regulatory guidelines, TOO identification is anticipated to progress from research to routine clinical application, ultimately supporting precise and personalized care for patients with CUP.

Artificial intelligence↗
PubMed1995

Using data preprocessing and single layer perceptron to analyze laboratory data.

During daily work in hospitals a large amount of clinical data is produced each day. Totally computerized patient records are not yet widely used but a large part of essential information is already stored on computer files. These include laboratory test results, diagnoses, codes for operations, codes of histopathological diagnoses and maybe even the patient's medication. Accordingly, these databases include much clinical knowledge that would be useful for clinicians. Laboratories try to support clinicians by producing reference values for laboratory tests. It is, of course, necessary information but, however, it does not give very much information about the weight of evidence that an abnormal laboratory test will give in special clinical settings. We have developed a software package - DiagaiD - in order to build a smart link between patient databases and clinicians. It utilizes neural network-based machine learning techniques and can produce decision support which meets the special needs of clinicians. From example cases it can learn clinically relevant transformations from original numeric values to logical values. By using data transformation together with a single layer perceptron it is possible to build nonlinear models from a set of preclassified example cases. In this paper, we use two small datasets to show how this scheme works in the diagnosis of acute appendicitis and in the diagnosis of myocardial infarction. Results are compared with those obtained using logistic regression or backpropagation neural networks. The performance of our neuro-fuzzy tool seemed to be slightly better in these two materials but the differences did not reach statistical significance.

Appendicitis↗
PubMed1994

Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates.

An interactive computer system evaluates and diagnoses based on cytologic features derived directly from a digital scan of fine-needle aspirate (FNA) slides. A consecutive series of 569 patients provided the data to develop the system and an additional 54 consecutive, new patients provided samples to test the system. The projected prospective accuracy of the system estimated by tenfold cross validation was 97%. The actual accuracy on 54 new samples (36 benign, 1 atypia, and 17 malignant) was 100%. Digital image analysis coupled with machine learning techniques will improve diagnostic accuracy of breast fine needle aspirates.

Biopsy, Needle↗
PubMed2026

A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.

In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by learning which biomolecules are highly predictive of a treatment, biological outcome, or phenotype. A major challenge of applying ML to high throughput omics is overcoming noise when sample size is limited and unbalanced with respect to tens of thousands of biomolecules measured. Thus, feature selection (the process of reducing the number of predictors) is both a critical and common step in the ML analysis pipeline. While much attention has been given to embedding and wrapping techniques for feature selection in the omics space, filter-based methods for model-free feature selection have appealing theoretical properties. This manuscript evaluates sure screening, a class of filter-based feature selection methods which provide analytical guarantees for true feature set retention. Here, we cover existing feature screening methods based on the sure screening principal, available software, methods to improve feature screening, and contextualize feature screening in the larger discussion of feature selection for omics data analysis. Additionally, a suite of model-free sure screening approaches is applied and compared for several omics biomedical applications in a ML classification context. We identified BcorSIS as the most effective and computationally efficient screening method across various omics datasets, consistently outperforming others like CSIS and DCSIS in runtime.

Humans↗
PubMed2026

SSB deficiency-induced R-loop accumulation triggers podocyte inflammation in DKD.

INTRODUCTION: Diabetic kidney disease (DKD) is fundamentally a podocytopathy in which sterile inflammation plays a central pathogenic role, yet the upstream triggers that initiate inflammatory cascades in podocytes remain elusive. R-loops are critical regulators of genomic stability, and their pathological accumulation triggers DNA damage and innate immune activation. Whether R-loop dysregulation contributes to podocyte-driven inflammation in DKD is unknown. METHODS: We integrated single-cell transcriptomic profiling, dual machine learning algorithms, and functional experiments to dissect the R-loop regulatory network in the diabetic kidney. RESULTS: Integrated analysis of human diabetic kidney single-cell RNA-seq data revealed a globally compromised R-loop regulatory network selectively within podocytes. Intersection of podocyte-specific transcriptomic shifts with validated R-loop regulators identified 93 candidate genes, from which dual machine learning algorithms pinpointed SSB (Sjögren syndrome antigen B) as the principal podocyte-selective R-loop resolver and a superior diagnostic biomarker (AUC = 0.983). SSB expression was selectively downregulated in diabetic podocytes and showed the strongest positive correlation with the R-loop resolution module. Mechanistically, SSB loss impaired RNA splicing and stability pathways, leading to aberrant R-loop accumulation that activated the cGAS-dependent inflammatory signaling in podocytes. In two murine DKD models and high glucose-challenged podocytes, SSB was markedly reduced. Remarkably, SSB knockdown in podocytes alone sufficed to trigger R-loop accumulation and pro-inflammatory cytokine expression, whereas both RNase H1-mediated R-loop removal and cGAS co-depletion blunted this response. DISCUSSION: These findings suggest that an SSB-governed R-loop -cGAS -inflammatory signaling axis may link genomic instability to podocyte inflammation and contribute to DKD progression, nominating R-loop homeostasis as a previously unrecognized potential therapeutic target.

Podocytes↗
PubMed2025

Identification of Biomarkers for Right Ventricular Dysfunction in Idiopathic Dilated Cardiomyopathy Via Urinary Proteomics and Machine Learning.

BACKGROUND: Right ventricular dysfunction (RVD) is a common complication of idiopathic dilated cardiomyopathy linked to poor outcomes. However, reliable noninvasive biomarkers for RVD remain lacking. This study aimed to identify urinary proteomic markers using mass spectrometry and machine learning. METHODS: In this prospective cohort, patients with idiopathic dilated cardiomyopathy were classified by cardiac magnetic resonance imaging into groups with RVD (RV ejection fraction <45%) and without RVD groups. Baseline urine samples were profiled by data-independent acquisition mass spectrometry. Differentially expressed proteins were identified and selected by least absolute shrinkage and selection operator regression to build a diagnostic model, developed in a training set, and validated in a test set. The primary end point was a composite of cardiovascular death, heart failure rehospitalization, left ventricular assist device implantation, or heart transplantation. RESULTS: The study enrolled 147 patients with idiopathic dilated cardiomyopathy (64 with RVD, 83 without), with a median follow-up of 19.3&#x2009;months. Of 3579 quantified urinary proteins, 46 were differentially expressed between groups. A 3-protein panel (RARRES1 [retinoic acid receptor responder protein 1], MVB12B [multivesicular body subunit 12B], GSK3A [glycogen synthase kinase 3 alpha]) was identified and showed excellent diagnostic accuracy (training area under the curve 0.946; validation area under the curve0.935), outperforming both NT-proBNP (N-terminal pro-brain natriuretic peptide) and tricuspid annular plane systolic excursion. The risk score derived from this panel effectively stratified patients, with the high-risk group exhibiting significantly worse outcomes than the low-risk group (hazard ratio, 3.24 [95% CI, 1.56-6.71], P=0.002). CONCLUSIONS: The urinary proteomic panel developed in this study demonstrates diagnostic and prognostic potential for identifying RVD in idiopathic dilated cardiomyopathy, providing a promising noninvasive tool for precise detection and clinical risk stratification.

Humans↗
PubMed2026

Crosstalk between cysteine and lysine modifications: Integrating redox and metabolic regulation.

Protein post-translational modifications (PTMs) on amino acid residues enable dynamic cellular responses to changes in metabolic and redox state. Cysteine and lysine are among the most extensively modified amino acid residues, with both undergoing a diversity of acylation and oxidative modifications. Indeed, proximal (<10&#x202f;&#xc5;) cysteine and lysine residues may form integration nodes for crosstalk between metabolism and redox homeostasis pathways. This review highlights the interaction of proximal Cys-Lys residues, including influence on residue pKa by local electrostatics, cysteine-to-lysine transfer of PTM moieties, and covalent crosslinking. We discuss candidate Cys-Lys regulatory pairs in proteins involved in redox regulation, proteostasis, metabolic adaptation and inflammation. We further utilize computational modeling to identify proximity between cysteine and lysine residues in proteins known to be regulated by acylation and oxidative PTMs, and to demonstrate changes in these distances and local electrostatic potential due to lysine acetylation. Finally, we review how mass spectrometry-based proteomics and machine-learning PTM predictive tools can enable the identification, validation, and interpretation of proximal Cys-Lys interactions that regulate cellular responses to oxidative challenge and metabolic flux.

Cysteine↗
PubMed2003

Categorization of sentence types in medical abstracts.

This study evaluated the use of machine learning techniques in the classification of sentence type. 7253 structured abstracts and 204 unstructured abstracts of Randomized Controlled Trials from MedLINE were parsed into sentences and each sentence was labeled as one of four types (Introduction, Method, Result, or Conclusion). Support Vector Machine (SVM) and Linear Classifier models were generated and evaluated on cross-validated data. Treating sentences as a simple "bag of words", the SVM model had an average ROC area of 0.92. Adding a feature of relative sentence location improved performance markedly for some models and overall increasing the average ROC to 0.95. Linear classifier performance was significantly worse than the SVM in all datasets. Using the SVM model trained on structured abstracts to predict unstructured abstracts yielded performance similar to that of models trained with unstructured abstracts in 3 of the 4 types. We conclude that classification of sentence type seems feasible within the domain of RCT's. Identification of sentence types may be helpful for providing context to end users or other text summarization techniques.

Abstracting and Indexing↗
PubMed1996

Machine learning paradigms for pattern recognition and image understanding.

In this paper some issues are considered related to the encoding of spatial information and associated perceptual learning algorithms which, it is claimed, are necessary for robust pattern and object recognition in multi-object (natural) scenes. The types of learning requirements within a 'recognition-by-parts' paradigm are contrasted with findings from alternative models.

Form Perception↗