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.

SourceResearch connectionFrom yearThrough year

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

At least 343 records · Page 19Linked to original sources

PubMed2026

Comparison of classic statistical methods and machine learning approaches to classify readiness.

MOTIVATION: Predicting physical and cognitive readiness in warfighters is critical for mission success. These predictions can be improved by identifying key biomarkers using multiple omics modalities. The MASTR-E study conducted by McKetney and colleagues is one of the most comprehensive multi-omics studies of saliva samples collected from warfighters, which also applied classic linear statistical (CLS) techniques to discover key biomarkers of readiness. Aligning with McKetney et al.'s assumptions, we operationalize readiness as a binary proxy, where pre-mission samples are labeled as "ready" to reflect a rested, unstressed physiological baseline, while post-mission samples are labeled "not ready" to reflect cumulative physical and cognitive load from the mission. As such, readiness here is not a direct biological or physiological construct, but an inferred state likely dominated by stress-related physiological changes. This assumption and definition is discussed further in the Introduction and Limitations sections. Here, we apply machine learning (ML) analyses to better assess generalizability, consider hidden interactions, and identify nonlinear patterns in the data. We investigated whether ML approaches could predict readiness and identify relevant biomarkers. ML models were trained on proteomics-only or metabolomics-only datasets to classify participants as ready or not ready and important model features were considered as putative biomarkers. Training and testing datasets were curated for two objectives: (i) recognize biomolecular signatures indicative of readiness within the same donor and (ii) assess generalizability across warfighters by withholding donors for testing. RESULTS: Proteomics-based models achieved AUCs of 0.907 ± 0.034 and 0.860 ± 0.063 for Objectives 1 and 2, respectively. Metabolomics-based models achieved Objective 1 AUC of 0.994 ± 0.007 and Objective 2 AUC of 0.993 ± 0.010. Comparative analysis with existing literature validates the model's feature importances, but the identified putative biomarkers significantly differ from those discovered through CLS analyses, as only one ML-identified biomarker overlapping with those identified through CLS methods. We show that these ML models and identified features are more robust to noise and generalizable across participants than those identified using CLS methods. AVAILABILITY: The analysis pipelines are provided as Jupyter notebooks, including all code and documentation, and are available publicly on GitHub at {https://github.com/netrias/ReadinessClassification}.

Machine Learning↗

PubMed2026

Pan-cancer multi-omics machine learning defines a lactylation-associated immune-excluded tumor state with proteomic and experimental corroboration.

BACKGROUND: Histone lactylation links lactate metabolism to chromatin regulation, but whether lactylation-program-associated transcriptional patterns delineate recurrent pan-cancer tumor states remains unclear. METHODS: We integrated mRNA, lncRNA, and miRNA profiles from 9712 TCGA tumors across 33 cancer types with GTEx references, six GEO cohorts, IMvigor210, and an institutional clear-cell renal cell carcinoma (ccRCC) cohort used for exploratory DIA-NN proteomic corroboration. Random-effects co-expression meta-analysis, multi-omics consensus clustering, regulon inference, immune deconvolution, TIDE, oncoPredict, and SHAP-based machine learning were applied. hsa-miR-431-5p was functionally evaluated as a proof-of-concept CS2-associated miRNA in bladder cancer models. RESULTS: LacCoEx-Atlas comprised 398,491 lactylation-related co-expression pairs across 24,667 RNA features under a random-effects framework (median I² = 88.6%). Consensus clustering identified two subtypes: CS2 showed glycolytic-mesenchymal-immune-excluded features, M2 macrophage enrichment, CD8⁺ T-cell depletion, elevated HDAC4/NSD3/KDM6B activity, and worse survival, whereas CS1 showed oxidative, sirtuin-active programs. CS2 had fewer predicted ICI responders (18.3% vs. 52.0%) and a lower observed ORR in IMvigor210 (15.3% vs. 24.0%). oncoPredict identified NU7441 as a hypothesis-generating CS2-associated sensitivity signal (Hedges' g = 1.17). DIA-NN proteomics in 50 ccRCC specimens provided exploratory support for CS2-associated hypoxia, ECM degradation, and metastasis programs. The 10-feature mRNA LARItools model achieved an apparent AUC of 0.9413, while a separate multi-omics model achieved 0.971; neither was independently validated. LARItools reproduced prognostic separation across six GEO cohorts. miR-431-5p promoted malignant phenotypes and EMT in bladder cancer cells, with concordant CMU4h expression findings. CONCLUSIONS: Lactylation-program-associated transcriptional patterns delineate a recurrent immune-excluded pan-cancer tumor state associated with adverse prognosis, reduced predicted immunotherapy responsiveness, exploratory single-cancer protein-level support, and testable DNA damage response-targeting hypotheses. LacCoEx-Atlas and LARItools provide open resources for lactylation-program-associated tumor-state stratification and future translational research.

Humans↗

PubMed2026

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

MOTIVATION: CRISPR-Cas9 genome-editing efficiency is strongly influenced by the sequence composition and positional context of single-guide RNAs (sgRNAs). Although numerous deep learning-based models have been developed to predict Cas9 efficiency from sgRNA sequences, most operate as black boxes, offering limited insight into the sequence determinants underlying Cas9 activity. In addition, previous studies often overlook how the positional context of sequence motifs within sgRNAs influences their effects on Cas9 binding or cleavage. RESULTS: We introduce DeepCC9, an interpretable machine learning framework that combines explicit sequence feature extraction with a residual block-based deep architecture to improve interpretability and identify composition- and position-based motifs governing Cas9 genome-editing efficiency. We applied this method to multiple Cas9 variant datasets, achieving superior predictive performance compared with existing methods while enabling direct interpretation of sequence motifs and their positional effects. Our analysis uncovered 74 sequence motifs enriched or depleted at specific positions within sgRNAs and strongly associated with Cas9 efficiency, providing mechanistic insight into sequence features that influence guide performance. Together, these results establish DeepCC9 as a generalizable and interpretable framework for modeling sequence-function relationships and advancing the understanding of the sequence determinants underlying CRISPR-Cas9 genome editing. AVAILABILITY AND IMPLEMENTATION: The authors have implemented their algorithm in the Python programming language (version 3.X), which is accessible using (https://zenodo.org/records/20073890).

Deep Learning↗

PubMed1997

Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis.

The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.

Cluster Analysis↗

PubMed1997

Computer simulation of neuronal toxicity in the spinal cord.

The use of computers to model biological systems is a relatively new research tool. For example, it is possible to write mathematical systems to model neuronal activity involved in memory and learning and to model blood flow in any organ such as the brain. There is also an interest in designing computer-controlled machines to simulate human activities such as hand movements and vision. One of the most important uses of computer modeling is as a research tool to test hypotheses and aid in formulating new hypotheses. This enables the investigator to apply preliminary tests on several experimental strategies and select for animal experimentation the ones that are most likely to produce unambiguous and interpretable results. In the following article, we describe a computer model of neuron toxicity in the mammalian spinal cord.

Dynorphins↗

PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗

PubMed1995

The Helmholtz machine.

Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.

Algorithms↗

PubMed2026

Predicting enhancer-promoter interactions using a stacking-based ensemble strategy.

MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998.

Promoter Regions, Genetic↗

PubMed1995

Speech technology in the year 2001.

This paper introduces the session "Technology in the Year 2001" and is the first of four papers dealing with the future of human-machine communication by voice. In looking to the future it is important to recognize both the difficulties of technological forecasting and the frailties of the technology as it exists today--frailties that are manifestations of our limited scientific understanding of human cognition. The technology to realize truly advanced applications does not yet exist and cannot be supported by our presently incomplete science of speech. To achieve this long-term goal, the authors advocate a fundamental research program using a cybernetic approach substantially different from more conventional synthetic approaches. In a cybernetic approach, feedback control systems will allow a machine to adapt to a linguistically rich environment using reinforcement learning.

Computers↗

PubMed2026

AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease around the world, affecting 33.6% of the adult population (95% CI: 28.1%-39.5%; I 2 = 99.9%), or roughly one in three. The extent of the liver damage is variable, from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH, formerly NASH), cirrhosis and hepatocellular carcinoma (HCC). Early diagnosis is essential to prevent serious liver damage. Traditional diagnostic techniques such as liver biopsy, imaging, and biomarker testing are all invasive, costly, reduced sensitive to early-stage disease, and they also have variability among observers. Modern diagnostic and prognostic approaches based on the principles of Artificial Intelligence (AI) and specifically on machine learning (ML) and deep learning (DL) have enabled multimodal approaches integrating clinical, imaging and molecular data. This scoping review conducted per PRISMA-ScR guidelines, synthesizes findings from 73 studies (search window 2020-2026) across three dimensions: clinical data driven models, imaging-based classifiers (ultrasound, CT and MRI), and multi-omics (genomics, transcriptomics and proteomics) techniques. Moreover, emergence of models such as U-Net and LiverNet 2.x, classification models like DeepLiverNet and BiLSTM models, as well as transformer frameworks and the identification of biomarkers models are also described. This study also investigates challenges such as data heterogeneity, data interpretability, fairness and real-world clinical application. Finally, important areas of research opportunities and future directions are highlighted to present a developing clinically applicable, explainable and ethical AI solutions to manage MASLD.

MASLD↗

PubMed1994

Application of machine learning to structural molecular biology.

A technique of machine learning, inductive logic programming implemented in the program GOLEM, has been applied to three problems in structural molecular biology. These problems are: the prediction of protein secondary structure; the identification of rules governing the arrangement of beta-sheets strands in the tertiary folding of proteins; and the modelling of a quantitative structure activity relationship (QSAR) of a series of drugs. For secondary structure prediction and the QSAR, GOLEM yielded predictions comparable with contemporary approaches including neural networks. Rules for beta-strand arrangement are derived and it is planned to contrast their accuracy with those obtained by human inspection. In all three studies GOLEM discovered rules that provided insight into the stereochemistry of the system. We conclude machine learning used together with human intervention will provide a powerful tool to discover patterns in biological sequences and structures.

Amino Acid Sequence↗

PubMed2026

Integrating machine learning and GWAS for variant prioritization in the INCIPE cohort highlights ABC transporter genes in chronic kidney disease.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge, affecting approximately 674 million people worldwide and representing one of the fastest-growing causes of mortality. Since CKD is frequently asymptomatic in its early stages, the identification of novel genetic biomarkers may improve early detection and risk stratification. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with CKD and related traits; however, their performance is often limited in small and imbalanced cohorts, where reduced statistical power increases both false-positive and false-negative findings. Machine learning (ML) approaches can complement conventional GWAS by prioritizing biologically relevant genetic signals from high-dimensional genomic data. METHODS: In this study, we implemented a nested ensemble (NCBC) model composed of an undersampler and a CatBoostClassifier (CBC) to prioritize candidate genetic variants associated with CKD in the INCIPE cohort. Prioritized variants were functionally annotated and evaluated through enrichment analyses, GTEx gene expression profiling, and protein-protein interaction network analyses. Genes identified by the CKDGen Consortium were analysed as an external reference set and used to validate the biological relevance of the prioritized results. RESULTS: The NCBC model outperformed conventional ML classifiers, achieving a ROC AUC score of 87.77%, compared to 50%-53% for the other evaluated models. Among the prioritized genes, 56.25% showed protein-protein interactions with genes previously reported by the CKDGen Consortium, whereas only 1.9% of randomly generated gene sets showed interactions. DISCUSSION: Our study demonstrates that the NCBC model improves the prioritization of biologically plausible candidate variants in a small and imbalanced CKD cohort. Functional analyses suggested ABC transporter-related genes, including ABCA13, ABCA4, and ABCC4 genes, as promising candidate for future validation, with ABCA4 showing substantial expression in kidney tissues. Overall, these findings support the integration of ML with GWAS to prioritize candidate genes and investigate the genetic architecture of complex diseases.

SNP prioritization↗

PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

PubMed2026

Senescent fibroblasts drive CD8+ T cell dysfunction in colorectal cancer via CD36-mediated lipid transfer and peroxidation.

BACKGROUND: Functional exhaustion of tumor-infiltrating CD8+ T cells represents a hallmark of colorectal cancer (CRC) immunosuppression, though its mechanistic drivers remain elusive. Given the established correlation between CRC progression and stromal senescence characterized by pathological lipid accumulation and impaired immunity, we investigated whether and how senescent fibroblasts actively regulate CD8+ T cell dysfunction. METHODS: Single-cell RNA sequencing (scRNA-seq) analysis was conducted to unveil the diverse fibroblast populations and the significant lipid metabolism changes between senescent fibroblasts and non-senescent fibroblasts in human CRC specimens and adjacent normal mucosa. Machine-learning identified senescent fibroblasts with a distinct gene signature. Cell-cell communication analysis was used to evaluate the interactions between senescent fibroblasts and CD8+ T cells in colorectal cancer. Co-culture experiments were conducted among senescent fibroblasts, CD8+ T cells and patient-derived organoids of CRC (CRC-PDOs), with the results evaluated with high-content imaging and propidium iodide/Hoechst 33,342 staining. Flow cytometry, ELISA and lipid pulse-chase with BODIPY FL C16 were performed to detect the alterations of CD8+ T cell cytotoxic function and metabolic status. AOM/DSS-induced CRC mouse model was used to conduct in vivo validation to evaluate whether senolytics could suppress CRC progression. Patients from the Cancer Genome Atlas colorectal cancer cohort were stratified into CD36-high and CD36-low groups by median expression, and drug sensitivity for GDSC2 compounds was predicted computationally using the oncoPredict R package. RESULTS: ScRNA-seq demonstrated the specific cell population presence and divergence of senescent fibroblasts between neoplastic and histologically normal adjacent cell clusters in CRC. Random Forest was employed for cell senescence classification. Feature importance analysis identified five genes as key contributors to the model’s decision process. Cell-cell communication analysis revealed enhanced interactions between senescent fibroblasts and CD8+ T cells in CRC. Co-culture of senescent fibroblasts significantly impaired the cytotoxic functions of CD8+ T cells on CRC-PDOs, which was reflected by the declined proportions of granzyme B (GZMB) + and interferon gamma (IFNγ) + CD8+ T cells and enhanced viability of CRC-PDOs. Mechanistically, the co-culture with senescent fibroblasts promoted the lipid shuttling into CD8+ T cells to induce lipid peroxidation and downstream impairment of cytotoxicity. Furthermore, the inhibition of CD36, the specific scavenger receptor for lipid uptake of CD8+ T cells, effectively suppressed lipid transfer and peroxidation thereby preserving the effector functions of CD8+ T cells and ultimately promoting tumor apoptosis. Complementarily, in vivo senolytic treatment significantly suppressed CRC progression in AOM-DSS CRC mouse models. Top 12 therapeutic agents were identified significantly enhanced predicted efficacy in CD36-high tumors. CONCLUSIONS: Our study identified a substantial population of senescent fibroblasts in human CRC through single cell transcriptomics, machine-learning and clinical biopsies. These senescent fibroblasts impair CD8+ T cell-mediated killing of CRC-PDOs via CD36-dependent lipid transfer, suggesting senolytic targeting of stromal cells as a promising immunotherapeutic strategy for CRC.

Colorectal Neoplasms↗

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗

PubMed2026

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning↗

PubMed2026

Development and validation of a serum peptidomic signature for early detection of asymptomatic ovarian cancer: A multi-center prospective study.

Early detection of asymptomatic ovarian cancer (asym-OC) remains a critical challenge, the failure of which underlies its high mortality. Performing serum peptidomic profiling of 843 participants in the cohort SOCFCP, we distill 1,081 initial features into a 7-marker panel for asym-OC detection via a biology-informed machine-learning (ML)-based feature selection strategy. Three markers significantly revert toward non-OC levels after surgery. Integrating the panel with age, CA125, and HE4, we develop and externally validate (n = 159) a LightGBM model, ProMS+. For early-stage OC detection, ProMS+ shows a specificity of 92.6% at 95.0% sensitivity, outperforming CA125 (44.7%), HE4 (11.2%), and Risk of Ovarian Malignancy Algorithm (ROMA) (24.0%), with an area under the curve (AUC) of 0.993. In a simulated high-risk population (n = 100,000; OC prevalence = 1%), ProMS+ yields a high AUC (0.983) and a higher positive predictive value than CA125, HE4, and Age + CA125 + HE4 combined model (0.201 vs. 0.027, 0.090, and 0.064). ProMS+ offers a promising, non-invasive, and interpretable approach for the early detection of asym-OC.

Humans↗

PubMed2025

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗