bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.05.22.541684

Identification of immune-related genes for the diagnosis of ischaemic heart failure based on bioinformatics and three machine learning models

Yu, Y.·Xue, Y.·Li, Y.
View original source ↗Export metadata ↓

Abstract

BackgroundThe role of immune cells in the pathogenesis of ischaemic heart failure (IHF) is well-established. However, identifying key diagnostic candidate genes in patients with IHF remains a challenge. Therefore, this study aimed to use bioinformatics and machine learning algorithms to identify potential diagnostic genes for IHF. MethodsTwo IHF datasets were obtained from the GEO database, and key genes for IHF were identified using Limma and WGCNA. Functional enrichment analysis was performed to explore the potential mechanisms of IHF. Next, we used three machine learning algorithms, namely LASSO, RF, and SVM-REF, to identify immune-related diagnostic genes for IHF. ssGSEA enrichment analysis was also completed. To assess the diagnostic value of the identified genes, we developed nomogram and validated them on additional GEO datasets. Finally, an immune infiltration analysis was conducted using the CIBERSORT algorithm to explore immune cell dysregulation in IHF. ResultsOur analysis yielded a total of 92 key genes associated with IHF. Enrichment analysis revealed that the mechanisms underlying IHF are mostly associated with immunity and inflammation. Using the machine learning algorithms, we identified four IHF diagnostic genes, namely RNASE2, MFAP4, CHRDL1, and KCNN3. We constructed nomogram and validated the diagnostic value of these genes on additional GEO datasets. The results showed that these four genes had high diagnostic value (AUC value of 0.961). Furthermore, our immune infiltration analysis revealed the presence of three dysregulated immune cells in IHF, namely Macrophages M2, Monocytes, and T cells gamma delta. ConclusionIn summary, our study identified four potential diagnostic candidate genes for IHF by using bioinformatics and machine learning algorithms. We also explored the potential molecular mechanisms of IHF and the immune cell infiltration environment of the failing myocardium. These findings provide new insights into the pathogenesis, diagnosis, and treatment of IHF.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yu, Y., Xue, Y., Li, Y.. 2023-05-24. Identification of immune-related genes for the diagnosis of ischaemic heart failure based on bioinformatics and three machine learning models. https://doi.org/10.1101/2023.05.22.541684

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗
bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗
bioRxiv2026

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗