Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information for this task. However, prior studies assume complete data, an unrealistic setting as methylation profiles frequently contain missing entries due to limited coverage and experimental artifacts. Our work challenges these favorable scenarios and introduces ReMAC, an extension of ReMasker designed to tackle classification tasks on high-dimensional data under complete and incomplete regimes. Evaluation on real clinical data demonstrates that ReMAC achieves strong and robust performance compared to competing classification methods in the stratification of ST. Code is available: https://github.com/roshni-mahtani/ReMAC.

Multi-omics data integration is crucial for understanding complex diseases, yet limited sample sizes, noise, and heterogeneity often reduce predictive power. To address these challenges, we introduce Omics-GAN, a Generative Adversarial Network (GAN)-based framework designed to generate high-quality synthetic multi-omics profiles while preserving biological relationships. We evaluated Omics-GAN on three omics types (mRNA, miRNA, and DNA methylation) using the ROSMAP cohort for Alzheimer's disease (AD) and TCGA datasets for colon and liver cancer. A support vector machine (SVM) classifier with repeated 5-fold cross-validation demonstrated that synthetic datasets consistently improved prediction accuracy compared to original omics profiles. The AUC of SVM for mRNA improved from 0.72 to 0.74 in AD, and from 0.68 to 0.72 in liver cancer. Synthetic miRNA enhanced classification in colon cancer from 0.59 to 0.69, while synthetic methylation data improved performance in liver cancer from 0.64 to 0.71. Boxplot analyses confirmed that synthetic data preserved statistical distributions while reducing noise and outliers. Feature selection identified significant genes overlapping with original datasets and revealed additional candidates validated by GO and KEGG enrichment analyses. Finally, molecular docking highlighted potential drug repurposing candidates, including Nilotinib for AD, Atovaquone for liver cancer, and Tecovirimat for colon cancer. Omics-GAN enhances disease prediction, preserves biological fidelity, and accelerates biomarker and drug discovery, offering a scalable strategy for precision medicine applications.

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. In this paper, we analyze lung cancer multi-omics datasets from The Cancer Genome Atlas (TCGA). Using four statistical methods, LIMMA, the T test, Canonical Correlation Analysis (CCA), and the Wilcoxon test, we identified differentially expressed genes across gene expression, DNA methylation, and miRNA expression data. We then integrated these multi-omics data using the Kernel Machine Regression (KMR) approach. Our findings reveal significant interactions among the three omics: gene expression, miRNA expression, and DNA methylation in lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. From our data analysis, we identified 38 genes significantly associated with lung cancer. Among these, eight genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that could play a crucial role in the treatment of lung cancer. These proposed drugs are also supported by the findings of other independent studies, which underscore their potential efficacy in the fight against lung cancer.

When used correctly, they can help us unpick some of the mysteries of our biology, and our mortality. Be honest: Have you ever looked up someone from your childhood on social media with the sole intention of seeing how they've aged? One of my colleagues, who shall remain nameless, certainly has. He recently shared a photo of a former classmate. "Can you believe we're the same age?" he asked, with a hint of glee in his voice. A relative also delights in this pastime. "Wow, she looks like an old woman," she'll say when looking at a picture of someone she has known since childhood. The years certainly are kinder to some of us than others. But wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging, under the hood. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging (such as elevated cholesterol or markers of inflammation), might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Longevity clinics offer a mix of services that largely cater to the wealthy.

Omer Abid, Gholamreza Rafiee * Abstract -- Medulloblastoma is a malignant pediatric brain cancer, and the discovery of molecular subgroups is enabling personalized treatment strategies. In 2019, a consensus identified eight novel subtypes within Groups 3 and 4, each displaying heterogeneous chara cteristics. Classifiers are essential for translating these findings into clinical practice by supporting clinical trials, personalized therapy development and application, and patient monitoring. This study presents a DNA methylation - based, cross - platform machine learning classifier capable of distinguishing these subtypes on both HM450 and EPIC methylation array samples . Across two independent test sets, the model achieved weighted F1 = 0.95 and balanced accuracy = 0.957, consistent across platforms. As the first cross - platform solution, it provides backward compatibility while extending applicability to a newer platform, also enhancing accessibility. It also has the potential to become the first publicly available classifier for these subtypes once deployed through a web application, as planned in the future . Th is work overall takes steps in the direction of advancing precision medicine and improving clinical outcomes for patients within the majority prevalence medulloblastoma subgroups, g roups 3 and 4. Keywords -- Medulloblastoma, Molecular Subgroup Classification, Machine Learning, AI for Health Medulloblastoma is a malignant brain cancer widely known for its prevalence in children. Through extensive treatment strategies based on surgery, chemotherapy and radiation therapy, approximately 75% of the patient are able to survive in the long term [1]. These treatments whi le crucial also come along with negative side effects, effecting patients' li ves [1] [2], especially considering the implications on the growing children. However, with advancement in genomics, molecular subgroups have been discover ed within the disease . T hese subgroups have shown to be heterogenous in clinical, biological and outcomes perspective [3] . These in fact are now considered better definition of disease behaviour than conventional techniques [3] .

Integrating multi-omics data, such as DNA methylation, mRNA expression, and microRNA (miRNA) expression, offers a comprehensive view of the biological mechanisms underlying disease. However, the high dimensionality and complex interactions among omics layers present major challenges for predictive modeling. We propose Multi-Omics integration with Tree-generated Graph Neural Network (MOTGNN), a novel and interpretable framework for binary disease classification. MOTGNN employs eXtreme Gradient Boosting (XGBoost) to perform omics-specific supervised graph construction, followed by modality-specific Graph Neural Networks (GNNs) for hierarchical representation learning, and a deep feedforward network for cross-omics integration. On three real-world disease datasets, MOTGNN outperforms state-of-the-art baselines by 5-10% in accuracy, ROC-AUC, and F1-score, and remains robust to severe class imbalance (e.g., 87.2% vs. 33.4% F1 on imbalanced data). The model maintains computational efficiency through sparse graphs (2.1-2.8 edges per node) and provides built-in interpretability, revealing both top-ranked biomarkers and the relative contributions of each omics modality. These results highlight MOTGNN's potential to improve both predictive accuracy and interpretability in multi-omics disease modeling.

Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant hurdles, especially for researchers lacking a biomedical background. Here, we introduce the CMOB, the first large-scale cancer multi-omics benchmark integrates the TCGA platform, making data resources accessible and usable for machine learning researchers without significant preparation and expertise.To date, CMOB includes a collection of 20 cancer multi-omics datasets covering 32 cancers, accompanied by a systematic data processing pipeline. CMOB provides well-processed dataset versions to support 20 meaningful tasks in four studies, with a collection of benchmarks. We also integrate CMOB with two complementary resources and various biological tools to explore broader research avenues.All resources are open-accessible with user-friendly and compatible integration scripts that enable non-experts to easily incorporate this complementary information for various tasks. We conduct extensive experiments on selected datasets to offer recommendations on suitable machine learning baselines for specific applications. Through CMOB, we aim to facilitate algorithmic advances and hasten the development, validation, and clinical translation of machine-learning models for personalized cancer treatments. CMOB is available on GitHub (\url{https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark}).

DNA methylation is a crucial regulator of gene transcription and has been linked to various diseases, including autoimmune diseases and cancers. However, diagnostics based on DNA methylation face challenges due to large feature sets and small sample sizes, resulting in overfitting and suboptimal performance. To address these issues, we propose MIRACLE, a novel interpretable neural network that leverages autoencoder-based multi-task learning to integrate multiple datasets and jointly identify common patterns in DNA methylation. MIRACLE's architecture reflects the relationships between methylation sites, genes, and pathways, ensuring biological interpretability and meaningfulness. The network comprises an encoder and a decoder, with a bottleneck layer representing pathway information as the basic unit of heredity. Customized defined MaskedLinear Layer is constrained by site-gene-pathway graph adjacency matrix information, which provides explainability and expresses the site-gene-pathway hierarchical structure explicitly. And from the embedding, there are different multi-task classifiers to predict diseases. Tested on six datasets, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, inflammatory bowel disease, psoriasis, and type 1 diabetes, MIRACLE demonstrates robust performance in identifying common functions of DNA methylation across different phenotypes, with higher accuracy in prediction dieseases than baseline methods. By incorporating biological prior knowledge, MIRACLE offers a meaningful and interpretable framework for DNA methylation data analysis in the context of autoimmune diseases.

Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection. Recent discoveries about Epigenetics regulation processes that occur "on top of" or "in addition to" the genetic basis for inheritance involve changes that affect and improve gene expression. They raise the question of improving genetic algorithms (GAs) by modeling epigenetics operators. This paper proposes a new epigenetics algorithm that mimics the epigenetics phenomenon known as DNA methylation. The novelty of our epigenetics algorithms lies primarily in taking advantage of attention mechanisms and deep learning, which fits well with the genes enhancing/silencing concept. The paper develops theoretical arguments and presents empirical studies to exhibit the capability of the proposed epigenetics algorithms to solve more complex problems efficiently than has been possible with simple GAs; for example, facing two Non-convex (multi-peaks) optimization problems as presented in this paper, the proposed epigenetics algorithm provides good performances and shows an excellent ability to overcome the lack of local optimum and thus find the global optimum.

Eran Halperin, SVP of AI and Machine Learning in Optum Labs and a professor in the departments of Computer Science, Computational Medicine, Anaesthesiology, and Human Genetics at UCLA, gave a keynote talk on using whole-genome methylation patterns as a biomarker for electronic health record (EHR) imputation. Dr Halperin showed that methylation provides a better imputation performance when compared to genetic or EHR data. This approach uses a new tensor deconvolution of bulk DNA methylation to obtain cell-type-specific methylation that is in turn used for imputation. Irene Chen from the Massachusetts Institute of Technology (MIT) gave a keynote describing how to leverage machine learning towards equitable healthcare. Dr Chen demonstrated how to adapt disease progression modeling to account for differences in access to care.