Similar Datasets
Project description:The hepatocyte is a vital cell type responsible for numerous physiological processes in the body. We developed a machine learning-based AI algorithm to monitor morphological changes in hESCs during their differentiation into mature hepatocytes.Furthermore, we developed a modified strategy that safely and efficiently produces HLCs from hESCs.
2025-12-31 | GSE246556 | GEO
Project description:The hepatocyte is a vital cell type responsible for numerous physiological processes in the body. We developed a machine learning-based AI algorithm to monitor morphological changes in hESCs during their differentiation into mature hepatocytes.Furthermore, we developed a modified strategy that safely and efficiently produces HLCs from hESCs.
2025-12-31 | GSE243881 | GEO
Project description:Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
| S-EPMC10940614 | biostudies-literature
Project description:Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).
| S-EPMC11316398 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.
| S-EPMC11221067 | biostudies-literature
Project description:RNA-sequencing (RNA-seq) is widely used for analysis of alternative splicing, but in practice, has inherent biases which hinder its ability to detect and quantify splicing events. To address this, we present a targeted RNA-seq method that specifically enriches for splicing-informative junction-spanning reads. Local Splicing Variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splice junctions of interest. Primers are designed using Optimal Prime, a novel dedicated machine learning algorithm trained on the performance of thousands of primer sequences. LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring several-fold lower sequencing depth. We use LSV-seq to target events with low coverage in GTEx RNA-seq data and discover hundreds of previously hidden tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to capture alternative splicing with exceptional sensitivity and highlight its potential to improve the detection of other RNA features of interest.
2024-12-27 | GSE246294 | GEO
Project description:Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors. In this study, we present a framework to analyze the effects of multi-omics characteristics on the identification of driver genes. We utilize four machine learning algorithms within this framework to detect cancer driver genes in pan-cancer data, including 75 characteristics among 19,636 genes. The 75 features are divided into four types and analyzed using Kullback-Leibler divergence based on CGC genes and non-CGC genes. We detect cancer driver genes in two different ways. One is to detect driver genes from a single feature type, while the other is from the top N features. The first analysis denotes that the mutational features are the best characteristics. The second analysis reveals that the top 45 features are the most effective feature combinations and superior to the mutational features. The top 45 features not only contain mutational features but also three other types of features. Therefore, our study extends the detection of cancer driver genes and provides a more comprehensive understanding of cancer mechanisms.
| S-EPMC9141966 | biostudies-literature
Project description:BackgroundArterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter.MethodsThis study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score.FindingsComplete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers.InterpretationWe have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment.FundingEuropean Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).
| S-EPMC9520210 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.MethodsEmploying a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes. A prognostic model was developed using the least absolute shrinkage and selection operator (LASSO) regression and independently validated against similarly composed HGSOC datasets. Comprehensive gene expression and immune cell infiltration analyses were conducted, employing advanced algorithms like xCell to delineate the immune landscape of HGSOC.ResultsOur investigation unveiled distinctive immune cell infiltration patterns and gene expression profiles within the TME of HGSOC. Notably, the prevalence of exhausted CD8+ T cells in high-risk patient samples emerged as a critical finding, underscoring the dualistic nature of the immune response in OC. The developed prognostic model, incorporating immune cell markers, exhibited robust predictive accuracy for patient outcomes, showing significant correlations with immunotherapy responses and drug sensitivities.ConclusionsThis study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.
| S-EPMC11651752 | biostudies-literature
Project description:Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg.
| S-EPMC8281595 | biostudies-literature