A5030484245- Bioinformatics and Genomic Networks
- Computational Drug Discovery Methods
- Cancer Immunotherapy and Biomarkers
- CAR-T cell therapy research
- Cancer Genomics and Diagnostics
- Gene Regulatory Network Analysis
- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- vaccines and immunoinformatics approaches
- Genomics and Rare Diseases
- Genetics, Bioinformatics, and Biomedical Research
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over past several years. However, only a minority respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail predict response. Here, we present machine learning (ML) framework that leverages network-based analyses identify (NetBio) can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes transcriptomic...
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The could (i) predict across four types (median AUROC: 0.79) (ii) identify key pathways with crucial players responsible patient...
Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs do not often recapitulate disease which might be due molecular evolution phenotypic differences species from time last common ancestor. Here, we systematically investigated evolutionary divergence regulatory relationships between transcription factors...
Diverse molecular networks have been extensively studied to discover therapeutic targets and repurpose approved drugs. However, it is necessary select a suitable network since the performance of medicine relies heavily on completeness characteristics selected network. Although using gene essentiality from cancer cells could be an effective platform for identifying anticancer targets, efforts apply these in applications limited. We constructed phenotype-level co-essentiality relationship...
Abstract Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict in a manner, the still have room improve. Here, we devise novel feature based on sequence co-evolution analysis identify construct machine learning (ML) model with state-of-the-art performance. Specifically, relying 28 000...