ArticleBMC cancer2024
Reference-free inferring of transcriptomic events in cancer cells on single-cell data.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.NPJ precision oncology · 2026Article
- Development and validation of a clinical-adipometric nomogram for predicting tumor response to transarterial chemoembolization in hepatocellular carcinoma.Scientific reports · 2026Article
- A distributed fusion framework for breast cancer recurrence prediction using MapReduce.Scientific reports · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- Development and expert radiologist validation of a custom pipeline for simplification of oncology radiology reports using large language model.Frontiers in oncology · 2026Article
- AR and ITGAL: Key Mediators of Andrographis paniculata's Anti-Psoriatic Effects Revealed by Multi-Omics Analysis.Combinatorial chemistry & high throughput screening · 2026Article
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- Radiomics-integrated machine learning framework for quantitative breast cancer diagnosis.Frontiers in artificial intelligence · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.Scientific reports · 2025Article
- Liver cancer knowledge graph construction based on dynamic entity replacement and masking strategies RoBERTa-wwm-large-BiLSTM-CRF model with clinical Chinese EMRs.Frontiers in artificial intelligence · 2025Article
- Comparative RNA-Seq Analysis of Colon Spheroids and Patient-derived Tissues Identifies Non-Canonical Transcript Isoforms of Protein-Coding Genes Implicated in Colon Carcinogenesis.Cancer informatics · 2025Article
- Scaling transformers to high-dimensional sparse data: a Reformer-BERT approach for large-scale classification.Frontiers in artificial intelligence · 2025Article
- Integrated analysis for drug repositioning in migraine using genetic evidence and claims database.Frontiers in big data · 2025Article
- Advancements in real-time oncology diagnosis: harnessing AI and image fusion techniques.Frontiers in oncology · 2025Review
- Evaluation of the accuracy of large language models in answering bone cancer-related questions.Frontiers in public health · 2025Article
- Improving early liver metastasis detection in colorectal cancer using a weighted ensemble of ResNet50 and swin transformer: a KHCC study.Frontiers in big data · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundCancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference.
resultsVia our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment.
conclusionsOur results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.