ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2016
ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed.
- ADAMTS9-AS2 Disrupts Docetaxel-Resistance in Castration-Resistant Prostate Cancer via Stemness Suppression and Ferroptosis Induction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-cell multi-omics reveals DUSP9 as a key regulator of cancer stemness and a potential therapeutic target in hepatocellular carcinoma.Journal of translational medicine · 2026Article
- Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets.Cell genomics · 2025Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- hUSI is a robust transcriptome-based cellular senescence prediction tool.Nature aging · 2025Article
- A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.Nature aging · 2025Article
- Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.Medicine · 2025Article
- Investigating Associations Between Prognostic Factors in Gliomas: Unsupervised Multiple Correspondence Analysis.JMIR bioinformatics and biotechnology · 2025Article
- Investigating combined hypoxia and stemness indices for prognostic transcripts in gastric cancer: Machine learning and network analysis approaches.Biochemistry and biophysics reports · 2025Article
- Pan-cancer analysis of the potential of PEA3 subfamily genes as tumor markers.Scientific reports · 2024Article
- Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance.Molecular therapy. Nucleic acids · 2024Article
- Identification of gasdermin B function in the progression of renal clear cell carcinoma by a pan-cancer analysis.Discover oncology · 2024Article
- Characterization of stem cell landscape and assessing the stemness degree to aid clinical therapeutics in hematologic malignancies.Scientific reports · 2024Article
- Article
- Identification of prognostic stemness-related genes in kidney renal papillary cell carcinoma.BMC medical genomics · 2024Article
- Comprehensive pan-cancer analysis: essential role of ABCB family genes in cancer.Translational cancer research · 2024Article
- Article
- Proteogenomic characterization of primary colorectal cancer and metastatic progression identifies proteome-based subtypes and signatures.Cell reports · 2024Article
- scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.Bioinformatics (Oxford, England) · 2024Article
- Identification and Functional Characterization of PI3K/Akt/mTOR Pathway-Related lncRNAs in Lung Adenocarcinoma: A Retrospective Study.Cell journal · 2024Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.
Indexed as
Identifiers
26776204PMC4856035What OpenQuestion holds
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.