ArticleIEEE journal of biomedical and health informatics2017
Models for Predicting Stage in Head and Neck Squamous Cell Carcinoma Using Proteomic and Transcriptomic Data.
Article in IEEE journal of biomedical and health informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 9 citations in OpenAlex.
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature.Frontiers in genetics · 2021Review
- A novel feature ranking method for prediction of cancer stages using proteomics data.PloS one · 2017Article
- Developing Robust Predictive Models for Head and Neck Cancer across Microarray and RNA-seq Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2015Article
- An Optimized Framework for Cancer Prediction Using Immunosignature.Journal of medical signals and sensorsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Late diagnosis is one of the reasons that head and neck squamous cell carcinoma (HNSCC) patients experience relative five-year survival rates ranging from 40%-66%. The molecular-level differences between early and advanced stage HNSCC may provide insight into therapeutic targets and strategies. Previous bioinformatics studies have shown mixed or limited results in identifying gene and protein markers and in developing models for discriminating between early and advanced stage HNSCC. Thus, we have investigated models for HNSCC stage prediction using RNAseq and reverse phase protein array data from The Cancer Genome Atlas and The Cancer Proteome Atlas. We systematically assessed individual and ensemble binary classifiers, using filter and wrapper feature selection methods, to develop several well-performing models. In particular, integrated models harnessing both data types consistently resulted in better performance. This study identifies informative protein and gene feature sets which may increase understanding of HNSCC progression.
Indexed as
Identifiers
What 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.