ArticleJournal of computational biology : a journal of computational molecular cell biology2018
A Curated Target Gene Pool Assisting Early Disease Prediction and Patient-Specific Treatment for Small Cell Lung Cancer.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
Hundreds of genes have been linked to small cell lung cancer (SCLC), presenting multiple levels of connections with the disease. The question is whether these genes are sufficient as genetic biomarkers for the early diagnosis and personalized treatment of SCLC. An SCLC genetic database was developed through comprehensive ResNet relationship data analysis, where 557 SCLC target genes were curated. Multiple levels of associations between these genes and SCLC were studied. Then, a sparse representation-based variable selection (SRVS) was employed for gene selection for four SCLC gene expression data sets, followed by a case-control classification. Results were compared with that of analysis of variance (ANOVA)-based gene selection approaches. Using SRVS, a gene vector was selected for each data set, leading to significant higher classification accuracy compared with randomly selected genes (100%, 77.12%, 100%, and 100%; permutation p values: 0.017, 0.00060, 0.012, and 0.0066). The SRVS method outperformed ANOVA in terms of classification ratio. The genes were selected within the 557 SCLC gene pool, showing data set and method specificity. Our results suggested that for a given SCLC patient group, there might exist a gene vector in the 557 curated SCLC genes that possess significant prediction power. SRVS is effective for identifying the optimum gene subset targeting personalized treatment.
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.