Abstract LB-297: Integrative analysis identifies driver lncRNAs in prostate cancer
Bibliographic record
Abstract
Abstract Genome-Wide Association Studies (GWAS) and whole-genome resequencing studies identified thousands of germline risk variants and somatic mutations in cancer. However, efforts to interpret these data have mainly focused on protein coding genes, despite the fact that majority of these genetic alterations locate outside of coding gene exons. A recent curation of over seven thousand RNA-seq data characterized more than fifty-eight thousand expressed long noncoding RNAs (lncRNAs) in human genome, twice as large as the number of protein coding genes. In this study, we integrated lncRNA gene expression, epigenetic and genetic alteration data to nominate potential driver lncRNAs in prostate cancer. We have identified eleven lncRNAs that are regulated by risk SNPs and somatic mutations through both cis and trans actions. The function of two of these lncRNAs in prostate cancer development and progression has been characterized. Our data suggests that lncRNAs may function as driving factors in prostate cancer development and progression. Note: This abstract was not presented at the meeting. Citation Format: Haiyang Guo, Musaddeque Ahmed, Junjie Hua, Yi Liang, Jens Langstein, Housheng Hansen He. Integrative analysis identifies driver lncRNAs in prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-297. doi:10.1158/1538-7445.AM2015-LB-297
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".