Predictive Analytics on Genomic Data with High-Performance Computing
Bibliographic record
Abstract
Recent technological advancements and scientific discoveries have revolutionized the current era of genomics. Next-generation sequencing (NGS) technologies have led to tremendous reduction in the sequencing time and given rise to the production and collection of high volumes of genomic datasets. Predicting protein-coding genes from these copious genomic datasets is significant for the synthesis of protein and the understating of the regulatory function of the non-coding region. Methods have been developed to find protein-coding genes from the genome of organisms. Notwithstanding, the recent data explosion in genomics accentuates the need for more efficient algorithms for gene prediction. In this paper, we explore predictive analytics on genomic data. In particular, we present a scalable naïve Bayes-based algorithm that is deployed over a cluster of Apache Spark framework for efficient prediction of genes in the genome of eukaryotic organisms. Evaluation results on the human genome chromosome GRCh37 and GRCh38 show that effectiveness of our algorithm for predictive analytics on genomic data with high-performance computing. high sensitivity, specificity and accuracy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".