Spark-based data analytics of sequence motifs in large omics data
Bibliographic record
Abstract
Data explosion in bioinformatics in recent years has led to new challenges for researchers to develop novel techniques to discover new knowledge from the avalanche of omics data (e.g., genomics, proteomics, transcriptomics). These data are embedded with a wealth of information including frequently repeated patterns (i.e., sequence motifs). In genomics, deoxyribonucleic acid (DNA) sequence motifs are short repeated contiguous frequent subsequences located in the prompter region. Due to the high volume and various degrees of veracity of these DNA datasets generated by the next-generation sequencing techniques, sequence motif mining from DNA sequences poised a major challenge in bioinformatics. In this article, we present a distributed sequential algorithm—which uses the MapReduce programming model on a cluster of homogeneous distributed-memory system running on an Apache Spark computing framework—for DNA sequence motif mining. Experimental results show the effectiveness of our algorithm in Spark-based data analytics of sequence motifs in large omics data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".