Mining sequential patterns from uncertain big DNA in the spark framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Big data has become ubiquitous as high volumes of wide varieties of valuable data of different veracities (e.g., precise, imprecise or uncertain data) are made available at a high velocity through fast throughput machines and techniques for data gathering and curation in many real life applications in various domains and application areas such as bioinformatics, biomedicine, finance, social networking, and weather forecasting. In bioinformatics, terabytes of deoxyribonucleic acid (DNA) sequences can now be generated within a few hours with the use of next generation sequencing (NGS) technologies such as Illumina HiSeq X and Illumina Genome Analyzer. Due to the nature of these NGS technologies, generated data are usually inherent with some noise or other forms of error. These uncertain data are embedded with a wealth of information in the form of frequent patterns. Mining frequently occurring patterns (e.g., motifs) from these big uncertain DNA sequences is a challenge in bioinformatics and biomedicine. Many existing algorithms are serial and mine DNA sequence motifs using precise data mining methods. Mining of motifs from big DNA sequences is a computationally intensive task because of the high volume and the associated uncertainty of these DNA sequences. In this paper, we propose a scalable algorithm for high performance computing on bioinformatics. Specifically, our parallel algorithm uses a fault-tolerant collection of resilient distributed datasets (RDDs) in Apache Spark computing framework to mine sequence motifs from uncertain big DNA data. Experimental results show that our algorithm extracts accurate motifs within a short time frame.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it