Bibliographic record
Abstract
Abstract Motivation The Matlab programming language is widely used for both teaching and research in engineering, computer science, and mathematics. Despite its many strengths, it has never been a dominant language in computational genomics or bioinformatics more generally. Results Here, we introduce COGEM, a long-term project to develop computational genomics functionality in Matlab. The initial release provides functions for manipulating genomic intervals, stranded or unstranded, with or without numerical data associated. It includes features for both text and binary file input and output, conversion between BAM, BED and BEDGRAPH formats, and numerous functions for manipulating intervals, including shifting, expanding, overlapping, intersecting, unioning, finding nearest intervals, piling up intervals, and performing unary and binary numerical and logical operations on sets of intervals. The toolbox is well-suited to the analysis of high-throughput sequencing data. We demonstrate its functionality by creating a ChIP-seq peak-calling algorithm by chaining together a series of commands, and find it capable of analyzing genome-scale data in reasonable time. Availability The current toolbox and reference manual is available as supplementary material, and updated versions will be maintained at www.perkinslab.ca online.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.061 |
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".