Genome and genomics of schistosomes
Bibliographic record
Abstract
Schistosomes infect over 200 million people and 600 million are at risk. Genomics and post-genomic studies of schistosomes will contribute greatly to developing new reagents for diagnostic purposes and new vaccines that are of interest to the biotechnology industry. In this review, the most recent advances in these fields as well as new projects and future perspectives will de described. A vast quantity of data is publicly available, including short cDNA and genomic sequences, complete large genomic fragments, and the mitochondrial genomes of three species of the genus Schistosoma. The physical structure of the genome is being studied by physically mapping large genomic fragments and characterizing the highly abundant repetitive DNA elements. Bioinformatic manipulations of the data have already been carried out, mostly dealing with the functional analysis of the genes described. Specific search tools have also been developed. Sequence variability has been used to better understand the phylogeny of the species and for population studies, and new polymorphic genomic markers are currently being developed. The information generated has been used for the development of post-genomic projects. A small microarray detected genes that were differentially expressed between male and female worms. The identification of two-dimensional spots by mass spectrometry has also been demonstrated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".