Meeting abstracts from the 2020 UCD School of Medicine Summer Student Research Awards (SSRA 2020)
Bibliographic record
Abstract
Melanin, derived from the oxidation and polymerization of tyrosine is a light and free radical absorbing pigment.Melanin is synthesized in melanosomes an organelle subset within melanocyte's and have a complex biosynthesis reaction, induced by exposure to UV stimulation and catalyzed by various enzymes, proteins/genes that results in either eumelanin or pheomelanin [1].This project started by extensively researching for an array of hypo, hyper and mixed hyper/hypo pigmentation disorders and their various associated genes/proteins using the OpenTarget database.These 174 genes/protein associations were represented using the cytoscape software to create a 'diseasome' (174 genes and 104 pigmentation disorders).Diseasomes provide a comprehensive approach into network medicine to understand how complex a specific gene is, and how a gene can build a relationship with other genes or phenotypes[2].In a second diseasome, we analyzed to which other non-pigmentary diseases the 174 genes are associated too.Indeed, we found several phenotypes/diseases (neurological, genetic and coronary etc..) involved in pigmentation disorders, showing us these genes are distributed all throughout the human body.Next, we generated a high-confidence map of the known pigmentation pathways and all their own associated genes, enzymes and proteins.We then complemented these known interactions with binary interactions using the Bioplex database (48 novel interactions, and 45 proteins/genes), of which 69% of the genes are associated to pigmentation disorders, validating their potential impact within the pigmentation pathway.Among those, HPS6, HPS5, BLOC1S6 and DTNBP1 were the top three scoring interaction proteins.Currently, we are analyzing the impact of mutations on protein function, and hope to obtain further insights into to molecular mechanism underlying pigmentation disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.627 | 0.407 |
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".