Identification of variants, genes and pathways in synucleinopathies using bioinformatics and machine learning
Bibliographic record
Abstract
Synucleinopathies are a group of neurodegenerative diseases characterized by the presence of alpha-synuclein in the brain of the patient.Synucleinopathies are primarily composed of Parkinson's disease (PD), dementia with Lewy body (DLB) and multiple system atrophies (MSA).Currently, there is no cure for any of the above-mentioned disorders.Since degeneration occurs before disease diagnosis, drugs and therapeutics slowing down or stopping disease progression are of crucial importance.Early diagnosis is important for patients to administer treatment before severe degeneration occurs.For example, REM-sleep behavior disorder (RBD) is considered one of the best predictors for synucleinopathies as more than 80% of patients phenoconvert to PD, DLB or MSA.Genetic studies have been conducted to characterize the genetic landscape of synucleinopathies.Familial PD revealed monogenic PD genes such as LRRK2, PRKN, PINK1, and DJ-1 and case-control studies identified PD risk factors such as GBA1.Genome-wide association studies (GWAS) nominated genes such as GBA1, SNCA and TMEM175 that were central to PD, DLB and RBD.Many other genes were found to be distinct for a certain disorder such as LRRK2 in PD and APOE in DLB.Although GWAS nominated numerous novel loci, most of these loci have unknown causal genes due to a lack of additional biological evidence.In this thesis, I used bioinformatics and machine learning to characterize patients and identify novel genetic targets.In Chapter 2, I investigated the association of heterozygous PRKN single nucleotide variants (SNVs) and copy number variations (CNVs) with PD.While PRKN is an autosomal recessive PD gene, the role of heterozygous PRKN variants is controversial.Using targeted next-generation sequencing, we sequenced the coding and untranslated region of PRKN
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".