ANGDelMut – a web-based tool for predicting and analyzing functional loss mechanisms of amyotrophic lateral sclerosis-associated angiogenin mutations
Bibliographic record
Abstract
<ns4:p> ANGDelMut is a web-based tool for predicting the functional consequences of missense mutations in the angiogenin (ANG) protein, which is associated with amyotrophic lateral sclerosis (ALS). Missense mutations in ANG result in loss of either ribonucleolytic activity or nuclear translocation activity or both of these functions, and in turn cause ALS. However, no web-based tools are available to predict whether a newly identified ANG mutation will possibly lead to ALS. More importantly, no web-implemented method is currently available to predict the mechanisms of loss-of-function(s) of ANG mutants. In light of this observation, we developed the ANGDelMut web-based tool, which predicts whether an ANG mutation is deleterious or benign. The user selects certain attributes from the input panel, which serves as a query to infer whether a mutant will exhibit loss of ribonucleolytic activity or nuclear translocation activity or whether the overall stability will be affected. The output states whether the mutation is deleterious or benign, and if it is deleterious, gives the possible mechanism(s) of loss-of-function. This web-based tool, freely available at <ns4:ext-link xmlns:ns3="http://www.w3.org/1999/xlink" ext-link-type="uri" ns3:href="http://bioschool.iitd.ernet.in/DelMut/">http://bioschool.iitd.ernet.in/DelMut/</ns4:ext-link> , is the first of its kind to provide a platform for researchers and clinicians, to infer the functional consequences of ANG mutations and correlate their possible association with ALS ahead of experimental findings. </ns4:p>
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".