A Prediction Approach for the Functional Effects of Non-Coding Gene Variants
Bibliographic record
Abstract
The aim of this study is to develop an approach for predicting the functional effects of variants of non-coding genes which have great importance in human genetics. Non-coding genes have formed a very vital field of study since they have a high effect on diseases. However, little is known about non-coding genes compared to coding genes, and they are found in the body almost 9 times more than coding genes. This is a critical issue, and i t is very important to predict the effects of these genes, which are so abundant in the body and difficult to understand. This exhibits the motivation of the study described in the paper. For this purpose, an extensive literature review was first conducted, and possible datasets that could be used were examined. Then, using Python programming language, we developed a prediction model with high accuracy. After investigating how important non-coding gene variants are, and in what areas they can be used, we decided to use a functional interaction network from the deep learning models as the most suitable method. We used STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) which is a biological database and web resource of known and predicted protein-protein interactions. As a second step, we generated feature vectors. After checking the overlap of non-coding genes, we extracted three types of feature vectors. Identifying protein interaction network in Python, the outcome describes the interplay between the biomolecules encoded by genes. It allows to understand the complexities of cellular functions, and even predict potential therapeutics. As a last step, we implemented a deep learning model which included three fully connected (FC) layers, also known as dense layers, with dimensions 40, 10, and 2, respectively. Experimental results demonstrate that the proposed method captured high accuracy values.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".