DisFace: A Database of Human Facial Disorders
Bibliographic record
Abstract
Face is an integral part of human body by which an individual communicates in the society. Its importance can be highlighted by the fact that a person deprived of face cannot sustain in the living world. In the past few decades, human face has gained attention of several researchers, whether it is related to facial anthropometry, facial disorder, face transplantation or face reconstruction. Several researches have also shown the correlation between neuropsychiatry disorders and human face and also that how face recognition abilities are correlated with these disorders. Currently, several databases exist which contain the facial images of several individuals captured from different sources. The advantage of these databases is that the images in these databases can be used for testing and training purpose. However, in current date no such database exists which would provide not only facial images of individuals; but also the literature concerning the human face, list of several genes controlling human face, list of facial disorders and various tools which work on facial images. Thus, the current research aims at developing a database of human facial disorders using bioinformatics approach. The database will contain information about facial diseases, medications, symptoms, findings, etc. The information will be extracted from several other databases like OMIM, PubChem, Radiopedia, Medline Plus, FDA, etc. and links to them will also be provided. Initially, the diseases specific for human face have been obtained from already created published corpora of literature using text mining approach. Becas tool was used to obtain the specific task. A dataset will be created and stored in the form of database. It will be a database containing cross-referenced index of human facial diseases, medications, symptoms, signs, etc. Thus, a database on human face with complete existing information about human facial disorders will be developed. The novelty of the database lies in the fact that it is the first of its kind. The front end will be developed using HTML (Hyper Text Mark-up Language) and CSS (Cascading Style Sheets). The back end will be developed using PHP (Hypertext Pre-processor). JAVA Script will be used as scripting language and MySQL (Structured Query Language) will be used for database development as it is most widely used RDBMS (Relational Database Management System). XAMPP (X (cross platform), Apache, MySQL, PHP, Perl) open source web application software will be used as the server.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".