Multiple Linear Regression to predict larvicidal activity against Aedes aegypti mosquito
Bibliographic record
Abstract
Vector-borne diseases are one of the important health problems in most tropical countries. Aedes aegypti is an important vector for transmission of dengue, yellow fever, chikungunya, arthritis, and Zika fever. According to the World Health Organization, it is estimated that Ae. aegypti causes 50 million infections and 25,000 deaths per year. The emerging scenario highlights that the eco-friendly and effective control measures for mosquito vectors is of crucial importance. One of the most effective vector control measures has been the use of larvicidal compounds however; this success was short lived due to development of resistance against them in many mosquito strains, ecological imbalance and undesirable effects on non-target organisms. For this reason, the aim of present study was to deduce a mathematical model to predict the larvicidal action of chemical compounds, based on their structure. A series of different compounds with experimental evidence of larvicidal activity were selected to develop a predictive model, using Multiple Linear Regression and a Genetic Algorithm for the selection of variables, implemented in the QSARINS software. The model was assessed and validated using the OECDs principles. The best model showed good value for the determination coefficient (R2 = 0.752), and others parameters were appropriate for fitting (s = 0.278 and RMSEtr= 0.261). The validation results confirmed that the model has good robustness (Q2LOO=0.682) and stability (R2–Q2LOO=0.070) with low correlation between the descriptors (KXX=0.241), an excellent predictive power (R2ext=0.834) and was product of a non-random correlation (R2Yscr=0.100). The present model shows better parameters than the models reported earlier in the literature, using the same dataset, indicating that the proposed computational tools are more efficient in identifying novel larvicidal compounds against Ae. aegypti.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".