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Crisp et al. recently reported that 145 human genes have been horizontally transferred from distant species. Here, I re-analyze those genes listed by Crisp et al. as having the highest certainty of having been horizontally transferred, as well as 17 further genes from the 2001 human genome article, and find little or no evidence to support claims of horizontal gene transfer (HGT). Please see related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0607-3

We present strategies and heuristics underlying a search procedure that finds proofs for Gödel’s incompleteness theorems at an abstract axiomatic level. As axioms we take for granted the representability and derivability conditions for the central syntactic notions as well as the diagonal lemma for constructing self-referential sentences. The strategies are logical ones and have been developed to search for natural deduction proofs in classical first-order logic. The heuristics are mostly of a very general mathematical character and are concerned with the goal-directed use of definitions and lemmata. When they are specific to the meta-mathematical context, these heuistics allow us, for example, to move between the object-and meta-theory. Instead of viewing this work as high-level proof search, it can be regarded as a first step in a proof-planning framework: the next refining steps would consist in verifying the axiomatically given conditions. Comparisons with the literature are detailed in Section 4. (The general mathematical heuristics are indeed general: in Appendix B we show that they, together with two simple algebraic facts and the logical strategies, suffice to find a proof of “√2 is not rational”.)

Hyperparameters play a critical role in analyzing predictive performance in machine learning models. They serve to strike a balance between overfitting and underfitting of research-independent features to prevent extremes. Manual tuning and automated techniques are employed to identify the optimal combination and permutation to achieve the best model performance. This study explores the pursuit of the best fit through various hyperparameters. Following Logistic Regression analysis, this research compared Random Forest, Randomized search, Grid search, Genetic, Bayesian, and Optuna machine learning model tuning for the best accuracy of prediction the student The model accuracy was further assessed using confusion matrices and Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) curves for student grade classification. The genetic algorithm's recommended hyperparameter tuning yielded the highest accuracy (82.5%) and AUC-ROC score (90%) for student result classification. Manual tuning with an estimator of 300, criterion entropy, max features of sqrt, and a minimum sample leaf of 10 achieved an accuracy of 81.1%, which closely resembled the performance randomized search cross-validation algorithm. The default random forest model scored the least accuracy (78%). However, this manual tuning process took a lesser time (3.66 s) to fit the model while grid search CV tuned 941.5 s. Hence, this research made significant contributions to optimizing various machine learning models using a range of hyperparameters for grade classification.