An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significant for comprehending gene expression regulatory systems. While some useful predictive models have been proposed, there are still deficiencies in these models. To address current limitations, we propose a model, DNABERT2-Enhancer, based on transformer architecture and deep learning, designed for the recognition of enhancers (classified as either enhancer or non-enhancer) and the identification of their activity (strong or weak enhancers). More specifically, DNABERT2-Enhancer is composed of a BERT model for extracting features and a CNN model for enhancers classification. Parameters of the BERT model are initialized by a pre-training DNABERT-2 language model. The enhancer recognition task is then fine-tuned through transfer learning to convert the original sequence into feature vectors. Subsequently, the CNN network is employed to learn the feature vector generated by BERT and produce the prediction results. In comparison with existing predictors utilizing the identical dataset, our approach demonstrates superior performance. This suggests that the model will be a useful instrument for academic research on the enhancer recognition.

Prompt engineering provides an efficient way to adapt large language models (LLMs) to downstream tasks without retraining model parameters. However, designing effective prompts can be challenging, especially when model gradients are unavailable and human expertise is required. Existing automated methods based on gradient optimization or heuristic search exhibit inherent limitations under black box or limited-query conditions. We propose Domain-Aware Reinforcement Learning for Prompt Optimization (DA-RLPO), which treats prompt editing as a sequential decision process and leverages structured domain knowledge to constrain candidate edits. Our experimental results show that DA-RLPO achieves higher accuracy than baselines on text classification tasks and maintains robust performance with limited API calls, while also demonstrating effectiveness on text-to-image and reasoning tasks.

DR (Dimensional Decrease) arithmetic is a tool. Initially, DR arithmetic was used to solve problems in computer vision. Here, DR arithmetic is used to decrease the number of features. Contrast among various DR arithmetics is executed to face discrimination. Contrast among various DR arithmetics is executed to face discrimination. Tall order of dimension face pictures is low order of dimension manifolds in nature. The manifold learning arithmetic can decrease the order of dimension by digging the essential structure of the data, draw the inherent features of tall order of dimension face image data, and find the low order of dimension embedded manifold equivalent to the tall order of dimension original data. Contrast among LLE, Isomap arithmetic and modified Isomap arithmetic are performed to face discrimination. The outcomes of the test are gratifying, which it makes clear that DR arithmetic can settle face discrimination problems well.