Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.

BACKGROUND: DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity. RESULTS: In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation. CONCLUSIONS: Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction.

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Patch time allocation and oviposition behavior in response to patch quality and the presence of a generalist predator in Meteorus pulchricornis (Hymenoptera: Braconidae).

Foraging parasitoids often must estimate local risk of predation just as they must estimate local patch value. Here, we investigate the effects a generalist predator Chlaenius bioculatus (Coleoptera: Carabidae), has on the oviposition behavior and the patch residence decisions of a solitary parasitoid Meteorus pulchricornis (Hymenoptera: Braconidae) in response to the varying host quality of Spodoptera litura (Lepidoptera: Noctuidae) larvae (L2 and L4). M. pulchricornis attacked more L4 than on L2 hosts, with the difference in attack rate varying depending on predation treatments, greater in the presence (either actively feeding or not) of the predator than in the absence of it. The parasitoid attacked fewer L2 and L4 hosts when the predator was actively feeding than when it was not feeding or not present in the patch. M. pulchricornis decreased the patch leaving tendency with increasing rejections of hosts, but increased the tendency in response to the presence of the predator as compared with the absence of it, and furthermore, increased the patch leaving tendency when the predator was actively feeding as compared with when it was not. Our study suggests that M. pulchricornis can exploit high quality patches while minimizing predation risk, by attacking more hosts in high quality patches while reducing total patch time in response to risk of predation.