Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

Top gas is a critical reference for scheduling workers to analyze the operation status of a blast furnace (BF). An accurate prediction of top gas indices can help to forecast the operation status of BF. In this brief, an operation status analytical method is proposed based on top gas indices forecast for BF. The Hammerstein system is used to approximate the actual model of top gas indices series. Bayesian technique and reversible jump Markov Chain Monte Carlo method are adopted for parameter and model order identification in the proposed prediction algorithm. In the system, model order, parameters, and regularization parameters cannot be obtained directly and we compute these parameters based on sampling theory. The operation status of BF is reflected by indices such as the contents of CO and CO₂, temperature, and pressure. The confidence regions are computed based on large amounts of top gas indices data, in which the prediction of the indices is considered to be normal. To verify the effectiveness of the proposed method, the top gas of #2 BF from Liuzhou Iron and Steel is employed in the experiments. A series of experiments are reported and the confidence regions of top gas indices are presented for the operation status prediction of BF.

Haze is a major problem in China’s air pollution, which not only hinders economic development, but also causes harm to people’s health. PM2.5 (fine particulate matter) is the primary cause of haze. Therefore, the timely prevention and control of haze benefit the precise forecast of PM2.5 concentration. Air quality has high-dimensional, non-linear and complex characteristics. In this paper, a modified hybrid deep learning model is proposed under the framework of transfer learning, which can solve the problem of air quality prediction in the case of sparse data. The research focuses on solving the problem of inadequate feature extraction in existing studies, and and predicts PM2.5 concentration at multiple sites. In the domain of adaptive extraction of air pollutant characteristics, long and short-term neural networks and multi-layer perceptron are used to realize the long-term dependence and nonlinear transformation of features, respectively. The learned features can be shared by PM2.5 prediction tasks at multiple sites. The channel and spatial attention mechanisms are added to extract the key information in the representation target. In the whole network, the residual neural unit is used to increase the depth of the network and improve prediction accuracy. This paper discusses the experimental results in Beijing dataset from 2013 to 2017 and Hengshui dataset from 2020 to 2022. Based on the findings, it shows that compared with the classical deep learning models, hybrid deep learning models and the most recent transfer learning approaches, the network can obtain higher accuracy and better robustness, especially for the prediction of sites with sparse data. The RMSE value of TL-Modified compared with TL-LSTM and TL-CNN-LSTM models decreased by 38 %, 16.5% and 25.6 % at different sites, respectively.