TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies

Research on time series characteristics of the gas drainage evaluation index based on lasso regression

The evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.

A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies

The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today’s big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets.

Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

Revisiting genome-wide association studies from statistical modelling to machine learning

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures-statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene-gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS.