Ranking near-native candidate protein structures via random forest classification

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

Using amino acids co-occurrence matrices and explainability model to investigate patterns in dengue virus proteins

Background

Dengue is a common vector-borne disease in tropical countries caused by the Dengue virus. This virus may trigger a disease with several symptoms like fever, headache, nausea, vomiting, and muscle pain. Indeed, dengue illness may also present more severe and life-threatening conditions like hemorrhagic fever and dengue shock syndrome. The causes that lead hosts to develop severe infections are multifactorial and not fully understood. However, it is hypothesized that different viral genome signatures may partially contribute to the disease outcome. Therefore, it is plausible to suggest that deeper DENV genetic information analysis may bring new clues about genetic markers linked to severe illness.

Method

Pattern recognition in very long protein sequences is a challenge. To overcome this difficulty, we map protein chains onto matrix data structures that reveal patterns and allow us to classify dengue proteins associated with severe illness outcomes in human hosts. Our analysis uses co-occurrence of amino acids to build the matrices and Random Forests to classify them. We then interpret the classification model using SHAP Values to identify which amino acid co-occurrences increase the likelihood of severe outcomes.

Results

We trained ten binary classifiers, one for each dengue virus protein sequence. We assessed the classifier performance through five metrics: PR-AUC, ROC-AUC, F1-score, Precision and Recall. The highest score on all metrics corresponds to the protein E with a 95% confidence interval. We also compared the means of the classification metrics using the Tukey HSD statistical test. In four of five metrics, protein E was statistically different from proteins M, NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5, showing that E markers has a greater chance to be associated with severe dengue. Furthermore, the amino acid co-occurrence matrix highlight pairs of amino acids within Domain 1 of E protein that may be associated with the classification result.

Conclusion

We show the co-occurrence patterns of amino acids present in the protein sequences that most correlate with severe dengue. This evidence, used by the classification model and verified by statistical tests, mainly associates the E protein with the severe outcome of dengue in human hosts. In addition, we present information suggesting that patterns associated with such severe cases can be found mostly in Domain 1, inside protein E. Altogether, our results may aid in developing new treatments and being the target of debate on new theories regarding the infection caused by dengue in human hosts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04597-y.

Research on RNA secondary structure predicting via bidirectional recurrent neural network

BACKGROUND

RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance.

RESULTS

The algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively.

CONCLUSIONS

The flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.