ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.

Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

MiR-2b-3p Downregulated PxTrypsin-9 Expression in the Larval Midgut to Decrease Cry1Ac Susceptibility of the Diamondback Moth, Plutella xylostella (L.).

Crystal (Cry) toxins, produced by Bacillus thuringiensis, are widely used as effective biological pesticides in agricultural production. However, insects always quickly evolve adaptations against Cry toxins within a few generations. In this study, we focused on the Cry1Ac protoxin activated by protease. Our results identified PxTrypsin-9 as a trypsin gene that plays a key role in Cry1Ac virulence in Plutella xylostella larvae. In addition, P. xylostella miR-2b-3p, a member of the micoRNA-2 (miR-2) family, was significantly upregulated by Cry1Ac protoxin and targeted to PxTrypsin-9 downregulated its expression. The mRNA level of PxTrypsin-9, regulated by miR-2b-3p, revealed an increased tolerance of P. xylostella larvae to Cry1Ac at the post-transcriptional level. Considering that miR-2b and trypsin genes are widely distributed in various pest species, our study provides the basis for further investigation of the roles of miRNAs in the regulation of the resistance to Cry1Ac and other insecticides.

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts. METHODS: We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients. RESULTS: Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values. CONCLUSIONS: Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .

Functional status analysis of RNH1 in bladder cancer for predicting immunotherapy response.

Bladder cancer (BLCA) typically has a poor prognosis due to high rates of relapse and metastasis. Although the emergence of immunotherapy brings hope for patients with BLCA, not all patients will benefit from it. Identifying some markers to predict treatment response is particularly important. Here, we aimed to determine the clinical value of the ribonuclease/angiogenin inhibitor 1 (RNH1) in BLCA therapy based on functional status analysis. First, we found that RNH1 is aberrantly expressed in multiple cancers but is associated with prognosis in only a few types of cancer. Next, we determined that low RNH1 expression was significantly associated with enhanced invasion and metastasis of BLCA by assessing the relationship between RNH1 and 17 functional states. Moreover, we identified 95 hub genes associated with invasion and metastasis among RNH1-related genes. Enrichment analysis revealed that these hub genes were also significantly linked with immune activation. Consistently, BLCA can be divided into two molecular subtypes based on these hub genes, and the differentially expressed genes between the two subtypes are also significantly enriched in immune-related pathways. This indicates that the expression of RNH1 is also related to the tumour immune response. Subsequently, we confirmed that RNH1 shapes an inflammatory tumour microenvironment (TME), promotes activation of the immune response cycle steps, and has the potential to predict the immune checkpoint blockade (ICB) treatment response. Finally, we demonstrated that high RNH1 expression was significantly associated with multiple therapeutic signalling pathways and drug targets in BLCA. In conclusion, our study revealed that RNH1 could provide new insights into the invasion of BLCA and predict the immunotherapy response in patients with BLCA.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning.

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .