MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

KRAS is a prognostic biomarker associated with diagnosis and treatment in multiple cancers.

KRAS encodes K-Ras proteins, which take part in the MAPK pathway. The expression level of KRAS is high in tumor patients. Our study compared KRAS expression levels between 33 kinds of tumor tissues. Additionally, we studied the association of KRAS expression levels with diagnostic and prognostic values, clinicopathological features, and tumor immunity. We established 22 immune-infiltrating cell expression datasets to calculate immune and stromal scores to evaluate the tumor microenvironment. KRAS genes, immune check-point genes and interacting genes were selected to construct the PPI network. We selected 79 immune checkpoint genes and interacting related genes to calculate the correlation. Based on the 33 tumor expression datasets, we conducted GSEA (genome set enrichment analysis) to show the KRAS and other co-expressed genes associated with cancers. KRAS may be a reliable prognostic biomarker in the diagnosis of cancer patients and has the potential to be included in cancer-targeted drugs.

iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network.

An enhancer is a short DNA sequence containing many binding sites of transcription factors that plays a crucial role in the gene expression of major eukaryotes. It is difficult to avoid the time consumption and high cost of experimental methods. Therefore, with the continuous development of genomics, it is an urgent task to identify enhancers and their intensities by computational methods. In this paper, we propose a two-layer model called iEnhancer-MRBF, wherein the first layer is used to identify enhancers, and the identified enhancers are divided into strong enhancers and weak enhancers according to their strength in the second layer. In iEnhancer-MRBF, a new classifier multiple Laplacian-regularized radial basis function network (MLR-RBFN) is proposed, and three feature representation methods, namely, kmer, nucleotide binary profiles (NBP) and ac-cumulated nucleotide frequency (ANF), as well as feature selection, are used to process DNA sequences. The experimental results show that the model is significantly better than the previous prediction models, and the test accuracy rates of the first and second layers of independent datasets are 79.75% and 83.50%, respectively.

MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences.

Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).

[Case-control study on cancellous screw and compression bolt in the treatment of calcaneal fractures wih minimally invasive plate].

OBJECTIVE: To evaluate percutaneous reduction by Kirschner pin and internal fixation with cancellous screw and plate through minimal incision at the lateral side of heel for the treatment of intra-articular displacement fracture of calcaneus. METHODS: From September 2009 to June 2012, 80 patients with close intra-articular displacement fractures of calcaneus were divided into treatment group and control group. There were 40 patients in the control group, including 21 males and 19 females, ranging in age from 23 to 64 years old, averaged (39.1 +/-11.7) years old; and the patients were treated with fixation by compress screws and plates of self-broken type. There were 40 patients in the treatment group, including 24 males and 16 females,ranging in age from 21 to 67 years old, averaged (39.6+/-14.3) years old;and the patients were treated with full thread cancellous screws and plates fixation. The fixation time,intra-operative blood loss and the number of injured medial plntar nerve were compared between the two groups. The Böhler angle, Gissane angle and the correction degree of calcaneal width were measured at 1 year after operation. The internal fixators were taken out after 1 year. The AOFAS ankle-hindfoot score was used to evaluate therapeutic effects. RESULTS: All the patients were followed up,and the duration ranged from 9 to 32 months, with a mean of 16 months. There were shorter operation time, less injury, satisfactory postoperative Böhler angle and calcaneal width in treatment group compared with control group,which reduced the medial neurovascular injuries, and the plate was easily removed after fracture healing. CONCLUSION: Full thread cancellous screws and plate fixation for the treatment of calcaneal fractures could get same therapeutic effects as compress screws and plate of self-broken type fixation, and also has follow advantages: shortening the operation time, reducing blood loss, easily to be removed, avoiding the medial neurovascular damage, reducing the incidence of complications.