Journal of Computational Biology最新文献

Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions and drug combinations. This work introduces a Bidirectional Recurrent Neural Network model for DDI event type prediction (BiRNN-DDI), which simultaneously considers structural relationships and contextual information. Our BiRNN-DDI model constructs drug feature graphs to mine structural relationships. For contextual information, it transforms drug graphs into sequences and employs a two-channel structure, integrating BiRNN, to obtain contextual representations of drug-drug pairs. The model's effectiveness is demonstrated through comparisons with state-of-the-art models on two DDI event-type benchmarks. Extensive experimental results reveal that BiRNN-DDI surpasses other models in accuracy, AUPR, AUC, F1 score, Precision, and Recall metrics on both small and large datasets. Additionally, our model exhibits a lower parameter space, indicating more efficient learning of drug feature representations and prediction of potential DDI event types.

The exploration of pathways and alternative pathways that have a specific function is of interest in numerous chemical contexts. A framework for specifying and searching for pathways has previously been developed, but a focus on which of the many pathway solutions are realizable, or can be made realizable, is missing. Realizable here means that there actually exists some sequencing of the reactions of the pathway that will execute the pathway. We present a method for analyzing the realizability of pathways based on the reachability question in Petri nets. For realizable pathways, our method also provides a certificate encoding an order of the reactions, which realizes the pathway. We present two extended notions of realizability of pathways, one of which is related to the concept of network catalysts. We exemplify our findings on the pentose phosphate pathway. Furthermore, we discuss the relevance of our concepts for elucidating the choices often implicitly made when depicting pathways. Lastly, we lay the foundation for the mathematical theory of realizability.

Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.

The identification of intrinsically disordered proteins and their functional roles is largely dependent on the performance of computational predictors, necessitating a high standard of accuracy in these tools. In this context, we introduce a novel series of computational predictors, termed PDFll (Predictors of Disorder and Function of proteins from the Language of Life), which are designed to offer precise predictions of protein disorder and associated functional roles based on protein sequences. PDFll is developed through a two-step process. Initially, it leverages large-scale protein language models (pLMs), trained on an extensive dataset comprising billions of protein sequences. Subsequently, the embeddings derived from pLMs are integrated into streamlined, yet sophisticated, deep-learning models to generate predictions. These predictions notably surpass the performance of existing state-of-the-art predictors, particularly those that forecast disorder and function without utilizing evolutionary information.

De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches used in the field of mass spectrometry to analyze and sequence proteins. However, these approaches often produce incomplete protein sequences with gaps, namely scaffolds. The protein scaffold filling problem refers to filling the missing amino acids in the gaps of a scaffold to infer the complete protein sequence. In this article, we tackle the protein scaffold filling problem based on generative AI techniques, such as convolutional denoising autoencoder, transformer, and generative pretrained transformer (GPT) models, to complete the protein sequences and compare our results with recently developed convolutional long short-term memory-based sequence model. We evaluate the model performance both on a real dataset and generated datasets. All proposed models show outstanding prediction accuracy. Notably, the GPT-2 model achieves 100% gap-filling accuracy and 100% full sequence accuracy on the MabCampth protein scaffold, which outperforms the other models.

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the diagnosis of depression and promote its automatic identification. However, these methods still have some limitations. The current approaches overlook the importance of subgraphs in brain graphs, resulting in low accuracy. Using these methods with low accuracy for FC analysis may lead to unreliable results. To address these issues, we have designed a graph neural network-based model called AFMDD, specifically for analyzing FC features of depression and depression identification. Through experimental validation, our model has demonstrated excellent performance in depression diagnosis, achieving an accuracy of 73.15%, surpassing many state-of-the-art methods. In our study, we conducted visual analysis of nodes and edges in the FC networks of depression and identified several novel FC features. Those findings may provide valuable clues for the development of biomarkers for the clinical diagnosis of depression.

Time-lapse microscopy imaging is a crucial technique in biomedical studies for observing cellular behavior over time, providing essential data on cell numbers, sizes, shapes, and interactions. Manual analysis of hundreds or thousands of cells is impractical, necessitating the development of automated cell segmentation approaches. Traditional image processing methods have made significant progress in this area, but the advent of deep learning methods, particularly those using U-Net-based networks, has further enhanced performance in medical and microscopy image segmentation. However, challenges remain, particularly in accurately segmenting touching cells in images with low signal-to-noise ratios. Existing methods often struggle with effectively integrating features across different levels of abstraction. This can lead to model confusion, particularly when important contextual information is lost or the features are not adequately distinguished. The challenge lies in appropriately combining these features to preserve critical details while ensuring robust and accurate segmentation. To address these issues, we propose a novel framework called RA-SE-ASPP-Net, which incorporates Residual Blocks, Attention Mechanism, Squeeze-and-Excitation connection, and Atrous Spatial Pyramid Pooling to achieve precise and robust cell segmentation. We evaluate our proposed architecture using an induced pluripotent stem cell reprogramming dataset, a challenging dataset that has received limited attention in this field. Additionally, we compare our model with different ablation experiments to demonstrate its robustness. The proposed architecture outperforms the baseline models in all evaluated metrics, providing the most accurate semantic segmentation results. Finally, we applied the watershed method to the semantic segmentation results to obtain precise segmentations with specific information for each cell.

Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in gene regulation within cells, and mutations and abnormal expression of miRNA can cause a myriad of intricate diseases. Forecasting the association between miRNA and diseases can enhance disease prevention and treatment and accelerate drug research, which holds considerable importance for the development of clinical medicine and drug research. This investigation introduces a contrastive learning-augmented hypergraph neural network model, termed CLHGNNMDA, aimed at predicting associations between miRNAs and diseases. Initially, CLHGNNMDA constructs multiple hypergraphs by leveraging diverse similarity metrics related to miRNAs and diseases. Subsequently, hypergraph convolution is applied to each hypergraph to extract feature representations for nodes and hyperedges. Following this, autoencoders are employed to reconstruct information regarding the feature representations of nodes and hyperedges and to integrate various features of miRNAs and diseases extracted from each hypergraph. Finally, a joint contrastive loss function is utilized to refine the model and optimize its parameters. The CLHGNNMDA framework employs multi-hypergraph contrastive learning for the construction of a contrastive loss function. This approach takes into account inter-view interactions and upholds the principle of consistency, thereby augmenting the model's representational efficacy. The results obtained from fivefold cross-validation substantiate that the CLHGNNMDA algorithm achieves a mean area under the receiver operating characteristic curve of 0.9635 and a mean area under the precision-recall curve of 0.9656. These metrics are notably superior to those attained by contemporary state-of-the-art methodologies.

In this study, we develop a comprehensive model to investigate the intricate relationship between the bone remodeling process, tumor growth, and bone diseases such as multiple myeloma. By analyzing different scenarios within the Basic Multicellular Unit, we uncover the dynamic interplay between remodeling and tumor progression. The model developed developed in the paper are based on the well accepted Komarova's and Ayati's models for the bone remodeling process, then these models were modified to include the effects of the tumor growth. Our in silico experiments yield results consistent with existing literature, providing valuable insights into the complex dynamics at play. This research aims to improve the clinical management of bone diseases and metastasis, paving the way for targeted interventions and personalized treatment strategies to enhance the quality of life for affected individuals.