Interdisciplinary Sciences: Computational Life Sciences最新文献

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Graphical Abstract

With the rapid development of NGS technology, the number of protein sequences has increased exponentially. Computational methods have been introduced in protein functional studies because the analysis of large numbers of proteins through biological experiments is costly and time-consuming. In recent years, new approaches based on deep learning have been proposed to overcome the limitations of conventional methods. Although deep learning-based methods effectively utilize features of protein function, they are limited to sequences of fixed-length and consider information from adjacent amino acids. Therefore, new protein analysis tools that extract functional features from proteins of flexible length and train models are required. We introduce DeepPI, a deep learning-based tool for analyzing proteins in large-scale database. The proposed model that utilizes Global Average Pooling is applied to proteins of flexible length and leads to reduced information loss compared to existing algorithms that use fixed sizes. The image generator converts a one-dimensional sequence into a distinct two-dimensional structure, which can extract common parts of various shapes. Finally, filtering techniques automatically detect representative data from the entire database and ensure coverage of large protein databases. We demonstrate that DeepPI has been successfully applied to large databases such as the Pfam-A database. Comparative experiments on four types of image generators illustrated the impact of structure on feature extraction. The filtering performance was verified by varying the parameter values and proved to be applicable to large databases. Compared to existing methods, DeepPI outperforms in family classification accuracy for protein function inference.

Graphical Abstract

The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, databases, and other predictors. Second, various techniques for feature extraction are applied to derive characteristics from protein sequences, which are subsequently amalgamated into the PPSNO predictor for training. Five-fold cross-validation experiments show that PPSNO outperformed existing predictors, such as PSNO, PreSNO, pCysMod, DeepNitro, RecSNO, and Mul-SNO. The PPSNO predictor achieved an impressive accuracy of 92.8%, an area under the curve (AUC) of 96.1%, a Matthews correlation coefficient (MCC) of 81.3%, an F1-score of 85.6%, an SN of 79.3%, an SP of 97.7%, and an average precision (AP) of 92.2%. We also employed ROC curves, PR curves, and radar plots to show the superior performance of PPSNO. Our study shows that fused protein sequence features and two-layer stacked ensemble models can improve the accuracy of predicting SNO sites, which can aid in comprehending cellular processes and disease mechanisms. The codes and data are available at https://github.com/serendipity-wly/PPSNO .

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Liver transplantation is one of the most effective treatments for acute liver failure, cirrhosis, and even liver cancer. The prediction of postoperative complications is of great significance for liver transplantation. However, the existing prediction methods based on traditional machine learning are often unavailable or unreliable due to the insufficient amount of real liver transplantation data. Therefore, we propose a new framework to increase the accuracy of computer-aided diagnosis of complications after liver transplantation with transfer learning, which can handle small-scale but high-dimensional data problems. Furthermore, since data samples are often high dimensional in the real world, capturing key features that influence postoperative complications can help make the correct diagnosis for patients. So, we also introduce the SHapley Additive exPlanation (SHAP) method into our framework for exploring the key features of postoperative complications. We used data obtained from 425 patients with 456 features in our experiments. Experimental results show that our approach outperforms all compared baseline methods in predicting postoperative complications. In our work, the average precision, the mean recall, and the mean F1 score reach 91.22%, 91.70%, and 91.18%, respectively.

The exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and chemical structure information for the prediction of synergistic effects in drug combinations. GTextSyn employs a sentence classification model within the domain of Natural Language Processing (NLP), wherein drugs and cell lines are regarded as entities possessing biochemical relevance. Meanwhile, combinations of drug pairs and cell lines are construed as sentences with biochemical relational significance. To assess the efficacy of GTextSyn, we conduct a comparative analysis with alternative deep learning approaches using a standard benchmark dataset. The results from a five-fold cross-validation demonstrate a 49.5% reduction in Mean Square Error (MSE) achieved by GTextSyn, surpassing the performance of the next best method in the regression task. Furthermore, we conduct a comprehensive literature survey on the predicted novel drug combinations and find substantial support from prior experimental studies for many of the combinations identified by GTextSyn.

In cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours that expand using either glandular fission or invasive growth. A two-dimensional, lattice-based model of sites containing sensitive and resistant cells within individual glands is developed to study the evolution of glandular tumour cells under continuous and adaptive therapies. We found that although both growth models benefit from adaptive therapy's ability to prevent recurrence, invasive growth benefits more from it than fission growth. This difference is due to the migration of daughter cells into neighboring glands that is absent in fission but present in invasive growth. The migration resulted in greater mixing of cells, enhancing competition induced by adaptive therapy. By varying the initial spatial spread and location of the resistant cells within the tumour, we found that modifying the conditions within the resistant cells containing glands affect both fission and invasive growth. However, modifying the conditions surrounding these glands affect invasive growth only. Our work reveals the interplay between growth mechanism and tumour topology in modulating the effectiveness of cancer therapy.

Vascular disease is one of the major causes of death worldwide. Endothelial cells are important components of the vascular structure. A better understanding of the endothelial cell changes in the development of vascular disease may provide new targets for clinical treatment strategies. Single-cell RNA sequencing can serve as a powerful tool to explore transcription patterns, as well as cell type identity. Our current study is based on comprehensive scRNA-seq data of several types of human vascular disease datasets with deep-learning-based algorithm. A gene set scoring system, created based on cell clustering, may help to identify the relative stage of the development of vascular disease. Metabolic preference patterns were estimated using a graphic neural network model. Overall, our study may provide potential treatment targets for retaining normal endothelial function under pathological situations.

Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification.