Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.","authors":"Wei Liu, Zhijie Teng, Zejun Li, Jing Chen","doi":"10.1007/s12539-024-00633-y","DOIUrl":"10.1007/s12539-024-00633-y","url":null,"abstract":"<p><p>Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"990-1004"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.","authors":"Fırat Hardalaç, Haad Akmal, Kubilay Ayturan, U Rajendra Acharya, Ru-San Tan","doi":"10.1007/s12539-024-00647-6","DOIUrl":"10.1007/s12539-024-00647-6","url":null,"abstract":"<p><p>Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeutic intervention, can be diagnosed using baseline FHR and its reaction to uterine contractions. Using CTG, a pragmatic machine learning strategy based on feature reduction and hyperparameter optimization was suggested in this study to classify the various fetal states (Normal, Suspect, Pathological). An application of this strategy can be a decision support tool to manage pregnancies. On a public dataset of 2126 CTG recordings, the model was assessed using various standard CTG dataset specific and relevant classifiers. The classifiers' accuracy was improved by the proposed method. The model accuracy was increased to 97.20% while using Random Forest (best classifier). Practically speaking, the model was able to correctly predict 100% of all pathological cases and 98.8% of all normal cases in the dataset. The proposed model was also implemented on another public CTG dataset having 552 CTG signals, resulting in a 97.34% accuracy. If integrated with telemedicine, this proposed model could also be used for long-distance \"stay at home\" fetal monitoring in high-risk pregnancies.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"882-906"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.","authors":"Safia Firdous, Zubair Nawaz, Rizwan Abid, Leo L Cheng, Syed Ghulam Musharraf, Saima Sadaf","doi":"10.1007/s12539-024-00642-x","DOIUrl":"10.1007/s12539-024-00642-x","url":null,"abstract":"<p><p>Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"854-871"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Promoters in Multiple Prokaryotes with Prompt.","authors":"Qimeng Du, Yixue Guo, Junpeng Zhang, Fuping Lu, Chong Peng, Chichun Zhou","doi":"10.1007/s12539-024-00637-8","DOIUrl":"10.1007/s12539-024-00637-8","url":null,"abstract":"<p><p>Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"814-828"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.","authors":"Xiongwen Quan, Xingyuan Ou, Li Gao, Wenya Yin, Guangyao Hou, Han Zhang","doi":"10.1007/s12539-024-00650-x","DOIUrl":"10.1007/s12539-024-00650-x","url":null,"abstract":"<p><p>As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"926-935"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.","authors":"Hua Chai, Weizhen Deng, Junyu Wei, Ting Guan, Minfan He, Yong Liang, Le Li","doi":"10.1007/s12539-024-00641-y","DOIUrl":"10.1007/s12539-024-00641-y","url":null,"abstract":"<p><strong>Background: </strong>Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into tumor molecular subtyping. However, the high dimensionality and small sample size of the data may result in ambiguous and overlapping cancer subtypes during clustering. In this study, we propose a novel contrastive-learning-based approach to address this issue. The proposed end-to-end deep learning method can extract crucial information from the multi-omics features by self-supervised learning for patient clustering.</p><p><strong>Results: </strong>By applying our method to nine public cancer datasets, we have demonstrated superior performance compared to existing methods in separating patients with different survival outcomes (p < 0.05). To further evaluate the impact of various omics data on cancer survival, we developed an XGBoost classification model and found that mRNA had the highest importance score, followed by DNA methylation and miRNA. In the presented case study, our method successfully clustered subtypes and identified 14 cancer-related genes, of which 12 (85.7%) were validated through literature review.</p><p><strong>Conclusions: </strong>Our findings demonstrate that our method is capable of identifying cancer subtypes that are both statistically and biologically significant. The code about COLCS is given at: https://github.com/Mercuriiio/COLCS .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"966-975"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142125654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deniable Encryption Method for Modulation-Based DNA Storage.","authors":"Ling Chu, Yanqing Su, Xiangzhen Zan, Wanmin Lin, Xiangyu Yao, Peng Xu, Wenbin Liu","doi":"10.1007/s12539-024-00648-5","DOIUrl":"10.1007/s12539-024-00648-5","url":null,"abstract":"<p><p>Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the \"plausible\" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"872-881"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141999872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.","authors":"Nicolás J Gallego-Molina, Andrés Ortiz, Juan E Arco, Francisco J Martinez-Murcia, Wai Lok Woo","doi":"10.1007/s12539-024-00634-x","DOIUrl":"10.1007/s12539-024-00634-x","url":null,"abstract":"<p><p>The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1005-1018"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11512920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.","authors":"Guolun Zhong, Hui Liu, Lei Deng","doi":"10.1007/s12539-024-00640-z","DOIUrl":"10.1007/s12539-024-00640-z","url":null,"abstract":"<p><p>The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"951-965"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Luteinizing Hormone Receptor Mutation (LHR^N316S) Causes Abnormal Follicular Development Revealed by Follicle Single-Cell Analysis and CRISPR/Cas9.","authors":"Chen Zhang, Yongqiang Nie, Bufang Xu, Chunlan Mu, Geng G Tian, Xiaoyong Li, Weiwei Cheng, Aijun Zhang, Dali Li, Ji Wu","doi":"10.1007/s12539-024-00646-7","DOIUrl":"10.1007/s12539-024-00646-7","url":null,"abstract":"<p><p>Abnormal interaction between granulosa cells and oocytes causes disordered development of ovarian follicles. However, the interactions between oocytes and cumulus granulosa cells (CGs), oocytes and mural granulosa cells (MGs), and CGs and MGs remain to be fully explored. Using single-cell RNA-sequencing (scRNA-seq), we determined the transcriptional profiles of oocytes, CGs and MGs in antral follicles. Analysis of scRNA-seq data revealed that CGs may regulate follicular development through the BMP15-KITL-KIT-PI3K-ARF6 pathway with elevated expression of luteinizing hormone receptor (LHR). Because internalization of the LHR is regulated by Arf6, we constructed LHR^N316S mice by CRISPR/Cas9 to further explore mechanisms of follicular development and novel treatment strategies for female infertility. Ovaries of LHR^N316S mice exhibited reduced numbers of corpora lutea and ovulation. The LHR^N316S mice had a reduced rate of oocyte maturation in vitro and decreased serum progesterone levels. Mating LHR^N316S female mice with ICR wild type male mice revealed that the infertility rate of LHR^N316S mice was 21.4% (3/14). Litter sizes from LHR^N316S mice were smaller than those from control wild type female mice. The oocytes from LHR^N316S mice had an increased rate of maturation in vitro after progesterone administration in vitro. Furthermore, progesterone treated LHR^N316S mice produced offspring numbers per litter equivalent to WT mice. These findings provide key insights into cellular interactions in ovarian follicles and provide important clues for infertility treatment.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"976-989"},"PeriodicalIF":3.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11512921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}