Methods最新文献

{"title":"Biomedical event causal relation extraction with deep knowledge fusion and Roberta-based data augmentation","authors":"Lishuang Li,&nbsp;Yi Xiang,&nbsp;Jing Hao","doi":"10.1016/j.ymeth.2024.08.007","DOIUrl":"10.1016/j.ymeth.2024.08.007","url":null,"abstract":"<div><p>Biomedical event causal relation extraction (BECRE), as a subtask of biomedical information extraction, aims to extract event causal relation facts from unstructured biomedical texts and plays an essential role in many downstream tasks. The existing works have two main problems: i) Only shallow features are limited in helping the model establish potential relationships between biomedical events. ii) Using the traditional oversampling method to solve the data imbalance problem of the BECRE tasks ignores the requirements for data diversifying. This paper proposes a novel biomedical event causal relation extraction method to solve the above problems using deep knowledge fusion and Roberta-based data augmentation. To address the first problem, we fuse deep knowledge, including structural event representation and entity relation path, for establishing potential semantic connections between biomedical events. We use the Graph Convolutional Neural network (GCN) and the predicated tensor model to acquire structural event representation, and entity relation paths are encoded based on the external knowledge bases (GTD, CDR, CHR, GDA and UMLS). We introduce the triplet attention mechanism to fuse structural event representation and entity relation path information. Besides, this paper proposes the Roberta-based data augmentation method to address the second problem, some words of biomedical text, except biomedical events, are masked proportionally and randomly, and then pre-trained Roberta generates data instances for the imbalance BECRE dataset. Extensive experimental results on Hahn-Powell's and BioCause datasets confirm that the proposed method achieves state-of-the-art performance compared to current advances.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 8-14"},"PeriodicalIF":4.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Godanti bhasma (anhydrous CaSO4) induces massive cytoplasmic vacuolation in mammalian cells: A model for phagocytosis assay","authors":"Subrata K. Das ,&nbsp;Alpana Joshi ,&nbsp;Laxmi Bisht ,&nbsp;Vishakha Goswami ,&nbsp;Abul Faiz ,&nbsp;Gaurav Dutt ,&nbsp;Shiva Sharma","doi":"10.1016/j.ymeth.2024.08.006","DOIUrl":"10.1016/j.ymeth.2024.08.006","url":null,"abstract":"<div><p>Phagocytosis is an essential physiological mechanism; its impairment is associated with many diseases. A highly smart particle is required for understanding detailed sequential cellular events in phagocytosis. Recently, we identified an Indian traditional medicine named Godanti Bhasma (GB), a bioactive calcium sulfate particle prepared by thermo-transformation of<!--> <!-->gypsum. Thermal processing of the gypsum transforms its native physicochemical properties by removing water molecules into the anhydrous GB, which was confirmed by Raman and FT-IR spectroscopy. GB particle showed a 0.5–5 µm size range and a neutral surface charge. Exposure of mammalian cells to GB particles showed a rapid cellular uptake through phagocytosis and induced massive cytoplasmic vacuolation in cells. Interestingly, no cellular uptake and cytoplasmic vacuolation were observed with the parent gypsum particle. The presence of the GB particles in intra-vacuolar space was confirmed using FESEM coupled with EDX. Flow cytometry analysis and live tracking of GB-treated cells showed particle internalization, vacuole formation, particle dissolution, and later vacuolar turnover. Quantification of GB-induced vacuolation was done using neutral red uptake assay in cells. Treatment of lysosomal inhibitors (BFA1 or CQ) with GB could not induce vacuolation, suggesting the requirement of an acidic environment for the vacuolation. In the mimicking experiment, GB particle dissolution in acidic cell-free solution suggested that degradation of GB occurs by acidic pH inside the cell vacuole. Vacuole formation generally accompanies with cell death, whereas GB-induced massive vacuolation does not cause cell death. Moreover, the cell divides and proliferates with the vacuolar process, intra-vacuolar cargo degradation, and eventually vacuolar turnover. Taken together, the sequential cellular events in this study suggest that GB can be used as a smart particle for phagocytosis assay development in animal cells.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 158-168"},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction","authors":"Xiwei Tang ,&nbsp;Wanjun Ma ,&nbsp;Mengyun Yang ,&nbsp;Wenjun Li","doi":"10.1016/j.ymeth.2024.08.008","DOIUrl":"10.1016/j.ymeth.2024.08.008","url":null,"abstract":"<div><p>Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 1-7"},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001890/pdfft?md5=a691264b50021b10f091a9d3d57ce863&pid=1-s2.0-S1046202324001890-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions","authors":"Wenting Ye ,&nbsp;Chen Li ,&nbsp;Wen Zhang ,&nbsp;Jiuyong Li ,&nbsp;Lin Liu ,&nbsp;Debo Cheng ,&nbsp;Zaiwen Feng","doi":"10.1016/j.ymeth.2024.08.009","DOIUrl":"10.1016/j.ymeth.2024.08.009","url":null,"abstract":"<div><p>Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 15-25"},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142103008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework","authors":"Watshara Shoombuatong ,&nbsp;Ittipat Meewan ,&nbsp;Lawankorn Mookdarsanit ,&nbsp;Nalini Schaduangrat","doi":"10.1016/j.ymeth.2024.08.003","DOIUrl":"10.1016/j.ymeth.2024.08.003","url":null,"abstract":"<div><p>Epigenetics involves reversible modifications in gene expression without altering the genetic code itself. Among these modifications, histone deacetylases (HDACs) play a key role by removing acetyl groups from lysine residues on histones. Overexpression of HDACs is linked to the proliferation and survival of tumor cells. To combat this, HDAC inhibitors (HDACi) are commonly used in cancer treatments. However, pan-HDAC inhibition can lead to numerous side effects. Therefore, isoform-selective HDAC inhibitors, such as HDAC3i, could be advantageous for treating various medical conditions while minimizing off-target effects. To date, computational approaches that use only the SMILES notation without any experimental evidence have become increasingly popular and necessary for the initial discovery of novel potential therapeutic drugs. In this study, we develop an innovative and high-precision stacked-ensemble framework, called Stack-HDAC3i, which can directly identify HDAC3i using only the SMILES notation. Using an up-to-date benchmark dataset, we first employed both molecular descriptors and Mol2Vec embeddings to generate feature representations that cover multi-view information embedded in HDAC3i, such as structural and contextual information. Subsequently, these feature representations were used to train baseline models using nine popular ML algorithms. Finally, the probabilistic features derived from the selected baseline models were fused to construct the final stacked model. Both cross-validation and independent tests showed that Stack-HDAC3i is a high-accuracy prediction model with great generalization ability for identifying HDAC3i. Furthermore, in the independent test, Stack-HDAC3i achieved an accuracy of 0.926 and Matthew’s correlation coefficient of 0.850, which are 0.44–6.11% and 0.83–11.90% higher than its constituent baseline models, respectively.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 147-157"},"PeriodicalIF":4.2,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142078708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features","authors":"Muhammad Arif ,&nbsp;Saleh Musleh ,&nbsp;Ali Ghulam ,&nbsp;Huma Fida ,&nbsp;Yasser Alqahtani ,&nbsp;Tanvir Alam","doi":"10.1016/j.ymeth.2024.08.001","DOIUrl":"10.1016/j.ymeth.2024.08.001","url":null,"abstract":"<div><p>Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 129-139"},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001828/pdfft?md5=315d0a8005d4827680fb3f30ae38db5c&pid=1-s2.0-S1046202324001828-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142034770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells","authors":"Yong Li ,&nbsp;Ru Gao ,&nbsp;Shan Liu ,&nbsp;Hongqi Zhang ,&nbsp;Hao Lv ,&nbsp;Hongyan Lai","doi":"10.1016/j.ymeth.2024.08.004","DOIUrl":"10.1016/j.ymeth.2024.08.004","url":null,"abstract":"<div><p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and is responsible for the outbreak of coronavirus disease 19 (COVID-19). Numerous studies have demonstrated that SARS-CoV-2 infection will lead to a significant dysregulation of protein post-translational modification profile in human cells. The accurate recognition of phosphorylation sites in host cells will contribute to a deep understanding of the pathogenic mechanisms of SARS-CoV-2 and also help to screen drugs and compounds with antiviral potential. Therefore, there is a need to develop cost-effective and high-precision computational strategies for specifically identifying SARS-CoV-2-infected phosphorylation sites. In this work, we first implemented a custom neural network model (named PhosBERT) on the basis of a pre-trained protein language model of ProtBert, which was a self-supervised learning approach developed on the Bidirectional Encoder Representation from Transformers (BERT) architecture. PhosBERT was then trained and validated on serine (S) and threonine (T) phosphorylation dataset and tyrosine (Y) phosphorylation dataset with 5-fold cross-validation, respectively. Independent validation results showed that PhosBERT could identify S/T phosphorylation sites with high accuracy and <em>AUC</em> (area under the receiver operating characteristic) value of 81.9% and 0.896. The prediction accuracy and <em>AUC</em> value of Y phosphorylation sites reached up to 87.1% and 0.902. It indicated that the proposed model was of good prediction ability and stability and would provide a new approach for studying SARS-CoV-2 phosphorylation sites.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 140-146"},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced deep learning approaches enable high-throughput biological and biomedicine data analysis","authors":"Leyi Wei","doi":"10.1016/j.ymeth.2024.08.002","DOIUrl":"10.1016/j.ymeth.2024.08.002","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 116-118"},"PeriodicalIF":4.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141999174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel deep learning identifier for promoters and their strength using heterogeneous features","authors":"Aqsa Amjad ,&nbsp;Saeed Ahmed ,&nbsp;Muhammad Kabir ,&nbsp;Muhammad Arif ,&nbsp;Tanvir Alam","doi":"10.1016/j.ymeth.2024.08.005","DOIUrl":"10.1016/j.ymeth.2024.08.005","url":null,"abstract":"<div><p>Promoters, which are short (50–1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named “PROCABLES“, which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron–ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 119-128"},"PeriodicalIF":4.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001853/pdfft?md5=4c4374f8b06a9c662b2af0a84d0208ad&pid=1-s2.0-S1046202324001853-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142015945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based method for predicting DNA N6-methyladenosine sites","authors":"Ke Han ,&nbsp;Jianchun Wang ,&nbsp;Ying Chu ,&nbsp;Qian Liao ,&nbsp;Yijie Ding ,&nbsp;Dequan Zheng ,&nbsp;Jie Wan ,&nbsp;Xiaoyi Guo ,&nbsp;Quan Zou","doi":"10.1016/j.ymeth.2024.07.012","DOIUrl":"10.1016/j.ymeth.2024.07.012","url":null,"abstract":"<div><p>DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 91-98"},"PeriodicalIF":4.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}