Methods最新文献

{"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":"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":"Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases","authors":"Yuchao Liang ,&nbsp;Yuting Guo ,&nbsp;Yifei Zhai ,&nbsp;Jian Zhou ,&nbsp;Wuritu Yang ,&nbsp;Yongchun Zuo","doi":"10.1016/j.ymeth.2024.07.011","DOIUrl":"10.1016/j.ymeth.2024.07.011","url":null,"abstract":"<div><p>Cervical cancer (CC) is one of the most common gynecological malignancies. Cytological screening, while being the most common and accurate method for detecting cervical cancer, is both time-consuming and costly. Predicting CC based on bioinformatics can assist in the rapid early screening of CC in clinical practice. Most recent CC prediction methods require a large amount of detection data or sequencing data and are not ideal for CC detection in complex disease samples. We developed the Disease trend analysis platform (Dtap), which can quickly predict the occurrence of diseases using only blood routine data. Blood routine data was collected from 1,292 cervical cancer patients, 4,860 patients with complex diseases, and 4,980 healthy individuals from various sources. The results show that the Dtap-based trend model maintained good and stable performance in the prediction task of multiple datasets as well as complex disease samples. Finally, we built DTAPCC (<span><span>http://bioinfor.imu.edu.cn/dtapcc</span><svg><path></path></svg></span>), a Dtap-based CC disease prediction platform, to help users quickly predict CC and visualize trend features.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 108-115"},"PeriodicalIF":4.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141900435","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":"m5c-iDeep: 5-Methylcytosine sites identification through deep learning","authors":"Sharaf J. Malebary ,&nbsp;Nashwan Alromema ,&nbsp;Muhammad Taseer Suleman ,&nbsp;Maham Saleem","doi":"10.1016/j.ymeth.2024.07.008","DOIUrl":"10.1016/j.ymeth.2024.07.008","url":null,"abstract":"<div><p>5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at <span><span>https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 80-90"},"PeriodicalIF":4.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873837","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":"Integrated analysis of patients with bladder cancer from prospective transcription factor activity: Implications for personalized treatment approaches","authors":"Haodong Wei,&nbsp;Xu Luo,&nbsp;Rifang Lan,&nbsp;Yuqiang Xiong,&nbsp;Siru Yang,&nbsp;Shiyuan Wang,&nbsp;Lei Yang,&nbsp;Yingli Lv","doi":"10.1016/j.ymeth.2024.07.006","DOIUrl":"10.1016/j.ymeth.2024.07.006","url":null,"abstract":"<div><p>Transcription factors are a specialized group of proteins that play important roles in regulating gene expression in human. These proteins control the transcription and translation of genes by binding to specific sites on DNA, thereby regulating key biological processes such as cell differentiation, proliferation, immune response, and neural development. Moreover, transcription factors are also involved in apoptosis and the pathogenesis of various diseases. By investigating transcription factors, researchers can uncover the mechanisms of gene regulation in organisms and develop more effective methods for preventing and treating human diseases. In the present study, the Virtual Inference of Protein-activity by Enriched Regulon algorithm was utilized to calculate the protein activity of transcription factors, and the metabolic-related protein activity were used for classifying bladder cancer patients into different subtype. To identify chemotherapy drugs with clinical benefits, the differences in prognosis and drug sensitivity between two distinct subtypes of bladder cancer patients were investigated. Simultaneously, the master regulators that display varying levels of transcription factor activity between two different bladder cancer subtypes were explored. Additionally, the potential transcriptional regulatory mechanisms and targets of these factors were investigated, thereby generating novel insights into bladder cancer research at the transcriptional regulation level.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 32-43"},"PeriodicalIF":4.2,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843052","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":"Quantitation of F-actin in cytoskeletal reorganization: Context, methodology and implications","authors":"Subhashree Shubhrasmita Sahu ,&nbsp;Parijat Sarkar ,&nbsp;Amitabha Chattopadhyay","doi":"10.1016/j.ymeth.2024.07.009","DOIUrl":"10.1016/j.ymeth.2024.07.009","url":null,"abstract":"<div><p>The actin cytoskeleton is involved in a large number of cellular signaling events in addition to providing structural integrity to the cell. Actin polymerization is a key event during cellular signaling. Although the role of actin cytoskeleton in cellular processes such as trafficking and motility has been extensively studied, the reorganization of the actin cytoskeleton upon signaling has been rarely explored due to lack of suitable assays. Keeping in mind this lacuna, we developed a confocal microscopy based approach that relies on high magnification imaging of cellular F-actin, followed by image reconstruction using commercially available software. In this review, we discuss the context and relevance of actin quantitation, followed by a detailed hands-on approach of the methodology involved with specific points on troubleshooting and useful precautions. In the latter part of the review, we elucidate the method by discussing applications of actin quantitation from our work in several important problems in contemporary membrane biology ranging from pathogen entry into host cells, to GPCR signaling and membrane-cytoskeleton interaction. We envision that future discovery of cell-permeable novel fluorescent probes, in combination with genetically encoded actin-binding reporters, would allow real-time visualization of actin cytoskeleton dynamics to gain deeper insights into active cellular processes in health and disease.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 44-58"},"PeriodicalIF":4.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791580","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":"Hydrogel-based hybrid membrane enhances in vitro ophthalmic drug evaluation in the OphthalMimic device","authors":"Geisa N. Barbalho,&nbsp;Manuel A. Falcão,&nbsp;Venâncio A. Amaral,&nbsp;Jonad L. Contarato,&nbsp;Guilherme M. Gelfuso,&nbsp;Marcilio Cunha-Filho,&nbsp;Tais Gratieri","doi":"10.1016/j.ymeth.2024.07.010","DOIUrl":"10.1016/j.ymeth.2024.07.010","url":null,"abstract":"<div><p>Envisaging to improve the evaluation of ophthalmic drug products while minimizing the need for animal testing, our group developed the OphthalMimic device, a 3D-printed device that incorporates an artificial lacrimal flow, a cul-de-sac area, a moving eyelid, and a surface that interacts effectively with ophthalmic formulations, thereby providing a close representation of human ocular conditions. An important application of such a device would be its use as a platform for dissolution/release tests that closely mimic <em>in vivo</em> conditions. However, the surface that artificially simulates the cornea should have a higher resistance (10 min) than the previously described polymeric films (5 min). For this key assay upgrade, we describe the process of obtaining and thoroughly characterizing a hydrogel-based hybrid membrane to be used as a platform base to simulate the cornea artificially. Also, the OphthalMimic device suffered design improvements to fit the new membrane and incorporate the moving eyelid. The results confirmed the successful synthesis of the hydrogel components. The membrane’s water content (86.25 ± 0.35 %) closely mirrored the human cornea (72 to 85 %). Furthermore, morphological analysis supported the membrane’s comparability to the natural cornea. Finally, the performance of different formulations was analysed, demonstrating that the device could differentiate their drainage profile through the viscosity of PLX 14 (79 ± 5 %), PLX 16 (72 ± 4 %), and PLX 20 (57 ± 14 %), and mucoadhesion of PLXCS0.5 (69 ± 1 %), PLX16CS1.0 (65 ± 3 %), PLX16CS1.25 (67 ± 3 %), and the solution (97 ± 8 %). In conclusion, using the hydrogel-based hybrid membrane in the OphthalMimic device represents a significant advancement in the field of ophthalmic drug evaluation, providing a valuable platform for dissolution/release tests. Such a platform aligns with the ethical mandate to reduce animal testing and promises to accelerate the development of safer and more effective ophthalmic drugs.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"230 ","pages":"Pages 21-31"},"PeriodicalIF":4.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141791579","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}