Methods最新文献

{"title":"Gluconeogenesis unraveled: A proteomic Odyssey with machine learning","authors":"Seher Ansar Khawaja ,&nbsp;Fahad Alturise ,&nbsp;Tamim Alkhalifah ,&nbsp;Sher Afzal Khan ,&nbsp;Yaser Daanial Khan","doi":"10.1016/j.ymeth.2024.09.002","DOIUrl":"10.1016/j.ymeth.2024.09.002","url":null,"abstract":"<div><div>The metabolic pathway known as gluconeogenesis, which produces glucose from non-carbohydrate substrates, is essential for maintaining balanced blood sugar levels while fasting. It's extremely important to anticipate gluconeogenesis rates accurately to recognize metabolic disorders and create efficient treatment strategies. The implementation of deep learning and machine learning methods to forecast complex biological processes has been gaining popularity in recent years. The recognition of both the regulation of the pathway and possible therapeutic applications of proteins depends on accurate identification associated with their gluconeogenesis patterns. This article analyzes the uses of machine learning and deep learning models, to predict gluconeogenesis efficiency. The study also discusses the challenges that come with restricted data availability and model interpretability, as well as possible applications in personalized healthcare, metabolic disease treatment, and the discovery of drugs. The predictor utilizes statistics moments on the structures of gluconeogenesis and their enzymes, while Random Forest is utilized as a classifier to ensure the accuracy of this model in identifying the best outcomes. The method was validated utilizing the independent test, self-consistency, 10k fold cross-validations, and jackknife test which achieved 92.33 %, 91.87%, 87.88%, and 87.02%. An accurate prediction of gluconeogenesis has significant implications for understanding metabolic disorders and developing targeted therapies. This study contributes to the rising field of predictive biology by mixing algorithms for deep learning, and machine learning, with metabolic pathways.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 29-42"},"PeriodicalIF":4.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142278229","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":"DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest","authors":"Yaser Daanial Khan ,&nbsp;Tamim Alkhalifah ,&nbsp;Fahad Alturise ,&nbsp;Ahmad Hassan Butt","doi":"10.1016/j.ymeth.2024.09.004","DOIUrl":"10.1016/j.ymeth.2024.09.004","url":null,"abstract":"<div><p>Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 26-36"},"PeriodicalIF":4.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241846","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":"New methods in biomolecular nuclear magnetic resonance spectroscopy II","authors":"T. Michael Sabo","doi":"10.1016/j.ymeth.2024.09.006","DOIUrl":"10.1016/j.ymeth.2024.09.006","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 57-60"},"PeriodicalIF":4.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253897","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":"Artificial intelligence and computer-aided drug discovery: Methods development and application","authors":"Haiping Zhang,&nbsp;Yanjie Wei,&nbsp;Konda Mani Saravanan","doi":"10.1016/j.ymeth.2024.09.005","DOIUrl":"10.1016/j.ymeth.2024.09.005","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 55-56"},"PeriodicalIF":4.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241767","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":"Exploring the potential of epigenetic clocks in aging research","authors":"Yuduo Hao ,&nbsp;Kaiyuan Han ,&nbsp;Ting Wang ,&nbsp;Junwen Yu ,&nbsp;Hui Ding ,&nbsp;Fuying Dao","doi":"10.1016/j.ymeth.2024.09.001","DOIUrl":"10.1016/j.ymeth.2024.09.001","url":null,"abstract":"<div><p>The process of aging is a notable risk factor for numerous age-related illnesses. Hence, a reliable technique for evaluating biological age or the pace of aging is crucial for understanding the aging process and its influence on the progression of disease. Epigenetic alterations are recognized as a prominent biomarker of aging, and epigenetic clocks formulated on this basis have been shown to provide precise estimations of chronological age. Extensive research has validated the effectiveness of epigenetic clocks in determining aging rates, identifying risk factors for aging, evaluating the impact of anti-aging interventions, and predicting the emergence of age-related diseases. This review provides a detailed overview of the theoretical principles underlying the development of epigenetic clocks and their utility in aging research. Furthermore, it explores the existing obstacles and possibilities linked to epigenetic clocks and proposes potential avenues for future studies in this field.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"231 ","pages":"Pages 37-44"},"PeriodicalIF":4.2,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196501","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":"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}