Methods最新文献

{"title":"HCI-modelling for improving the clinical usability of digital health technologies","authors":"Chris Paton ,&nbsp;Elizabeth M Borycki ,&nbsp;Jim Warren ,&nbsp;Andre W Kushniruk ,&nbsp;Mike English","doi":"10.1016/j.ymeth.2024.04.019","DOIUrl":"10.1016/j.ymeth.2024.04.019","url":null,"abstract":"<div><h3>Introduction</h3><p>Digital Health Technologies (DHTs) have been shown to have variable usability as measured by efficiency, effectiveness and user satisfaction despite large-scale government projects to regulate and standardise user interface (UI) design. We hypothesised that Human-Computer Interaction (HCI) modelling could improve the methodology for DHT design and regulation, and support the creation of future evidence-based UI standards and guidelines for DHTs.</p></div><div><h3>Methodology</h3><p>Using a Design Science Research (DSR) framework, we developed novel UI components that adhered to existing standards and guidelines (combining the NHS Common User Interface (CUI) standard and the NHS Design System). We firstly evaluated the Patient Banner UI component for compliance with the two guidelines and then used HCI-modelling to evaluate the “Add New Patient” workflow to measure time to task completion and cognitive load.</p></div><div><h3>Results</h3><p>Combining the two guidelines to produce new UI elements is technically feasible for the Patient Banner and the Patient Name Input components. There are some inconsistencies between the NHS Design System and the NHS CUI when implementing the Patient Banner. HCI-modelling successfully quantified challenges adhering to the NHS CUI and the NHS Design system for the “Add New Patient” workflow.</p></div><div><h3>Discussion</h3><p>We successfully developed new design artefacts combing two major design guidelines for DHTs. By quantifying usability issues using HCI-modelling, we have demonstrated the feasibility of a methodology that combines HCI-modelling into a human-centred design (HCD) process could enable the development of standardised UI elements for DHTs that is more scientifically robust than HCD alone.</p></div><div><h3>Conclusion</h3><p>Combining HCI-modelling and Human-Centred Design could improve scientific progress towards developing safer and more user-friendly DHTs.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 60-77"},"PeriodicalIF":4.8,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001099/pdfft?md5=a2155e9404355a4307314524d17a6e4d&pid=1-s2.0-S1046202324001099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903708","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":"Computational intelligence in bioinformatics and biomedicine","authors":"Huiru Zheng (Guest Editor) ,&nbsp;Xiaohua Hu (Guest Editor)","doi":"10.1016/j.ymeth.2024.05.006","DOIUrl":"10.1016/j.ymeth.2024.05.006","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 58-59"},"PeriodicalIF":4.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903700","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":"MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models","authors":"Hiroyuki Kurata ,&nbsp;Md Harun-Or-Roshid ,&nbsp;Md Mehedi Hasan ,&nbsp;Sho Tsukiyama ,&nbsp;Kazuhiro Maeda ,&nbsp;Balachandran Manavalan","doi":"10.1016/j.ymeth.2024.05.004","DOIUrl":"10.1016/j.ymeth.2024.05.004","url":null,"abstract":"<div><p>RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer’s disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 37-47"},"PeriodicalIF":4.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903713","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":"Methods special issue: Recent advancement on fluorescent chemosensing and bioimaging","authors":"Suban K. Sahoo,&nbsp;S.K. Ashok Kumar","doi":"10.1016/j.ymeth.2024.05.003","DOIUrl":"10.1016/j.ymeth.2024.05.003","url":null,"abstract":"","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 35-36"},"PeriodicalIF":4.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140896277","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":"mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization","authors":"Yifan Chen ,&nbsp;Zhenya Du ,&nbsp;Xuanbai Ren ,&nbsp;Chu Pan ,&nbsp;Yangbin Zhu ,&nbsp;Zhen Li ,&nbsp;Tao Meng ,&nbsp;Xiaojun Yao","doi":"10.1016/j.ymeth.2024.04.018","DOIUrl":"10.1016/j.ymeth.2024.04.018","url":null,"abstract":"<div><p>Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 17-26"},"PeriodicalIF":4.8,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140847884","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":"Discovery and functional characterization of LncRNAs associated with inflammation and macrophage activation","authors":"Avisankar Chini ,&nbsp;Prarthana Guha ,&nbsp;Ashcharya Rishi ,&nbsp;Monira Obaid ,&nbsp;SM Nashir Udden ,&nbsp;Subhrangsu S. Mandal","doi":"10.1016/j.ymeth.2024.05.001","DOIUrl":"10.1016/j.ymeth.2024.05.001","url":null,"abstract":"<div><p>Long noncoding RNAs (lncRNA) are emerging players in regulation of gene expression and cell signaling and their dysregulation has been implicated in a multitude of human diseases. Recent studies from our laboratory revealed that lncRNAs play critical roles in cytokine regulation, inflammation, and metabolism. We demonstrated that lncRNA HOTAIR, which is a well-known regulator of gene silencing, plays critical roles in modulation of cytokines and proinflammatory genes, and glucose metabolism in macrophages during inflammation. In addition, we recently discovered a series of novel lncRNAs that are closely associated with inflammation and macrophage activation. We termed these as long-noncoding inflammation associated RNAs (LinfRNAs). We are currently engaged in the functional characterization of these hLinfRNAs (human LinfRNAs) with a focus on their roles in inflammation, and we are investigating their potential implications in chronic inflammatory human diseases. Here, we have summarized experimental methods that have been utilized for the discovery and functional characterization of lncRNAs in inflammation and macrophage activation.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 1-16"},"PeriodicalIF":4.8,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140851709","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 deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity","authors":"Konda Mani Saravanan ,&nbsp;Jiang-Fan Wan ,&nbsp;Liujiang Dai ,&nbsp;Jiajun Zhang ,&nbsp;John Z.H. Zhang ,&nbsp;Haiping Zhang","doi":"10.1016/j.ymeth.2024.04.020","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.04.020","url":null,"abstract":"<div><p>Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"226 ","pages":"Pages 164-175"},"PeriodicalIF":4.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140824849","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":"KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine β-Hydroxybutyrylation (Kbhb) modification sites","authors":"Leqi Chen ,&nbsp;Liwen Liu ,&nbsp;Haiyan Su ,&nbsp;Yan Xu","doi":"10.1016/j.ymeth.2024.04.016","DOIUrl":"10.1016/j.ymeth.2024.04.016","url":null,"abstract":"<div><p>Lysine β-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify β-hydroxybutyrylation modification sites based on protein sequence information. The traditional experimental methods employed for the identification of β-hydroxybutyrylated sites using proteomic techniques are both costly and time-consuming. Thus, the development of computational methods and predictors can play a crucial role in facilitating the rapid identification of β-hydroxybutyrylation sites. Our proposed KbhbXG model first utilizes machine learning algorithm XGBoost to predict β-hydroxybutyrylation modification sites. On the independent test set, KbhbXG achieves an accuracy of 0.7457, specificity of 0.7771, and an impressive area under the curve (AUC) score of 0.8172. The high AUC score achieved by our method demonstrates its potential for effectively identifying novel β-hydroxybutyrylation sites, thereby facilitating further research and exploration of the β-hydroxybutyrylation process. Also, functional analyses have revealed that different organisms preferentially engage in distinct biological processes and pathways, which can provide valuable insights for understanding the mechanism of β-hydroxybutyrylation and guide experimental verification. To promote transparency and reproducibility, we have made both the codes and dataset of KbhbXG publicly available. Researchers interested in utilizing our proposed model can access these resources at <span>https://github.com/Lab-Xu/KbhbXG</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"227 ","pages":"Pages 27-34"},"PeriodicalIF":4.8,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849889","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":"Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network","authors":"Sabrin Afroz ,&nbsp;Nadira Islam ,&nbsp;Md Ahsan Habib ,&nbsp;Md Selim Reza ,&nbsp;Md Ashad Alam","doi":"10.1016/j.ymeth.2024.04.017","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.04.017","url":null,"abstract":"<div><p>In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; <span>https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I</span><svg><path></path></svg></p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"226 ","pages":"Pages 138-150"},"PeriodicalIF":4.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649754","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":"Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state","authors":"Li Liu ,&nbsp;Ranran Jia ,&nbsp;Rui Hou ,&nbsp;Chengbing Huang","doi":"10.1016/j.ymeth.2024.04.014","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.04.014","url":null,"abstract":"<div><p>Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"226 ","pages":"Pages 151-160"},"PeriodicalIF":4.8,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649753","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}