Computer methods and programs in biomedicine update最新文献

{"title":"Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks","authors":"Seyed Alireza Khanghahi ,&nbsp;Hadi Kamkar ,&nbsp;Seyedehsamaneh Shojaeilangari ,&nbsp;Abdollah Allahverdi ,&nbsp;Parviz Abdolmaleki","doi":"10.1016/j.cmpbup.2025.100208","DOIUrl":"10.1016/j.cmpbup.2025.100208","url":null,"abstract":"<div><div>Inhibiting the mammalian Target of Rapamycin (mTOR) represents a promising strategy in cancer therapy due to its crucial role in cell growth, survival, and metabolism. Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learning (DL) and classical machine learning (ML) techniques for modeling mTOR inhibitor activity. Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. This comprehensive analysis provides a robust foundation for using the best QSAR models specific to mTOR inhibition. Our findings revealed that while the Random Forest classifier achieved the highest accuracy among all models (0.9290 accuracy, 0.8940 F1-score, 0.9737 AUC), DL methods also demonstrated strong predictive capabilities, with nearly all models attaining an accuracy above 0.90. Among the DL models, CNN-QSAR using Morgan fingerprints achieved the highest accuracy (0.9271), F1-score (0.8950), and AUC (0.9696), demonstrating its effectiveness in capturing structural characteristics. The GRU-QSAR and LSTM-QSAR models, which utilized tokenized SMILES, achieved accuracies of 0.9002 and 0.9021, F1-scores of 0.8595 and 0.8603, and AUCs of 0.9270 and 0.9529, respectively, leveraging their ability to process sequential data.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling cure indicators using the curesurv R package: a tutorial using data from the French cancer registries","authors":"Judith Breaud ,&nbsp;Olayidé Boussari ,&nbsp;Valérie Jooste ,&nbsp;Laura Botta ,&nbsp;Juste Goungounga","doi":"10.1016/j.cmpbup.2025.100222","DOIUrl":"10.1016/j.cmpbup.2025.100222","url":null,"abstract":"<div><div>Cure models are essential in survival analyses because they take into account the fraction of patients who achieve long-term survival and will not experience disease-related death. In a relative survival framework, these models estimate the excess hazard (i.e. the additional death risk attributable to the disease), while assuming that some patients will not experience such excess risk. In 2002, Phillips and colleagues introduced a class of cure models where background mortality is modelled as a scaled version of the general population’s mortality. This tutorial presents the <span>curesurv</span> R package for parametric cure modelling in the relative survival setting, with and without background mortality rescaling. The package supports both the Weibull mixture cure model and the non-mixture cure model proposed by Boussari and colleagues in 2018, offering flexible tools for assessing long-term survival. The <span>curesurv</span> R package application is illustrated using testicular, breast, and prostate cancer datasets from the French FRANCIM cancer registries. This tutorial provides a practical guide to researchers in epidemiology and health economics for implementing cure fraction models and interpreting long-term survival estimates.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multipath2.0: Extending Multilayer Reproducible Pathway Models with Omics Data","authors":"Zaynab Hammoud ,&nbsp;Mohammad Al Maaz ,&nbsp;Alicia D'Angelo ,&nbsp;Frank Kramer","doi":"10.1016/j.cmpbup.2025.100189","DOIUrl":"10.1016/j.cmpbup.2025.100189","url":null,"abstract":"<div><h3>Background</h3><div>Biological systems are often perceived as independent and consequently analyzed individually. In the field of omics, multiple disciplines target the study of specific types of molecules, such as genomics. The support of more data sources in these analyses is becoming more crucial for understanding the interplay of biological systems. However, this requires integration of heterogeneous knowledge, which is considered highly challenging in bioinformatics and biomedicine. Therefore, the R package Multipath was developed to model biological pathways as multilayered graphs and integrate influencing knowledge including proteins and drugs. In its previous form, Multipath generated multilayer models of BioPAX-encoded pathways and included features to integrate drug and protein information from DrugBank and UniProtKB respectively. Although the model showed remarkable utility, including further data sources ensures enriching and expanding its capabilities.</div></div><div><h3>Results</h3><div>In this paper, a new version Multipath 2.0 is presented. The update additionally supports the two databases KEGG Genes and OMIM, which serve as the source for gene and disease entries and interactions. Information on the interactions between the previously and newly added nodes are extracted and integrated. The Multipath 2.0 offers features to update the original multilayer model and integrate the corresponding nodes and edges into two additional layers referring to KEGG Genes and OMIM. Furthermore, the embedded nodes are inter- and intra-connected using interactions from the original and newly supported data sources.</div></div><div><h3>Conclusion</h3><div>The R Package Multipath is presented with the main functions that are newly developed to support the integration of the databases KEGG Genes and OMIM. The model comprises multiple information relevant to the analysis of pathway data, and offers a reproducible and simplified view of complex, intertwined systems. Through the application of such highly integrated models the inference of new knowledge becomes easier and contributes to many fields such as drug repurposing and biomarker discovery.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of the use of Shapley value to assess node importance in the analysis of biological networks","authors":"Giang Pham,&nbsp;Paolo Milazzo","doi":"10.1016/j.cmpbup.2025.100185","DOIUrl":"10.1016/j.cmpbup.2025.100185","url":null,"abstract":"<div><h3>Background:</h3><div>In 2017, Lundberg and Lee introduced SHAP, a breakthrough in Explainable AI, creatively applying the Shapley value to estimate the importance of input features in machine learning outputs. The Shapley value, from cooperative game theory, fairly distributes system gains among participants. Inspired by SHAP’s success, this survey explores the application of Shapley value-based methods in biological network analysis.</div></div><div><h3>Method:</h3><div>We conducted a comprehensive literature search on the application of the Shapley value in biological network analysis from 2004 to 2024. From this, we focused on studies that applied the Shapley value in innovative and non-trivial ways, distinct from its typical usage.</div></div><div><h3>Result:</h3><div>The review identified six original studies that provide novel applications of the Shapley value in analyzing biological networks. These methods have also inspired further development and applications. For each, we discuss the foundational contributions, subsequent advancements, and applications.</div></div><div><h3>Discussion:</h3><div>Although the reviewed methods share the common objective of using the Shapley value to estimate an element’s contribution within a system, each one takes a distinct approach to modeling the cooperative game. Some methods employ game settings that enable more efficient Shapley value calculations, albeit with a narrower scope, as they are tailored to specific problems. Other methods offer broader applicability but encounter the usual computational challenges associated with calculating the exact Shapley value due to its time complexity. Fortunately, these challenges can be mitigated through the use of approximation techniques. Despite the computational challenges, Shapley value-based methods demonstrate to be beneficial for the interpretation of biological networks.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time series analysis and prediction of the trends of COVID-19 epidemic in Singapore based on machine learning","authors":"Wenbin Yang ,&nbsp;Xin Chang","doi":"10.1016/j.cmpbup.2025.100190","DOIUrl":"10.1016/j.cmpbup.2025.100190","url":null,"abstract":"<div><div>The COVID-19 pandemic has posed a significant threat to global health, with ongoing rises in new cases and deaths in Singapore, profoundly affecting public health, social activities, and the economy. This study compares the performance of LSTM, GRU, and a composite prediction model (LSTM-GRU) using a dataset of new and cumulative COVID-19 cases in Singapore, provided by the World Health Organization. The analysis uses weekly cumulative data from 2020 to January 21, 2024, to forecast new cases for the upcoming weeks. Model performance is evaluated using RMSE, MAE, MAPE, and R2. The results show that the LSTM model outperforms others, particularly in capturing significant data fluctuations. This research provides insights into the trends of the pandemic in Singapore and offers a basis for further epidemiological control efforts in the region.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model","authors":"Elias Mazrooei Rad ,&nbsp;Sayyed Majid Mazinani ,&nbsp;Seyyed Ali Zendehbad","doi":"10.1016/j.cmpbup.2025.100192","DOIUrl":"10.1016/j.cmpbup.2025.100192","url":null,"abstract":"<div><div>Biological signals have a dynamic and non-linear nature, and hence nonlinear analysis is important for understanding the signals. In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. The P300 component of the ERP signal, derived from acoustic stimulation, is a key indicator of AD, and its amplitude and latency are characterized. By using nonlinear features such as phase diagrams, correlation dimension, entropy, and Lyapunov exponents, the proposed model classifies AD stages. The hybrid CNN-LSTM architecture, enhanced by an attention mechanism, captures both spatial and temporal dependencies in the ERP signals, achieving high accuracy: For healthy people, 95 %, for mild AD patients, 92.5 %, and for severe AD patients, 97.5 %. The model achieves 75 % accuracy in recall mode for healthy individuals, 72.5 % for mild AD, and 87.5 % for severe AD. Results show that the proposed model outperforms traditional methods and provides a robust and accurate diagnostic framework for AD. The result of this approach is to show that the combination of non-linear EEG analysis with advanced deep learning methods could provide early and precise AD detection.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient synthesis of 3D MR images for schizophrenia diagnosis classification with generative adversarial networks","authors":"Sebastian King ,&nbsp;Yasmin Hollenbenders ,&nbsp;Alexandra Reichenbach","doi":"10.1016/j.cmpbup.2025.100197","DOIUrl":"10.1016/j.cmpbup.2025.100197","url":null,"abstract":"<div><div>Schizophrenia and other psychiatric disorders can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on neuroimaging, e.g. magnetic resonance imaging (MRI), have the potential to serve this purpose. However, the medical data sets these algorithms can be trained on are often rather small, leading to overfit, and the resulting models can therewith not be transferred into a clinical setting. The generation of synthetic images from real data is a promising approach to overcome this shortcoming. Due to the small data set size and the size and complexity of medical images, i.e. their three-dimensional nature, those algorithms are challenged on several levels. We develop four generative adversarial network (GAN) architectures that tackle these challenges and evaluate them systematically with a data set of 193 MR images of schizophrenia patients and healthy controls. The best architecture, a GAN with spectral normalization regulation and an additional encoder (α-SN-GAN), is then extended with an auxiliary classifier into an ensemble of networks capable of generating distinct image sets for the two diagnostic categories. The synthetic images increase the accuracy of a diagnostic classifier from a baseline accuracy of around 61 % to 79 %. This novel end-to-end pipeline for schizophrenia diagnosis demonstrates a data and memory efficient approach to support clinical decision-making that can also be transferred to support other psychiatric disorders.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fostering digital health literacy to enhance trust and improve health outcomes","authors":"Kristine Sørensen","doi":"10.1016/j.cmpbup.2024.100140","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100140","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"34 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139814804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delayed matrix pencil method for local shear wave viscoelastographic estimation","authors":"X. Li,&nbsp;S. Turco,&nbsp;R.M. Aarts,&nbsp;H. Wijkstra,&nbsp;M. Mischi","doi":"10.1016/j.cmpbup.2024.100156","DOIUrl":"10.1016/j.cmpbup.2024.100156","url":null,"abstract":"<div><p>Shear wave (SW) elastography is an ultrasound imaging modality that provides quantitative viscoelastic measurements of tissue. The phase difference method allows for local estimation of viscoelasticity by computing the dispersion curve using phases from two laterally-spaced pixels. However, this method is sensitive to measurement noise in the estimated SW particle velocities. Hence, we propose the delayed matrix pencil method to investigate this problem, and validated its feasibility both <em>in-silico</em> and <em>in-vitro</em>. The performance was compared with the original phase difference method and other two alternative techniques based on lowpass filtering and discrete wavelet transform denoising. The estimated viscoelastic values are summarized in box plots and followed by statistical analysis. Results from both studies show the proposed method to be more robust to noise with the smallest interquartile range in both elasticity and viscosity.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000235/pdfft?md5=407281c736aa0eadcc9ebd09eb287aa3&pid=1-s2.0-S2666990024000235-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140780086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of the commonly used portable medical sensors performance in comparison to clinical test results for telehealth systems","authors":"Rafiqul Islam Maruf ,&nbsp;Saori Tou ,&nbsp;Rieko Izukura ,&nbsp;Yoko Sato ,&nbsp;Mariko Nishikitani ,&nbsp;Kimiyo Kikuchi ,&nbsp;Fumihiko Yokota ,&nbsp;Subaru Ikeda ,&nbsp;Rakibul Islam ,&nbsp;Ashir Ahmed ,&nbsp;Masashi Miyazaki ,&nbsp;Naoki Nakashima","doi":"10.1016/j.cmpbup.2024.100147","DOIUrl":"10.1016/j.cmpbup.2024.100147","url":null,"abstract":"<div><h3>Background and Objective</h3><p>One of the primary challenges faced by telehealth systems is the accurate transmission of patient information to remote doctors. In this context, portable medical sensors deployed at the remote patients' end play a crucial role in measuring vital information. There are many sensors available in the market. However, the accuracy of the sensors has been always a concern. The objective of this study is to verify different sensors and create awareness for using accurate sensors to avoid misdiagnosis for the patients’ safety.</p></div><div><h3>Methods</h3><p>This study considered the test result of a Japanese clinical pathology laboratory as the reference gold standard. The clinical pathology laboratory uses 1) Hexokinase UV method for blood glucose, 2) Enzymatic Determination method for cholesterol, 3) Automatic Analyzer (EDTA-2 K) of Hemoglobin, and 4) Uricase POD method for uric acid. To assess the performance of a medical sensor, its test results were compared to the gold standard test results obtained from the laboratory using the same sample. A Normalized Root Mean Square Error (NRMSE) threshold of less than 0.2 was established as the criterion for determining whether the medical sensor's performance fell within an acceptable range.</p></div><div><h3>Results</h3><p>Among the eight most commonly used blood glucose devices in the Asian market, only one device was deemed acceptable with NRMSE less than 0.2. However, all four devices found in the Japanese market showed their acceptability. In the case of cholesterol, hemoglobin, and uric acid devices, only a limited number of items were available in Asian markets. Some of the hemoglobin and uric acid devices were found to be somewhat acceptable, while all the cholesterol sensors were found erroneous.</p></div><div><h3>Conclusions</h3><p>This study has clearly shown the issues with the portable medical sensors and recommends the device approval authority of each country to approve sales of the quality sensors only for patients’ safety.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000144/pdfft?md5=6cb858a0f60b29a1fc21629928f765b9&pid=1-s2.0-S2666990024000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}