Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100190
Wenbin Yang , Xin Chang
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.
{"title":"Time series analysis and prediction of the trends of COVID-19 epidemic in Singapore based on machine learning","authors":"Wenbin Yang , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100189
Zaynab Hammoud , Mohammad Al Maaz , Alicia D'Angelo , Frank Kramer
Background
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.
Results
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.
Conclusion
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.
{"title":"Multipath2.0: Extending Multilayer Reproducible Pathway Models with Omics Data","authors":"Zaynab Hammoud , Mohammad Al Maaz , Alicia D'Angelo , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100185
Giang Pham, Paolo Milazzo
Background:
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.
Method:
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.
Result:
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.
Discussion:
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.
{"title":"A comprehensive review of the use of Shapley value to assess node importance in the analysis of biological networks","authors":"Giang Pham, 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}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100197
Sebastian King , Yasmin Hollenbenders , Alexandra Reichenbach
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.
{"title":"Efficient synthesis of 3D MR images for schizophrenia diagnosis classification with generative adversarial networks","authors":"Sebastian King , Yasmin Hollenbenders , 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}
Pub Date : 2025-01-01DOI: 10.1016/j.cmpbup.2025.100192
Elias Mazrooei Rad , Sayyed Majid Mazinani , Seyyed Ali Zendehbad
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.
{"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 , Sayyed Majid Mazinani , 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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146308327","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146308329","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146308330","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146308335","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146308338","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}
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