Pub Date : 2023-12-04DOI: 10.2174/0115748936256869231019113616
Muhammad Shujaat, Hilal Tayara, Sunggoo Yoo, Kil To Chong
Background and Objective:: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods:: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results:: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion:: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.
{"title":"iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking","authors":"Muhammad Shujaat, Hilal Tayara, Sunggoo Yoo, Kil To Chong","doi":"10.2174/0115748936256869231019113616","DOIUrl":"https://doi.org/10.2174/0115748936256869231019113616","url":null,"abstract":"Background and Objective:: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods:: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results:: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion:: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"2 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515003","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}
Pub Date : 2023-12-01DOI: 10.2174/0115748936268302231110111456
Han Cheng, Shumei Ding, Cangzhi Jia
Background:: Super-enhancers are clusters of enhancers defined based on the binding occupancy of master transcription factors, chromatin regulators, or chromatin marks. It has been reported that super-enhancers are transcriptionally more active and cell-type-specific than regular enhancers. Therefore, it is necessary to identify super-enhancers from regular enhancers. A variety of computational methods have been proposed to identify super-enhancers as auxiliary tools. However, most methods use ChIP-seq data, and the lack of this part of the data will make the predictor unable to execute or fail to achieve satisfactory performance. Objective:: The aim of this study is to propose a stacking computational model based on the fusion of multiple features to identify super-enhancers in both human and mouse species. Methods:: This work adopted mean-shift to cluster majority class samples and selected five sets of balanced datasets for mouse and three sets of balanced datasets for humans to train the stacking model. Five types of sequence information are used as input to the XGBoost classifier, and the average value of the probability outputs from each classifier is designed as the final classification result. Results:: The results of 10-fold cross-validation and cross-cell-line validation prove that our method has superior performance compared to other existing methods. The source code and datasets are available at https://github.com/Cheng-Han-max/SE_voting. Conclusion:: The analysis of feature importance indicates that Mismatch accounts for the highest proportion among the top 20 important features.
{"title":"Prediction of Super-enhancers Based on Mean-shift Undersampling","authors":"Han Cheng, Shumei Ding, Cangzhi Jia","doi":"10.2174/0115748936268302231110111456","DOIUrl":"https://doi.org/10.2174/0115748936268302231110111456","url":null,"abstract":"Background:: Super-enhancers are clusters of enhancers defined based on the binding occupancy of master transcription factors, chromatin regulators, or chromatin marks. It has been reported that super-enhancers are transcriptionally more active and cell-type-specific than regular enhancers. Therefore, it is necessary to identify super-enhancers from regular enhancers. A variety of computational methods have been proposed to identify super-enhancers as auxiliary tools. However, most methods use ChIP-seq data, and the lack of this part of the data will make the predictor unable to execute or fail to achieve satisfactory performance. Objective:: The aim of this study is to propose a stacking computational model based on the fusion of multiple features to identify super-enhancers in both human and mouse species. Methods:: This work adopted mean-shift to cluster majority class samples and selected five sets of balanced datasets for mouse and three sets of balanced datasets for humans to train the stacking model. Five types of sequence information are used as input to the XGBoost classifier, and the average value of the probability outputs from each classifier is designed as the final classification result. Results:: The results of 10-fold cross-validation and cross-cell-line validation prove that our method has superior performance compared to other existing methods. The source code and datasets are available at https://github.com/Cheng-Han-max/SE_voting. Conclusion:: The analysis of feature importance indicates that Mismatch accounts for the highest proportion among the top 20 important features.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"25 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138515002","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}
Pub Date : 2023-11-22DOI: 10.2174/1574893618666230809121509
Han Wang, Jingyang Gao
Background: The high mutability of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) makes it easy for mutations to occur during transmission. As the epidemic continues to develop, several mutated strains have been produced. Researchers worldwide are working on the effective identification of SARS-CoV-2. Objective: In this paper, we propose a new deep learning method that can effectively identify SARSCoV- 2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components. Methods: Deep learning is effective in extracting rich features from sequence data, which has significant implications for the study of Coronavirus Disease 2019 (COVID-19), which has become prevalent in recent years. In this paper, we propose a new deep learning method that can effectively identify SARS-CoV-2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components. Results: The accuracy of the SCVfilter is 93.833% on Dataset-I consisting of different variant strains; 90.367% on Dataset-II consisting of data collected from China, Taiwan, and Hong Kong; and 79.701% on Dataset-III consisting of data collected from six continents (Africa, Asia, Europe, North America, Oceania, and South America). Conclusion: When using the SCV filter to process lengthy and high-homology SARS-CoV-2 data, it can automatically select features and accurately detect different variant strains of SARS-CoV-2. In addition, the SCV filter is sufficiently robust to handle the problems caused by sample imbalance and sequence incompleteness. Other: The SCVfilter is an open-source method available at https://github.com/deconvolutionw/ SCVfilter.
{"title":"SCV Filter: A Hybrid Deep Learning Model for SARS-CoV-2 Variants Classification","authors":"Han Wang, Jingyang Gao","doi":"10.2174/1574893618666230809121509","DOIUrl":"https://doi.org/10.2174/1574893618666230809121509","url":null,"abstract":"Background: The high mutability of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) makes it easy for mutations to occur during transmission. As the epidemic continues to develop, several mutated strains have been produced. Researchers worldwide are working on the effective identification of SARS-CoV-2. Objective: In this paper, we propose a new deep learning method that can effectively identify SARSCoV- 2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components. Methods: Deep learning is effective in extracting rich features from sequence data, which has significant implications for the study of Coronavirus Disease 2019 (COVID-19), which has become prevalent in recent years. In this paper, we propose a new deep learning method that can effectively identify SARS-CoV-2 Variant sequences, called SCVfilter, which is a deep hybrid model with embedding, attention residual network, and long short-term memory as components. Results: The accuracy of the SCVfilter is 93.833% on Dataset-I consisting of different variant strains; 90.367% on Dataset-II consisting of data collected from China, Taiwan, and Hong Kong; and 79.701% on Dataset-III consisting of data collected from six continents (Africa, Asia, Europe, North America, Oceania, and South America). Conclusion: When using the SCV filter to process lengthy and high-homology SARS-CoV-2 data, it can automatically select features and accurately detect different variant strains of SARS-CoV-2. In addition, the SCV filter is sufficiently robust to handle the problems caused by sample imbalance and sequence incompleteness. Other: The SCVfilter is an open-source method available at https://github.com/deconvolutionw/ SCVfilter.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"2 1","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514985","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}
Pub Date : 2023-10-27DOI: 10.2174/0115748936264122231016094702
Mauricio Arenas-Salinas, Jessica Lara Muñoz, José Antonio Reyes, Felipe Besoain
Introduction: Transcription factors are of great interest in biotechnology due to their key role in the regulation of gene expression. One of the most important transcription factors in gramnegative bacteria is Fur, a global regulator studied as a therapeutic target for the design of antibacterial agents. Its DNA-binding domain, which contains a helix-turn-helix motif, is one of its most relevant features. Methods: In this study, we evaluated several machine learning algorithms for the prediction of DNA-binding sites based on proteins from the Fur superfamily and other helix-turn-helix transcription factors, including Support-Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Naive Bayes (NB). We also tested the efficacy of using several molecular descriptors derived from the amino acid sequence and the structure of the protein fragments that bind the DNA. A feature selection procedure was employed to select fewer descriptors in each case by maintaining a good classification performance. Results: The best results were obtained with the SVM model using twelve sequence-derived attributes and the DT model using nine structure-derived features, achieving 82% and 76% accuracy, respectively. Conclusion: The performance obtained indicates that the descriptors we used are relevant for predicting DNA-binding sites since they can discriminate between binding and non-binding regions of a protein.
{"title":"Prediction of DNA-binding Sites in Transcriptions Factor in Fur-like Proteins Using Machine Learning and Molecular Descriptors","authors":"Mauricio Arenas-Salinas, Jessica Lara Muñoz, José Antonio Reyes, Felipe Besoain","doi":"10.2174/0115748936264122231016094702","DOIUrl":"https://doi.org/10.2174/0115748936264122231016094702","url":null,"abstract":"Introduction: Transcription factors are of great interest in biotechnology due to their key role in the regulation of gene expression. One of the most important transcription factors in gramnegative bacteria is Fur, a global regulator studied as a therapeutic target for the design of antibacterial agents. Its DNA-binding domain, which contains a helix-turn-helix motif, is one of its most relevant features. Methods: In this study, we evaluated several machine learning algorithms for the prediction of DNA-binding sites based on proteins from the Fur superfamily and other helix-turn-helix transcription factors, including Support-Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Naive Bayes (NB). We also tested the efficacy of using several molecular descriptors derived from the amino acid sequence and the structure of the protein fragments that bind the DNA. A feature selection procedure was employed to select fewer descriptors in each case by maintaining a good classification performance. Results: The best results were obtained with the SVM model using twelve sequence-derived attributes and the DT model using nine structure-derived features, achieving 82% and 76% accuracy, respectively. Conclusion: The performance obtained indicates that the descriptors we used are relevant for predicting DNA-binding sites since they can discriminate between binding and non-binding regions of a protein.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"25 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318847","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}
abstract: Interactions of RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. This study proposes an online network provider tool (NaProGraph) that offers an intuitive and user-friendly interface for studying interactions between nucleic acids (NA) and proteins. NaProGraph utilizes a comprehensive and curated dataset encompassing nearly all interacting macromolecules in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Researchers can employ this online tool to focus on a specific portion of the PDB, investigate its associated relationships, and visualize and extract pertinent information. This tool provides insights into the frequency of atoms and residues between proteins and nucleic acids (NAs) and the similarity of the macromolecules' primary structures. Furthermore, the functional similarity of proteins can be inferred using protein families and clans from Pfam. The tool we have developed is publicly available at https://naprolink.com/NaProGraph/ background: Interactions between RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. method: This study proposes an online network provider tool (NaProGraph) that offers an intuitive and user-friendly interface for studying interactions between nucleic acids (NA) and proteins. NaProGraph utilizes a comprehensive and curated dataset encompassing nearly all interacting macromolecules in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Researchers can employ this online tool to focus on a specific portion of the PDB, investigate its associated relationships, and visualize and extract pertinent information. This tool provides insights into the frequency of atoms and residues between proteins and NAs and the similarity of the macromolecules' primary structures. Furthermore, the functional similarity of proteins can be inferred using protein families and clans from Pfam. conclusion: The NaProGraph tool serves as an effective online resource for researchers interested in studying interactions between nucleic acids and proteins. By leveraging a comprehensive dataset and providing various visualization and extraction capabilities, NaProGraph facilitates the exploration of macromolecular relationships and aids in understanding intracellular processes in living organisms. other: -
{"title":"NaProGraph: Network Analyzer for Interactions between Nucleic Acids and Proteins","authors":"Sajjad nematzadeh, Nizamettin Aydin, Zeyneb Kurt, Mahsa Torkamanian-Afshar","doi":"10.2174/0115748936266189231004110412","DOIUrl":"https://doi.org/10.2174/0115748936266189231004110412","url":null,"abstract":"abstract: Interactions of RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. This study proposes an online network provider tool (NaProGraph) that offers an intuitive and user-friendly interface for studying interactions between nucleic acids (NA) and proteins. NaProGraph utilizes a comprehensive and curated dataset encompassing nearly all interacting macromolecules in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Researchers can employ this online tool to focus on a specific portion of the PDB, investigate its associated relationships, and visualize and extract pertinent information. This tool provides insights into the frequency of atoms and residues between proteins and nucleic acids (NAs) and the similarity of the macromolecules' primary structures. Furthermore, the functional similarity of proteins can be inferred using protein families and clans from Pfam. The tool we have developed is publicly available at https://naprolink.com/NaProGraph/ background: Interactions between RNA and DNA with proteins are crucial for elucidating intracellular processes in living organisms, diagnosing disorders, designing aptamer drugs, and other applications. Therefore, investigating the relationships between these macromolecules is essential to life science research. method: This study proposes an online network provider tool (NaProGraph) that offers an intuitive and user-friendly interface for studying interactions between nucleic acids (NA) and proteins. NaProGraph utilizes a comprehensive and curated dataset encompassing nearly all interacting macromolecules in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Researchers can employ this online tool to focus on a specific portion of the PDB, investigate its associated relationships, and visualize and extract pertinent information. This tool provides insights into the frequency of atoms and residues between proteins and NAs and the similarity of the macromolecules' primary structures. Furthermore, the functional similarity of proteins can be inferred using protein families and clans from Pfam. conclusion: The NaProGraph tool serves as an effective online resource for researchers interested in studying interactions between nucleic acids and proteins. By leveraging a comprehensive dataset and providing various visualization and extraction capabilities, NaProGraph facilitates the exploration of macromolecular relationships and aids in understanding intracellular processes in living organisms. other: -","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135666197","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}
Pub Date : 2023-10-18DOI: 10.2174/0115748936269606231001140647
Osphanie Mentari, Muhammad Shujaat, Hilal Tayara, Kil To Chong
Background: One of the problems in drug discovery that can be solved by artificial intelligence is toxicity prediction. In drug-induced immune thrombocytopenia, toxicity can arise in patients after five to ten days by significant bleeding caused by drugdependent antibodies. In clinical trials, when this condition occurs, all the drugs consumed by patients should be stopped, although sometimes this is not possible, especially for older patients who are dependent on their medication. Therefore, being able to predict toxicity in drug-induced immune thrombocytopenia is very important. Computational technologies, such as machine learning, can help predict toxicity better than empirical techniques owing to the lower cost and faster processing. Objective: Previous studies used the KNN method. However, the performance of these approaches needs to be enhanced. This study proposes a Logistic Regression to improve accuracy scores. Methods: In this study, we present a new model for drug-induced immune thrombocytopenia using a machine learning method. Our model extracts several features from the Simplified Molecular Input Line Entry System (SMILES). These features were fused and cleaned, and the important features were selected using the SelectKBest method. The model uses a Logistic Regression that is optimized and tuned by the Grid Search Cross Validation. Results: The highest accuracy occurred when using features from PADEL, CDK, RDKIT, MORDRED, BLUEDESC combinations, resulting in an accuracy of 80%. Conclusion: Our proposed model outperforms previous studies in accuracy categories. The information and source code is accessible online at Github: https://github.com/Osphanie/Thrombocytopenia.
{"title":"Toxicity Prediction for Immune Thrombocytopenia Caused by Drugs Based on Logistic Regression with Feature Importance","authors":"Osphanie Mentari, Muhammad Shujaat, Hilal Tayara, Kil To Chong","doi":"10.2174/0115748936269606231001140647","DOIUrl":"https://doi.org/10.2174/0115748936269606231001140647","url":null,"abstract":"Background: One of the problems in drug discovery that can be solved by artificial intelligence is toxicity prediction. In drug-induced immune thrombocytopenia, toxicity can arise in patients after five to ten days by significant bleeding caused by drugdependent antibodies. In clinical trials, when this condition occurs, all the drugs consumed by patients should be stopped, although sometimes this is not possible, especially for older patients who are dependent on their medication. Therefore, being able to predict toxicity in drug-induced immune thrombocytopenia is very important. Computational technologies, such as machine learning, can help predict toxicity better than empirical techniques owing to the lower cost and faster processing. Objective: Previous studies used the KNN method. However, the performance of these approaches needs to be enhanced. This study proposes a Logistic Regression to improve accuracy scores. Methods: In this study, we present a new model for drug-induced immune thrombocytopenia using a machine learning method. Our model extracts several features from the Simplified Molecular Input Line Entry System (SMILES). These features were fused and cleaned, and the important features were selected using the SelectKBest method. The model uses a Logistic Regression that is optimized and tuned by the Grid Search Cross Validation. Results: The highest accuracy occurred when using features from PADEL, CDK, RDKIT, MORDRED, BLUEDESC combinations, resulting in an accuracy of 80%. Conclusion: Our proposed model outperforms previous studies in accuracy categories. The information and source code is accessible online at Github: https://github.com/Osphanie/Thrombocytopenia.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889328","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}
Background: The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial. Objective: We aim to identify potential biomarkers of colorectal cancer patients with true oligometastatic states, who will benefit most from local therapy. Methods: This study retrospectively analyzed the transcriptome profiles and clinical parameters of 307 metastatic colorectal cancer patients. A novel network propagation method and network-based strategy were combined to identify oligometastatic biomarkers to predict the prognoses of metastatic colorectal cancer patients. Results: We defined two metastatic risk groups according to twelve oligometastatic biomarkers, which exhibit distinct prognoses, clinicopathological features, immunological characteristics, and biological mechanisms. The metastatic risk assessment model exhibited a more powerful capacity for survival prediction compared to traditional clinicopathological features. The low-MRS group was most consistent with an oligometastatic state, while the high-MRS might be a potential polymetastatic state, which leads to the divergence of their prognostic outcomes and response to treatments. We also identified 22 significant immune check genes between the high-MRS and low- MRS groups. The difference in molecular mechanism between the two metastatic risk groups was associated with focal adhesion, nucleocytoplasmic transport, Hippo, PI3K-Akt, TGF-β, and EMCreceptor interaction signaling pathways. Conclusion: Our study provided a molecular definition of the oligometastatic state in colorectal cancer, which contributes to precise treatment decision-making for advanced patients.
{"title":"Network propagation-based identification of oligometastatic biomarkers in metastatic colorectal cancer","authors":"Qing Jin, Kexin Yu, Xianze Zhang, Diwei Huo, Denan Zhang, Lei Liu, Hongbo Xie, Binhua Liang, Xiujie Chen","doi":"10.2174/1574893618666230913110025","DOIUrl":"https://doi.org/10.2174/1574893618666230913110025","url":null,"abstract":"Background: The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial. Objective: We aim to identify potential biomarkers of colorectal cancer patients with true oligometastatic states, who will benefit most from local therapy. Methods: This study retrospectively analyzed the transcriptome profiles and clinical parameters of 307 metastatic colorectal cancer patients. A novel network propagation method and network-based strategy were combined to identify oligometastatic biomarkers to predict the prognoses of metastatic colorectal cancer patients. Results: We defined two metastatic risk groups according to twelve oligometastatic biomarkers, which exhibit distinct prognoses, clinicopathological features, immunological characteristics, and biological mechanisms. The metastatic risk assessment model exhibited a more powerful capacity for survival prediction compared to traditional clinicopathological features. The low-MRS group was most consistent with an oligometastatic state, while the high-MRS might be a potential polymetastatic state, which leads to the divergence of their prognostic outcomes and response to treatments. We also identified 22 significant immune check genes between the high-MRS and low- MRS groups. The difference in molecular mechanism between the two metastatic risk groups was associated with focal adhesion, nucleocytoplasmic transport, Hippo, PI3K-Akt, TGF-β, and EMCreceptor interaction signaling pathways. Conclusion: Our study provided a molecular definition of the oligometastatic state in colorectal cancer, which contributes to precise treatment decision-making for advanced patients.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078556","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}
Pub Date : 2023-10-16DOI: 10.2174/0115748936264731230928112936
Jie Gao, Qiming Fu, Jiacheng Sun, Yunzhe Wang, Youbing Xia, You Lu, Hongjie Wu, Jianping Chen
Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single information source and a single model, or by combining some models, but the prediction results are still not accurate enough. Objective: The study aimed to utilize existing data and machine learning models to integrate heterogeneous data sources and different models, further improving the accuracy of DTI prediction. Methods: This paper has proposed a novel prediction method based on reinforcement learning, called QLDTI (predicting drug-target interaction based on Q-learning), which can be mainly divided into two parts: data fusion and model fusion. Firstly, it fuses the drug and target similarity matrices calculated by different calculation methods through Q-learning. Secondly, the new similarity matrix is inputted into five models, NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, for further training. Then, all sub-model weights are continuously optimized again by Q-learning, which can be used to linearly weight all sub-model prediction results to output the final prediction result. Results: QLDTI achieved AUC accuracy of 99.04%, 99.12%, 98.28%, and 98.35% on E, NR, IC, and GPCR datasets, respectively. Compared to the existing five models NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, the QLDTI method has achieved better results on four benchmark datasets of E, NR, IC, and GPCR. Conclusion: Data fusion and model fusion have been proven effective for DTI prediction, further improving the prediction accuracy of DTI.
{"title":"QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction","authors":"Jie Gao, Qiming Fu, Jiacheng Sun, Yunzhe Wang, Youbing Xia, You Lu, Hongjie Wu, Jianping Chen","doi":"10.2174/0115748936264731230928112936","DOIUrl":"https://doi.org/10.2174/0115748936264731230928112936","url":null,"abstract":"Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single information source and a single model, or by combining some models, but the prediction results are still not accurate enough. Objective: The study aimed to utilize existing data and machine learning models to integrate heterogeneous data sources and different models, further improving the accuracy of DTI prediction. Methods: This paper has proposed a novel prediction method based on reinforcement learning, called QLDTI (predicting drug-target interaction based on Q-learning), which can be mainly divided into two parts: data fusion and model fusion. Firstly, it fuses the drug and target similarity matrices calculated by different calculation methods through Q-learning. Secondly, the new similarity matrix is inputted into five models, NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, for further training. Then, all sub-model weights are continuously optimized again by Q-learning, which can be used to linearly weight all sub-model prediction results to output the final prediction result. Results: QLDTI achieved AUC accuracy of 99.04%, 99.12%, 98.28%, and 98.35% on E, NR, IC, and GPCR datasets, respectively. Compared to the existing five models NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, the QLDTI method has achieved better results on four benchmark datasets of E, NR, IC, and GPCR. Conclusion: Data fusion and model fusion have been proven effective for DTI prediction, further improving the prediction accuracy of DTI.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136182037","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}
Abstract: Different diseases can be treated with various therapeutic agents. Drug discovery aims to find potential molecules for existing and emerging diseases. However, factors, such as increasing development cost, generic competition due to the patent expiry of several drugs, increase in conservative regulatory policies, and insufficient breakthrough innovations impairs the development of new drugs and the learning productivity of pharmaceutical industries. Drug repurposing is the process of finding new therapeutic applications for already approved, withdrawn from use, abandoned, and experimental drugs. Drug repurposing is another method that may partially overcome the hurdles related to drug discovery and hence appears to be a wise attempt. However, drug repurposing being not a standard regulatory process, leads to administrative concerns and problems. The drug repurposing also requires expensive, high-risk clinical trials to establish the safety and efficacy of the repurposed drug. Recent innovations in the field of bioinformatics can accelerate the new drug repurposing studies by identifying new targets of the existing drugs along with drug candidate screening and refinement. Recent advancements in the field of comprehensive high throughput data in genomics, epigenetics, chromosome architecture, transcriptomic, proteomics, and metabolomics may also contribute to the understanding of molecular mechanisms involved in drug-target interaction. The present review describes the current scenario in the field of drug repurposing along with the application of various bioinformatic tools for the identification of new targets for the existing drug.
{"title":"Bioinformatics Perspective of Drug Repurposing","authors":"Binita Patel, Brijesh Gelat, Mehul Soni, Pooja Rathaur, Kaid Johar SR","doi":"10.2174/0115748936264692230921071504","DOIUrl":"https://doi.org/10.2174/0115748936264692230921071504","url":null,"abstract":"Abstract: Different diseases can be treated with various therapeutic agents. Drug discovery aims to find potential molecules for existing and emerging diseases. However, factors, such as increasing development cost, generic competition due to the patent expiry of several drugs, increase in conservative regulatory policies, and insufficient breakthrough innovations impairs the development of new drugs and the learning productivity of pharmaceutical industries. Drug repurposing is the process of finding new therapeutic applications for already approved, withdrawn from use, abandoned, and experimental drugs. Drug repurposing is another method that may partially overcome the hurdles related to drug discovery and hence appears to be a wise attempt. However, drug repurposing being not a standard regulatory process, leads to administrative concerns and problems. The drug repurposing also requires expensive, high-risk clinical trials to establish the safety and efficacy of the repurposed drug. Recent innovations in the field of bioinformatics can accelerate the new drug repurposing studies by identifying new targets of the existing drugs along with drug candidate screening and refinement. Recent advancements in the field of comprehensive high throughput data in genomics, epigenetics, chromosome architecture, transcriptomic, proteomics, and metabolomics may also contribute to the understanding of molecular mechanisms involved in drug-target interaction. The present review describes the current scenario in the field of drug repurposing along with the application of various bioinformatic tools for the identification of new targets for the existing drug.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358655","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}
Pub Date : 2023-10-10DOI: 10.2174/0115748936251658231002043812
Ishleen Kaur, Tanvir Ahmad, M.N. Doja
Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.
{"title":"A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction","authors":"Ishleen Kaur, Tanvir Ahmad, M.N. Doja","doi":"10.2174/0115748936251658231002043812","DOIUrl":"https://doi.org/10.2174/0115748936251658231002043812","url":null,"abstract":"Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.","PeriodicalId":10801,"journal":{"name":"Current Bioinformatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358104","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：