Biodata Mining最新文献

{"title":"Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles.","authors":"Thanyawee Srithanyarat, Kittisak Taoma, Thana Sutthibutpong, Marasri Ruengjitchatchawalya, Monrudee Liangruksa, Teeraphan Laomettachit","doi":"10.1186/s13040-024-00359-z","DOIUrl":"10.1186/s13040-024-00359-z","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.</p><p><strong>Results: </strong>This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.</p><p><strong>Conclusions: </strong>The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"8"},"PeriodicalIF":4.5,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10905801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139997938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.","authors":"Sandra Batista, Vered Senderovich Madar, Philip J Freda, Priyanka Bhandary, Attri Ghosh, Nicholas Matsumoto, Apurva S Chitre, Abraham A Palmer, Jason H Moore","doi":"10.1186/s13040-024-00358-0","DOIUrl":"10.1186/s13040-024-00358-0","url":null,"abstract":"<p><strong>Purpose: </strong>Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.</p><p><strong>Methods: </strong>We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.</p><p><strong>Results: </strong>This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.</p><p><strong>Conclusion: </strong>Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"7"},"PeriodicalIF":4.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10900690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139991555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis.","authors":"Xiao-Ce Dai, Yi Yu, Si-Yu Zhou, Shuo Yu, Mei-Xiang Xiang, Hong Ma","doi":"10.1186/s13040-024-00356-2","DOIUrl":"10.1186/s13040-024-00356-2","url":null,"abstract":"<p><strong>Background: </strong>Previous studies have shown an association between gut microbiota and cardiovascular diseases (CVDs). However, the underlying causal relationship remains unclear. This study aims to elucidate the causal relationship between gut microbiota and CVDs and to explore the pathogenic role of gut microbiota in CVDs.</p><p><strong>Methods: </strong>In this two-sample Mendelian randomization study, we used genetic instruments from publicly available genome-wide association studies, including single-nucleotide polymorphisms (SNPs) associated with gut microbiota (n = 14,306) and CVDs (n = 2,207,591). We employed multiple statistical analysis methods, including inverse variance weighting, MR Egger, weighted median, MR pleiotropic residuals and outliers, and the leave-one-out method, to estimate the causal relationship between gut microbiota and CVDs. Additionally, we conducted multiple analyses to assess horizontal pleiotropy and heterogeneity.</p><p><strong>Results: </strong>GWAS summary data were available from a pooled sample of 2,221,897 adult and adolescent participants. Our findings indicated that specific gut microbiota had either protective or detrimental effects on CVDs. Notably, Howardella (OR = 0.955, 95% CI: 0.913-0.999, P = .05), Intestinibacter (OR = 0.908, 95% CI:0.831-0.993, P = .03), Lachnospiraceae (NK4A136 group) (OR = 0.904, 95% CI:0.841-0.973, P = .007), Turicibacter (OR = 0.904, 95% CI: 0.838-0.976, P = .01), Holdemania (OR, 0.898; 95% CI: 0.810-0.995, P = .04) and Odoribacter (OR, 0.835; 95% CI: 0.710-0.993, P = .04) exhibited a protective causal effect on atrial fibrillation, while other microbiota had adverse causal effects. Similar effects were observed with respect to coronary artery disease, myocardial infarction, ischemic stroke, and hypertension. Furthermore, reversed Mendelian randomization analyses revealed that atrial fibrillation and ischemic stroke had causal effects on certain gut microbiotas.</p><p><strong>Conclusion: </strong>Our study underscored the importance of gut microbiota in the context of CVDs and lent support to the hypothesis that increasing the abundance of probiotics or decreasing the abundance of harmful bacterial populations may offer protection against specific CVDs. Nevertheless, further research is essential to translate these findings into clinical practice.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"6"},"PeriodicalIF":4.5,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10898129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A network-based drug prioritization and combination analysis for the MEK5/ERK5 pathway in breast cancer.","authors":"Regan Odongo, Asuman Demiroglu-Zergeroglu, Tunahan Çakır","doi":"10.1186/s13040-024-00357-1","DOIUrl":"10.1186/s13040-024-00357-1","url":null,"abstract":"<p><strong>Background: </strong>Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes.</p><p><strong>Results: </strong>By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer.</p><p><strong>Conclusion: </strong>This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"5"},"PeriodicalIF":4.5,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139913853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.","authors":"Muhammad Taseer Suleman, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan","doi":"10.1186/s13040-023-00353-x","DOIUrl":"10.1186/s13040-023-00353-x","url":null,"abstract":"<p><strong>Background: </strong>1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites.</p><p><strong>Objective: </strong>Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated.</p><p><strong>Methodology: </strong>The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models.</p><p><strong>Results: </strong>The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics.</p><p><strong>Conclusion: </strong>For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/ .</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"4"},"PeriodicalIF":4.5,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10868122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139742372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies","authors":"Burcu Yaldız, Onur Erdoğan, Sevda Rafatov, Cem Iyigün, Yeşim Aydın Son","doi":"10.1186/s13040-024-00355-3","DOIUrl":"https://doi.org/10.1186/s13040-024-00355-3","url":null,"abstract":"Non-linear relationships at the genotype level are essential in understanding the genetic interactions of complex disease traits. Genome-wide association Studies (GWAS) have revealed statistical association of the SNPs in many complex diseases. As GWAS results could not thoroughly reveal the genetic background of these disorders, Genome-Wide Interaction Studies have started to gain importance. In recent years, various statistical approaches, such as entropy-based methods, have been suggested for revealing these non-additive interactions between variants. This study presents a novel prioritization workflow integrating two-step Random Forest (RF) modeling and entropy analysis after PLINK filtering. PLINK-RF-RF workflow is followed by an entropy-based 3-way interaction information (3WII) method to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer Disease to discover early and differential diagnosis markers. Three models from different datasets are developed by integrating PLINK-RF-RF analysis and entropy-based three-way interaction information (3WII) calculation method, which enables the detection of the third-order interactions, which are not primarily considered in epistatic interaction studies. A reduced SNP set is selected for all three datasets by 3WII analysis by PLINK filtering and prioritization of SNP with RF-RF modeling, promising as a model minimization approach. Among SNPs revealed by 3WII, 4 SNPs out of 19 from GenADA, 1 SNP out of 27 from ADNI, and 4 SNPs out of 106 from NCRAD are mapped to genes directly associated with Alzheimer Disease. Additionally, several SNPs are associated with other neurological disorders. Also, the genes the variants mapped to in all datasets are significantly enriched in calcium ion binding, extracellular matrix, external encapsulating structure, and RUNX1 regulates estrogen receptor-mediated transcription pathways. Therefore, these functional pathways are proposed for further examination for a possible LOAD association. Besides, all 3WII variants are proposed as candidate biomarkers for the genotyping-based LOAD diagnosis. The entropy approach performed in this study reveals the complex genetic interactions that significantly contribute to LOAD risk. We benefited from the entropy-based 3WII as a model minimization step and determined the significant 3-way interactions between the prioritized SNPs by PLINK-RF-RF. This framework is a promising approach for disease association studies, which can also be modified by integrating other machine learning and entropy-based interaction methods.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"217 1","pages":""},"PeriodicalIF":4.5,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139581109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data.","authors":"André Fonseca, Mikolaj Spytek, Przemysław Biecek, Clara Cordeiro, Nuno Sepúlveda","doi":"10.1186/s13040-024-00354-4","DOIUrl":"10.1186/s13040-024-00354-4","url":null,"abstract":"<p><strong>Background: </strong>Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies  should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized).</p><p><strong>Methods: </strong>To answer this question, we developed three approaches for antibody selection in the context of predicting clinical malaria: (i) a basic and simple approach based on selecting antibodies via the nonparametric Mann-Whitney-Wilcoxon test; (ii) an optimal dychotomizationdichotomization approach where each antibody was selected according to the optimal cut-off via maximization of the chi-squared (χ²) statistic for two-way tables; (iii) a hybrid parametric/non-parametric approach that integrates Box-Cox transformation followed by a t-test, together with the use of finite mixture models and the Mann-Whitney-Wilcoxon test as a last resort. We illustrated the application of these three approaches with published serological data of 36 Plasmodium falciparum antigens for predicting clinical malaria in 121 Kenyan children. The predictive analysis was based on a Super Learner where predictions from multiple classifiers including the Random Forest were pooled together.</p><p><strong>Results: </strong>Our results led to almost similar areas under the Receiver Operating Characteristic curves of 0.72 (95% CI = [0.62, 0.82]), 0.80 (95% CI = [0.71, 0.89]), 0.79 (95% CI = [0.7, 0.88]) for the simple, dichotomization and hybrid approaches, respectively. These approaches were based on 6, 20, and 16 antibodies, respectively.</p><p><strong>Conclusions: </strong>The three feature selection strategies provided a better predictive performance of the outcome when compared to the previous results relying on Random Forest including all the 36 antibodies (AUC = 0.68, 95% CI = [0.57;0.79]). Given the similar predictive performance, we recommended that the three strategies should be used in conjunction in the same data set and selected according to their complexity.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"2"},"PeriodicalIF":4.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10811867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139564720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records.","authors":"Chih-Wei Chung, Seng-Cho Chou, Tzu-Hung Hsiao, Grace Joyce Zhang, Yu-Fang Chung, Yi-Ming Chen","doi":"10.1186/s13040-023-00352-y","DOIUrl":"10.1186/s13040-023-00352-y","url":null,"abstract":"<p><strong>Background: </strong>Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records.</p><p><strong>Methods: </strong>Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640.</p><p><strong>Results: </strong>A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered.</p><p><strong>Conclusions: </strong>ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"17 1","pages":"1"},"PeriodicalIF":4.5,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10770905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139106801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S","authors":"Tzong-Hann Yang, Yu-Fu Chen, Yen-Fu Cheng, Jue-Ni Huang, Chuan-Song Wu, Yuan-Chia Chu","doi":"10.1186/s13040-023-00351-z","DOIUrl":"https://doi.org/10.1186/s13040-023-00351-z","url":null,"abstract":"The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"33 4 1","pages":""},"PeriodicalIF":4.5,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138691791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.","authors":"Guohua Huang, Xiaohong Huang, Wei Luo","doi":"10.1186/s13040-023-00348-8","DOIUrl":"10.1186/s13040-023-00348-8","url":null,"abstract":"<p><p>DNA N6-adenine methylation (N6-methyladenine, 6mA) plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the past decades, there is a large root left to improve. We presented a cross validation-based stacking ensemble model for 6mA site prediction, called 6mA-StackingCV. The 6mA-StackingCV is a type of meta-learning algorithm, which uses output of cross validation as input to the final classifier. The 6mA-StackingCV reached the state of the art performances in the Rosaceae independent test. Extensive tests demonstrated the stability and the flexibility of the 6mA-StackingCV. We implemented the 6mA-StackingCV as a user-friendly web application, which allows one to restrictively choose representations or learning algorithms. This application is freely available at http://www.biolscience.cn/6mA-stackingCV/ . The source code and experimental data is available at https://github.com/Xiaohong-source/6mA-stackingCV .</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"16 1","pages":"34"},"PeriodicalIF":4.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138446729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}