Biodata Mining最新文献_第3页

Profile-guided Hybrid Approach for block-wise missing data handling in multi-omics: a breast cancer case study. 多组学中数据块丢失处理的概况引导混合方法：一个乳腺癌案例研究。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-20 DOI: 10.1186/s13040-026-00530-8

Esraa Hamdi Abdelaziz, Eman Amin, Rasha Ismail, Mai Mabrouk

引用次数: 0

TLEUDS: a cascade Dual-Transfer learning system with quality- and knowledge-enhanced for precise fetal CHD screening. TLEUDS：一个具有质量和知识增强的级联双转移学习系统，用于精确的胎儿CHD筛查。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-18 DOI: 10.1186/s13040-026-00535-3

Yuxuan Jiang, Jiajie Tang, Fanfan Zhu, Yuzhou Zeng, Junbo Wu, Wanqi Wang, Yuan Liu, Long Lu, Hongying Wang

引用次数: 0

BPX-Net: biomarker-preserved explainable networks for disease diagnosis and prognosis. BPX-Net：保留生物标志物的可解释疾病诊断和预后网络。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-12 DOI: 10.1186/s13040-026-00537-1

Jun Wang, Songchang Chen, Ru Wen, Haochao Ying, Wenqiu Xu, Lin Yin, Xiaojuan Deng, Can Han, Qun Zhu, Bin Zhang, Hongyan Tong, Chen Liu, Wei Chen, Jie Jin, Kai Jin, Chenming Xu, Hefeng Huang, Huafeng Wang, Dahong Qian

引用次数: 0

A multi-metric evaluation of readability in psychiatric discharge summaries. 精神科出院摘要可读性的多指标评价。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-10 DOI: 10.1186/s13040-026-00528-2

Fuchen Li, Casey Overby Taylor, Ayah Zirikly

引用次数: 0

Hierarchical machine learning models for predicting antenatal care utilisation among Nigerian women: Identifying actionable insights for health policy. 用于预测尼日利亚妇女产前保健利用情况的分层机器学习模型：确定卫生政策的可行见解。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-04 DOI: 10.1186/s13040-026-00538-0

Augustus Osborne, David B Olawade, Ekomobong Aniefiok Oton, Liwi Martin Odey, Kobloobase Usani

引用次数: 0

Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes. 癌症易感基因中罕见错义变异致病性预测的疾病和基因特异性深度学习。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-04 DOI: 10.1186/s13040-026-00533-5

Da-Bin Lee, Hyun-Uk Kang, Kyu-Baek Hwang

引用次数: 0

Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses. 用于检测左心室肥厚和预测心血管预后的深度学习ECG系统。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-03-04 DOI: 10.1186/s13040-026-00536-2

Zhe-Yu Yang, Shi-Chue Hsing, Dung-Jang Tsai, Chin Lin, Chin-Sheng Lin, Chih-Hung Wang, Wen-Hui Fang

{"title":"Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.","authors":"Zhe-Yu Yang, Shi-Chue Hsing, Dung-Jang Tsai, Chin Lin, Chin-Sheng Lin, Chih-Hung Wang, Wen-Hui Fang","doi":"10.1186/s13040-026-00536-2","DOIUrl":"10.1186/s13040-026-00536-2","url":null,"abstract":"Left ventricular hypertrophy (LVH) is a common condition with a prevalence of 15%-20% in general population. Prior studies have suggested that deep learning model (DLM)-enabled electrocardiogram (ECG) systems can aid LVH detection and cardiovascular risk assessment; however, conventional manual ECG criteria have limited sensitivity and their prognostic utility remains suboptimal. Therefore, this study aimed to develop a DLM-enabled ECG system to detect LVH and evaluate its prognostic associations with incident cardiovascular outcomes. A total of 40,736 patients from hospital A were used for model development (training and tuning) and internal validation (29,595/5,935/5,206 patients, respectively), and 6,271 patients from hospital B were used for external validation. LVH was defined by left ventricular mass index (LVMI) derived from echocardiography. Prognostic outcomes included new-onset acute myocardial infarction (AMI), heart failure (HF), and atrial fibrillation (AFib). In the external validation set, our AI-ECG-LVH model achieved area under the receiver operating characteristic curve (AUC) values of 0.82 in males and 0.77 in females. Furthermore, the hazard ratios for incident AMI, HF, and AFib were 2.67, 3.15, and 2.23 for AI-ECG-LVH, compared with 2.76, 3.78, and 2.25 for echocardiography-defined LVH (ECHO-LVH). Our AI-ECG-LVH model may provide a straightforward, affordable, and noninvasive approach for LVH screening and first-contact risk stratification.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13069749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357169","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}

引用次数: 0

From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening. 从视网膜到心脏：使用OCT和临床协变量进行心力衰竭筛查的可解释机器学习。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-02-28 DOI: 10.1186/s13040-026-00529-1

Sona M Al Younis, Samit Kumar Ghosh, Feryal A Alskafi, Siamak Yousefi, Namareq Widatalla, Ahsan H Khandoker

{"title":"From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening.","authors":"Sona M Al Younis, Samit Kumar Ghosh, Feryal A Alskafi, Siamak Yousefi, Namareq Widatalla, Ahsan H Khandoker","doi":"10.1186/s13040-026-00529-1","DOIUrl":"10.1186/s13040-026-00529-1","url":null,"abstract":"Heart failure affects over 64 million individuals globally, contributing to elevated mortality rates and substantial healthcare costs. This study investigates the potential of retinal optical coherence tomography features combined with routine clinical variables as biomarkers for the detection of heart failure, exploring a potential avenue for improved risk assessment and screening support using explainable machine-learning tools. A comprehensive dataset of normal and heart failure patients' demographic and medical records including retinal measurements from both eyes was used. Among the machine learning models employed, the Extreme Gradient Boosting model demonstrated the best performance, achieving an accuracy of 73.31%, a precision of 71.81%, and an area under the receiver operating characteristic curve of 0.837. Explainability analyses further revealed that macular thickness metrics, particularly in the inner temporal subfield, inner nasal subfield, and outer superior subfields of the left eye, along with key clinical indicators such as age, body mass index, and glycated hemoglobin, were the most influential predictors of heart failure status. Local explanation methods also provided patient-level reasoning consistent with overall cohort patterns. To our knowledge, this is the first study to use an integrated, explainable approach incorporating bilateral retinal optical coherence tomography measurements with routine clinical indicators for heart failure detection, providing an interpretable and accessible alternative to black-box models while helping address the cost, invasiveness, and limited accessibility of existing heart failure diagnostic tools.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":""},"PeriodicalIF":6.1,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13097962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345564","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}

引用次数: 0

KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model. 使用动态系统模型从稳态观测中识别关键微生物的稀疏字典学习。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-02-19 DOI: 10.1186/s13040-026-00527-3

Max Gordon, Turgut Yigit Akyol, B Amos, Stig U Andersen, Cranos Williams

引用次数: 0

Computational insights into the natural phage endolysin linker landscape. 对天然噬菌体内溶素连接体景观的计算见解。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-02-19 DOI: 10.1186/s13040-026-00526-4

Emma Cremelie, Alexandre Boulay, Roberto Vázquez, Yves Briers

引用次数: 0