Biodata Mining最新文献_第5页

Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrients. 基于45种膳食营养素预测妊娠糖尿病的可解释机器学习模型的构建。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-01-24 DOI: 10.1186/s13040-025-00515-z

Zhihui Xiong, Yi Yuan, Zhouhui Yun, Lijie Li, Yunmeng Chen

引用次数: 0

Deep learning based prediction of RNA 5hmC modifications using composite feature representations and comparative benchmarking with transformer models. 基于深度学习的RNA 5hmC修饰预测，使用复合特征表示和变压器模型的比较基准测试。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-01-19 DOI: 10.1186/s13040-025-00517-x

Muhammad Attique, Yaser Daanial Khan, Fahad Alturise, Tamim Alkhalifah

引用次数: 0

Genotype subtyping approach to identify unnoticed variants in diseases from GWAS data. 基因型分型方法从GWAS数据中识别疾病中未被注意的变异。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-01-10 DOI: 10.1186/s13040-025-00512-2

Debora Garza-Hernandez, Emmanuel Martinez-Ledesma, Victor Trevino

引用次数: 0

Generalization or mirage? Data leakage and reported performance in neonatal EEG seizure detection models: a systematic review. 概括还是海市蜃楼？新生儿脑电图发作检测模型的数据泄漏和报告性能：系统回顾。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2026-01-08 DOI: 10.1186/s13040-025-00516-y

Geletaw Sahle Tegenaw, Hailin Song, Tomas Ward

引用次数: 0

Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models. 阿尔茨海默病的跨队列遗传风险预测：使用GWAS和深度学习模型的迁移学习方法。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-22 DOI: 10.1186/s13040-025-00506-0

Isibor Kennedy Ihianle, Wathsala Samarasekara, Keeley Brookes, Pedro Machado

引用次数: 0

SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition. SBT-Net：一个用于抑郁症识别的三线索引导多模态融合框架。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-22 DOI: 10.1186/s13040-025-00498-x

Yujie Huo, Weng Howe Chan, Ahmad Najmi Bin Amerhaider Nuar, Hongyu Gao

{"title":"SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition.","authors":"Yujie Huo, Weng Howe Chan, Ahmad Najmi Bin Amerhaider Nuar, Hongyu Gao","doi":"10.1186/s13040-025-00498-x","DOIUrl":"10.1186/s13040-025-00498-x","url":null,"abstract":"Early detection of depression is vital for public health, yet current multimodal methods often struggle with challenges such as modality incompleteness, semantic inconsistency, and emotional temporal fluctuation. To address these issues, this paper proposes SBT-Net, a novel Semantic-Bias-Trend guided framework for robust depression detection from audio and text data. The model integrates three innovative modules: a semantically guided cross-modal gating (SGCMG) mechanism that dynamically filters effective modality features based on global semantic cues, a bias-guided tensor product attention (BG-TPA) mechanism that enhances fine-grained fusion and alignment between modalities, and an emotion trend modeling (ETM) module that captures the temporal evolution of depressive emotional states.We evaluate SBT-Net using two widely adopted benchmark datasets: DAIC-WOZ, which contains 189 interview sessions, and EATD-Corpus, comprising 162 conversational samples. Experimental results show that SBT-Net achieves excellent performance in multiple indicators, including 93.0% accuracy, 0.93 F1 score, and 0.92 recall, all of which surpass the competitive baselines.Ablation studies further validate the individual and synergistic contributions of each proposed module.These findings highlight the potential of integrating semantic guidance, bias-aware fusion, and emotional trend modeling to advance multimodal depression detection solutions. The source code can be found at https://github.com/ghy-yhg/SBT-Net .","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"86"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12723886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145811969","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

An unsupervised tool for biomarker discovery and cancer subtyping applied to glioblastoma. 用于胶质母细胞瘤的生物标志物发现和癌症亚型分型的无监督工具。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-22 DOI: 10.1186/s13040-025-00500-6

Roberta Coletti, J Orestes Cerdeira, Marcos Raydan, Marta B Lopes

{"title":"An unsupervised tool for biomarker discovery and cancer subtyping applied to glioblastoma.","authors":"Roberta Coletti, J Orestes Cerdeira, Marcos Raydan, Marta B Lopes","doi":"10.1186/s13040-025-00500-6","DOIUrl":"10.1186/s13040-025-00500-6","url":null,"abstract":"Background: High-dimensional omics data often contain more variables than observations, which can lead to overfitting and negatively impact the results of classical data analysis methods. To address the issue, supervised variable selection methods are often used, incorporating penalty terms into the model. While effective for selecting task-specific variables, this approach may not preserve the overall dataset structure for multiple downstream analyses. This study aims to evaluate unsupervised variable selection approaches and introduce a novel tool that improves data interpretability while maintaining biological information.Results: We assessed multiple unsupervised variable selection techniques to identify a representative subset of the original dataset. Based on this evaluation, we developed TRIM-IT, a computational tool that integrates unsupervised variable selection, clustering, survival analysis, and differential gene expression analysis. TRIM-IT was applied to glioblastoma transcriptomics data, uncovering three distinct patient clusters. These clusters correlated with tumor histology, exhibited significantly different survival outcomes, and revealed molecular profiles that suggest potential biomarker candidates.Conclusion: TRIM-IT provides a novel approach for analyzing high-dimensional omics data while preserving key biological insights. Its ability to identify meaningful patient subgroups and molecular signatures highlights its applicability across various biomedical research contexts. The tool is implemented in R and the code is publicly available for reproduction and adaptation to other studies.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"18 1","pages":"85"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12720448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145811993","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

Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study. 使用静态和动态特征的深度学习预测急诊科重访（深度重访）：开发和验证研究。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-20 DOI: 10.1186/s13040-025-00509-x

Su-Yin Hsu, Jhe-Yi Jhu, Jun-Wan Gao, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu

{"title":"Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.","authors":"Su-Yin Hsu, Jhe-Yi Jhu, Jun-Wan Gao, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu","doi":"10.1186/s13040-025-00509-x","DOIUrl":"10.1186/s13040-025-00509-x","url":null,"abstract":"Background: Emergency Department (ED) revisits represent a critical issue in emergency medicine. Identifying high-risk revisit cases (revisits with intensive care unit admissions, cardiac arrest, or requiring emergency surgery) is particularly important. While prior studies have explored machine learning models for ED revisit prediction, few deep learning approaches exist, and dynamic features remain underutilized.Methods: We used data from National Taiwan University Hospital (NTUH), incorporating both static (e.g., age, sex, triage) and dynamic (vital signs) features. A preprocessing strategy was developed to handle temporal irregularities. We proposed a hybrid deep learning model combining Temporal Convolutional Network (TCN) and feature tokenizer (FT)-Transformer to integrate static and short-term dynamic information.Results: We evaluated our model on NTUH 2016-2019 data, achieving the area under the receiver operating characteristic curve (AUROC) of 0.8453 and the area under precision recall curve (AUPRC) of 0.0935 for high-risk revisits (base rate = 0.01), and AUROC of 0.7250 and AUPRC of 0.2005 for general revisits (base rate = 0.042). The model maintained robust performance when validated on 2020-2022 data. Compared to the static-only logistic regression baseline, our model improved AUPRC from 0.0288 to 0.0935 and precision from 0.0281 to 0.0428.Conclusion: Our model significantly outperformed the static-only baseline. It demonstrates the effectiveness of multimodal clinical data fusion in improving ED revisit prediction and supporting clinical decision-making.","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":" ","pages":"88"},"PeriodicalIF":6.1,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12750547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800645","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

AI-Driven SaO₂ prediction from pulse oximetry and electronic health records. 基于脉搏血氧仪和电子健康记录的人工智能驱动的SaO2预测。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-20 DOI: 10.1186/s13040-025-00511-3

JiWon Woo, Orian Stapleton, Jay Luo, Chao Cheng Chuang, Yolanda Su, Sreenidhi Sankararaman, Esanika Mukherjee, Nikita Sivakumar, Katherine Calligy, Summer Duffy, Rebecca Mosier, Joseph Greenstein, Casey Overby Taylor, Danielle Gottlieb Sen

引用次数: 0

Subphenotype heterogeneity to guide predictive enrichment in acute kidney injury: insights from machine learning and target trial emulation. 亚表型异质性指导急性肾损伤的预测富集：来自机器学习和目标试验模拟的见解。

IF 6.1 3区生物学

Biodata Mining Pub Date : 2025-12-20 DOI: 10.1186/s13040-025-00514-0

Jiayang Li, Mingyi Zhao, Qingnan He

引用次数: 0