BIODATA MINING 是一本开放获取、开放的同行评审期刊，涵盖应用于高维生物和生物医学数据的数据挖掘各个方面的研究，重点关注从大规模遗传、转录组、基因组、蛋白质组和代谢组学数据。主题领域包括但不限于：-新型数据挖掘和机器学习算法的开发、评估和应用。-传统数据挖掘和机器学习算法的适应、评估和应用。-开源软件用于数据挖掘和机器学习算法的应用。-数据库、软件和网络服务的设计、开发和集成，用于大规模研究数据的存储、管理、检索和分析。-预处理、后处理、数据挖掘和机器学习结果的建模和解释，用于生物解释和知识发现。

BioData Mining is an open-access, peer-reviewed journal that focuses on all aspects of data mining applied to high-dimensional biological and biomedical data. The journal covers topics including but not limited to: the development, evaluation, and application of novel data mining and machine learning algorithms; the adaptation, evaluation, and application of traditional data mining and machine learning algorithms; the application of open-source software for data mining and machine learning; the design, development, and integration of databases, software, and web services for the storage, management, retrieval, and analysis of large-scale research data; and the modeling and interpretation of preprocessing, postprocessing, data mining, and machine learning results for biological interpretation and knowledge discovery.

《BioData Mining》是一本开放获取的同行评审期刊，专注于高维生物和生物医学数据的数据挖掘研究。期刊涵盖的主题包括：新型数据挖掘和机器学习算法的开发、评估与应用；传统数据挖掘和机器学习算法的适应、评估与应用；开源软件在数据挖掘和机器学习中的应用；数据库、软件和网络服务的设计、开发与集成，以支持大规模研究数据的存储、管理、检索与分析；以及数据的预处理、后处理、挖掘和机器学习结果的建模与解释，以促进生物学解释和知识发现。

BioData Mining

Abstract Background The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. Method In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. Results Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. Conclusion We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.

Jenna L Ballard

Zexuan Wang

Wenrui Li

Li Shen

Qi Long

Deep learning-based approaches for multi-omics data integration and analysis

基于深度学习的多组学数据整合与分析方法

The genetic architecture underlying central cornea thickness (CCT) is far from understood. Most of the CCT-associated variants are located in the non-coding regions, raising the difficulty of following functional characterizations. Thus, integrative functional analyses on CCT-associated loci might benefit in overcoming these issues by prioritizing the hub genes that are located in the center of CCT genetic network.Integrative analyses including functional annotations, enrichment analysis, and protein-protein interaction analyses were performed on all reported CCT GWAS lead SNPs, together with their proxy variants. Functional annotations were conducted by CADD, GWAVA, and Eigen. Enrichment analyses for CCT-associated genes were performed using ToppGene suite. Protein-protein interaction network and gene co-expression analyses were performed by GeneMANIA.Functional annotations prioritized eight genes (ADAMSTS6, ARID5B, FOXO1, AKAP13, COL4A3, COL8A2, TBL1XR1, and KCMB2) harboring SNPs with strong evidence of regulatory potential. It was also shown that CCT-associated genes were significantly enriched in collagen-related pathways and the phenotype of keratoconus, and some of them were found to be involved in one interaction network.This study revealed the hub genes that were located in the center of CCT genetic network and provided a new insight into the genetic regulation underlying CCT GWAS findings.

Jing Zhang

Dan Wu

Yiqin Dai

Jianjiang Xu

Functional relevance for central cornea thickness-associated genetic variants by using integrative analyses

整合分析揭示中央角膜厚度相关遗传变异的功能相关性

The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.

Maryam Ramezani

Amirhossein Takian

Ahad Bakhtiari

Hamid R. Rabiee

Sadegh Ghazanfari

Saharnaz Sazgarnejad

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

健康治理中应用人工智能的研究议程：解释性范围综述与框架

This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies.

Richa Gupta

Mansi Bhandari

Anhad Grover

Taher Al-shehari

Mohammed Kadrie

Taha Alfakih

Hussain Alsalman

Predictive modeling of ALS progression: an XGBoost approach using clinical features

ALS进展的预测建模：使用临床特征的XGBoost方法

Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets—HIV, Bipolar Disorder (BP), and Parkinson's Disease (PPMI), Alzheimer's Disease (ADNI)—demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at https://github.com/rongzhou7/TGNet .

Zhaoming Kong

Rong Zhou

Xinwei Luo

Songlin Zhao

Ann B. Ragin

Alex D. Leow

Lifang He

TGNet: tensor-based graph convolutional networks for multimodal brain network analysis

TGNet：基于张量的图卷积网络用于多模态脑网络分析

Abstract Background Timely identification of deteriorating patients is crucial to prevent the progression to cardiac arrest. However, current methods predicting emergency department cardiac arrest are primarily static, rule-based with limited precision and cannot accommodate time-series data. Deep learning has the potential to continuously update data and provide more precise predictions throughout the emergency department stay. Methods We developed and internally validated a deep learning-based scoring system, the Deep EDICAS for early prediction of cardiac arrest and a subset of arrest, cardiopulmonary resuscitation (CPR), in the emergency department. Our proposed model effectively integrates tabular and time series data to enhance predictive accuracy. To address data imbalance and bolster early prediction capabilities, we implemented data augmentation techniques. Results Our system achieved an AUPRC of 0.5178 and an AUROC of 0.9388 on on data from the National Taiwan University Hospital. For early prediction, our system achieved an AUPRC of 0.2798 and an AUROC of 0.9046, demonstrating superiority over other early warning scores. Moerover, Deep EDICAS offers interpretability through feature importance analysis. Conclusion Our study demonstrates the effectiveness of deep learning in predicting cardiac arrest in emergency department. Despite the higher clinical value associated with detecting patients requiring CPR, there is a scarcity of literature utilizing deep learning in CPR detection tasks. Therefore, this study embarks on an initial exploration into the task of CPR detection.

Yuan-Xiang Deng

Jyun-Yi Wang

Chia-Hsin Ko

Chien-Hua Huang

Chu-Lin Tsai

Li-Chen Fu

Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department

基于深度学习的急诊科院内心脏骤停评分（Deep EDICAS）用于早期预测急诊科心脏骤停和心肺复苏

Abstract Background Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining. Results We provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches. Conclusion Multiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.

Mitja Briscik

Gabriele Tazza

László Vidács

Marie-Agnès Dillies

Sébastien Déjean

Supervised multiple kernel learning approaches for multi-omics data integration

多组学数据整合的监督多核学习方法

Regulatory T cells (Tregs) play a critical role in shaping the immunosuppressive microenvironment within tumors. Investigating the role of Tregs in Clear cell renal cell carcinoma (ccRCC) is crucial for identifying prognostic markers and therapeutic targets for ccRCC.

Yang Qixin

Huang Jing

He Jiang

Liu Xueyang

Yu Lu

Li Yuehua

Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma

基于转录组网络分析与调节性T细胞浸润相关的研究揭示RCN1为透明细胞肾细胞癌预后的潜在生物标志物

Abbreviation	BioData Min.
Journal Impact	4.20
Quartiles(Global)	MATHEMATICAL & COMPUTATIONAL BIOLOGY(Q1)

ISSN	1756-0381
h-index	38

Publication Information	Publisher: BioMed Central Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2009，Proportion of original research papers: 96.55%，Self Citation Rate:0.00%， Gold OA Rate: 100.00%
Journal Fees	$2590Details
Average review cycle	网友分享经验：23 Weeks

BioData Mining

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Deep learning-based approaches for multi-omics data integration and analysis

Functional relevance for central cornea thickness-associated genetic variants by using integrative analyses

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

Predictive modeling of ALS progression: an XGBoost approach using clinical features

TGNet: tensor-based graph convolutional networks for multimodal brain network analysis

TGNet: tensor-based graph convolutional networks for multimodal brain network analysis

Predictive modeling of ALS progression: an XGBoost approach using clinical features

Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department

Supervised multiple kernel learning approaches for multi-omics data integration

Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma