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

Alzheimer's disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called "magnetic resonance imaging (MRI)-p value" was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects' brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases.

Jingru Wang

Shipeng Wen

Wenjie Liu

Xianglian Meng

Zhuqing Jiao

Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion

基于多模态特征融合的阿尔茨海默病深度联合学习诊断

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

Bladder cancer (BLCA) is a tumor that affects men more than women. The biological function and prognostic value of androgen-responsive genes (ARGs) in BLCA are currently unknown. To address this, we established an androgen signature to determine the prognosis of BLCA. Sequencing data for BLCA from the TCGA and GEO datasets were used for research. The tumor microenvironment (TME) was measured using Cibersort and ssGSEA. Prognosis-related genes were identified and a risk score model was constructed using univariate Cox regression, LASSO regression, and multivariate Cox regression. Drug sensitivity analysis was performed using Genomics of drug sensitivity in cancer (GDSC). Real-time quantitative PCR was performed to assess the expression of representative genes in clinical samples. ARGs (especially the CDK6, FADS1, PGM3, SCD, PTK2B, and TPD52) might regulate the progression of BLCA. The different expression patterns of ARGs may lead to different immune cell infiltration. The risk model indicates that patients with higher risk scores have a poorer prognosis, more stromal infiltration, and an enrichment of biological functions. Single-cell RNA analysis, bulk RNA data, and PCR analysis support the reliability of this risk model, and a nomogram was also established for clinical use. Drug prediction analysis showed that high-risk patients had a better response to fludarabine, AZD8186, and carmustine. ARGs played an important role in the progression, immune infiltration, and prognosis of BLCA. The ARGs model has high accuracy in predicting the prognosis of BLCA patients and provides more effective medication guidelines.

Jiang Zhao

Qian Zhang

Cunle Zhu

Wu Yuqi

Guohui Zhang

Qianliang Wang

Xingyou Dong

Benyi Li

Xiangwei Wang

Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

基于雄激素应答基因的膀胱癌预后特征及潜在靶向药物筛选

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

Nina Kastendiek

Roberta Coletti

Thilo Gross

Marta B. Lopes

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

探索胶质瘤异质性通过组学网络：从基因网络发现到因果见解及患者分层

The transformative feature of Artificial Intelligence (AI) is the massive capacity for interpreting and transforming unstructured data into a coherent and meaningful context. In general, the potential that AI will alter traditional approaches to student research and its evaluation appears to be significant. With regard to research in global health, it is important for students and research experts to assess strengths and limitations of GenAI within this space. Thus, the goal of our research was to evaluate the information literacy of GenAI compared to expectations that graduate students meet in writing research papers. After completing the course, Fundamentals of Global Health (INTH 401) at Case Western Reserve University (CWRU), Graduate students who successfully completed their required research paper were recruited to compare their original papers with a paper they generated by ChatGPT-4o using the original assignment prompt. Students also completed a Google Forms survey to evaluate different sections of the AI-generated paper (e.g., Adherence to Introduction guidelines, Presentation of three perspectives, Conclusion) and their original papers and their overall satisfaction with the AI work. The original student to ChatGPT-4o comparison also enabled evaluation of narrative elements and references. Of the 54 students who completed the required research paper, 28 (51.8%) agreed to collaborate in the comparison project. A summary of the survey responses suggested that students evaluated the AI-generated paper as inferior or similar to their own paper (overall satisfaction average = 2.39 (1.61–3.17); Likert scale: 1 to 5 with lower scores indicating inferiority). Evaluating the average individual student responses for 5 Likert item queries showed that 17 scores were < 2.9; 7 scores were between 3.0 to 3.9; 4 scores were ≥ 4.0, consistent with inferiority of the AI-generated paper. Evaluation of reference selection by ChatGPT-4o (n = 729 total references) showed that 54% (n = 396) were authentic, 46% (n = 333) did not exist. Of the authentic references, 26.5% (105/396) were relevant to the paper narrative; 14.4% of the 729 total references. Our findings reveal strengths and limitations on the potential of AI tools to assist in understanding the complexities of global health topics. Strengths mentioned by students included the ability of ChatGPT-4o to produce content very quickly and to suggest topics that they had not considered in the 3-perspective sections of their papers. Consistently presenting up-to-date facts and references, as well as further examining or summarizing the complexities of global health topics, appears to be a current limitation of ChatGPT-4o. Because ChatGPT-4o generated references from highly credible biomedical research journals that did not exist, our findings conclude that ChatGPT-4o failed an important component in using information effectively. Moreover, misrepresenting trusted sources of public health information is highly concerning, particularly given recent experiences from the COVID-19 pandemic and more recently in reporting on the impact of, and response to natural disasters. This is a significant limitation of GenAI's ability to meet information literacy standards expected of graduate students.

Shilpa R. Thandla

Grace Q. Armstrong

Adil Menon

Aashna Shah

David L. Gueye

Clara Harb

Estefania Hernandez

Yasaswini Iyer

Abigail R. Hotchner

Riddhi Modi

Anusha Mudigonda

Maria A. Prokos

Tharun M. Rao

Olivia R. Thomas

Camilo A. Beltran

Taylor Guerrieri

Sydney LeBlanc

Skanda Moorthy

Sara G. Yacoub

Jacob E. Gardner

Benjamin M. Greenberg

Alyssa Hubal

Yuliana P. Lapina

Jacqueline Moran

Joseph P. O’Brien

Anna C. Winnicki

Christina Yoka

Junwei Zhang

Peter A. Zimmerman

Comparing new tools of artificial intelligence to the authentic intelligence of our global health students

比较人工智能新工具与全球健康学生真实智能的差异

Abstract Background Long terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. Results We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges. Conclusions Our results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

Jakub Horvath

Pavel Jedlicka

Marie Kratka

Zdenek Kubat

Eduard Kejnovsky

Matej Lexa

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning

植物LTR逆转录转座子长末端重复序列的检测与分类及其可解释机器学习分析

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

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 joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion

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

Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

Prognostic feature based on androgen-responsive genes in bladder cancer and screening for potential targeted drugs

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Comparing new tools of artificial intelligence to the authentic intelligence of our global health students

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning

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