OOIR: Observatory of International Research

Papers

(The H4-Index of Biodata Mining is 16. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-09-01 to 2025-09-01.)

Article	Citations
Investigating potential drug targets for IgA nephropathy and membranous nephropathy through multi-queue plasma protein analysis: a Mendelian randomization study based on SMR and co-localization analys	359
Correction: Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning	296
Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion	60
Processing imbalanced medical data at the data level with assisted-reproduction data as an example	51
MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder	49
Exploring the common genetic basis of metabolic syndrome-related diseases and chronic kidney disease: insights from extensive genome-wide cross-trait analyses	47
A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics	41
Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma	33
Ten simple rules for providing bioinformatics support within a hospital	27
Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data	26
Polygenic risk modeling of tumor stage and survival in bladder cancer	23
Comparing new tools of artificial intelligence to the authentic intelligence of our global health students	23
circGPAcorr: an integrative tool for functional annotation of circular RNAs using expression data	20
Unsupervised clustering based coronary artery segmentation	19
Skin in the game: a review of computational models of the skin	17
Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records	17