Biodata Mining

Papers
(The TQCC of Biodata Mining is 5. 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 2021-06-01 to 2025-06-01.)
ArticleCitations
MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder305
Transcriptome-based network analysis related to regulatory T cells infiltration identified RCN1 as a potential biomarker for prognosis in clear cell renal cell carcinoma230
A new pipeline for structural characterization and classification of RNA-Seq microbiome data52
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 analys42
Correction: Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning40
Processing imbalanced medical data at the data level with assisted-reproduction data as an example39
Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion30
Comparing new tools of artificial intelligence to the authentic intelligence of our global health students25
Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient22
Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records22
Unsupervised clustering based coronary artery segmentation22
Polygenic risk modeling of tumor stage and survival in bladder cancer21
Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data20
Ten simple rules for providing bioinformatics support within a hospital19
Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning18
Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes17
The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data14
Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning14
Genetics and precision health: the ecological fallacy and artificial intelligence solutions13
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making12
Comparison of 16S and whole genome dog microbiomes using machine learning9
Advancing preeclampsia prediction: a tailored machine learning pipeline integrating resampling and ensemble models for handling imbalanced medical data9
New neural network classification method for individuals ancestry prediction from SNPs data9
Supervised multiple kernel learning approaches for multi-omics data integration9
Using GPT-4 to write a scientific review article: a pilot evaluation study9
m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models8
Correction: Motif clustering and digital biomarker extraction for free-living physical activity analysis8
GenoVault: a cloud based genomics repository8
Understanding predictions of drug profiles using explainable machine learning models7
Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery7
From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases7
Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles7
Effective hybrid feature selection using different bootstrap enhances cancers classification performance7
Motif clustering and digital biomarker extraction for free-living physical activity analysis7
Assessment of the causal relationship between gut microbiota and cardiovascular diseases: a bidirectional Mendelian randomization analysis7
Predictive modeling of ALS progression: an XGBoost approach using clinical features7
Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms6
Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability6
Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis6
An unsupervised image segmentation algorithm for coronary angiography6
Reference-free phylogeny from sequencing data6
Open challenges and opportunities in federated foundation models towards biomedical healthcare6
Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods6
A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records5
Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S5
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification5
Changing word meanings in biomedical literature reveal pandemics and new technologies5
6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site5
Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation5
Correction: A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma5
Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department5
Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis5
ChatGPT and large language models in academia: opportunities and challenges5
Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis5
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