Biodata Mining

Papers
(The TQCC of Biodata Mining is 8. 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 2020-02-01 to 2024-02-01.)
ArticleCitations
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation334
Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking48
Deep learning methods improve linear B-cell epitope prediction42
ChatGPT and large language models in academia: opportunities and challenges39
ISLAND: in-silico proteins binding affinity prediction using sequence information32
Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology29
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification28
Examining the effector mechanisms of Xuebijing injection on COVID-19 based on network pharmacology28
Deep learning-based ovarian cancer subtypes identification using multi-omics data28
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making25
Indels in SARS-CoV-2 occur at template-switching hotspots23
Network pharmacology reveals the multiple mechanisms of Xiaochaihu decoction in the treatment of non-alcoholic fatty liver disease19
A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions18
Evaluation of different approaches for missing data imputation on features associated to genomic data17
Acoustic and language analysis of speech for suicidal ideation among US veterans17
Application of network pharmacology and molecular docking to elucidate the potential mechanism of Eucommia ulmoides-Radix Achyranthis Bidentatae against osteoarthritis16
eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals14
A network pharmacology-based study on Alzheimer disease prevention and treatment of Qiong Yu Gao14
Therapeutic mechanism of Toujie Quwen granules in COVID-19 based on network pharmacology14
Machine Learning Algorithms for understanding the determinants of under-five Mortality13
Data analytics and clinical feature ranking of medical records of patients with sepsis12
Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms12
Ideas for how informaticians can get involved with COVID-19 research12
LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification12
Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis11
Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data11
Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest11
Benchmarking AutoML frameworks for disease prediction using medical claims10
Mechanistic modeling of the SARS-CoV-2 disease map10
Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods10
Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data9
Feature selection using distributions of orthogonal PLS regression vectors in spectral data9
Revisiting the use of graph centrality models in biological pathway analysis9
Merging microarray studies to identify a common gene expression signature to several structural heart diseases9
COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking8
Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms8
Estimating sequencing error rates using families8
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