Biodata Mining

Papers
(The H4-Index of Biodata Mining is 17. 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 2020-11-01 to 2024-11-01.)
ArticleCitations
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation457
ChatGPT and large language models in academia: opportunities and challenges171
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification130
Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking63
ISLAND: in-silico proteins binding affinity prediction using sequence information45
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making33
Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology32
Acoustic and language analysis of speech for suicidal ideation among US veterans27
Indels in SARS-CoV-2 occur at template-switching hotspots27
Evaluation of different approaches for missing data imputation on features associated to genomic data26
Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms25
A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions22
Machine Learning Algorithms for understanding the determinants of under-five Mortality21
Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis19
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare18
Benchmarking AutoML frameworks for disease prediction using medical claims18
LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification18
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