Siamese neural networks: An overview

D Chicco - Artificial neural networks, 2021 - Springer
Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

MA Myszczynska, PN Ojamies, AMB Lacoste… - Nature reviews …, 2020 - nature.com
Globally, there is a huge unmet need for effective treatments for neurodegenerative
diseases. The complexity of the molecular mechanisms underlying neuronal degeneration …

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

D Chicco, G Jurman - BioData Mining, 2023 - Springer
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix …

D Chicco, N Tötsch, G Jurman - BioData mining, 2021 - Springer
Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …

The Matthews correlation coefficient (MCC) is more informative than Cohen's Kappa and Brier score in binary classification assessment

D Chicco, MJ Warrens, G Jurman - Ieee Access, 2021 - ieeexplore.ieee.org
Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

D Chicco, G Jurman - BMC genomics, 2020 - Springer
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

D Chicco, G Jurman - BMC medical informatics and decision making, 2020 - Springer
Background Cardiovascular diseases kill approximately 17 million people globally every
year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) …

[HTML][HTML] The impact of class imbalance in classification performance metrics based on the binary confusion matrix

A Luque, A Carrasco, A Martín, A de Las Heras - Pattern Recognition, 2019 - Elsevier
A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …

Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges

RI Mukhamediev, Y Popova, Y Kuchin, E Zaitseva… - Mathematics, 2022 - mdpi.com
Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of
applied issues. The core of AI is machine learning (ML)—a complex of algorithms and …

Challenges and opportunities in sharing microbiome data and analyses

C Huttenhower, RD Finn, AC McHardy - Nature Microbiology, 2023 - nature.com
Microbiome data, metadata and analytical workflows have become 'big'in terms of volume
and complexity. Although the infrastructure and technologies to share data have been …