Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …
significantly reduces the time and cost required for developing novel drugs. With the …
A survey of predictive modeling on imbalanced domains
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix …
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 …
it can influence decisions in multiple areas, including for example prognosis or therapies of …
[HTML][HTML] The impact of class imbalance in classification performance metrics based on the binary confusion matrix
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 …
the most suitable performance metrics to be used. In previous work using several examples …
[HTML][HTML] A comparison among interpretative proposals for Random Forests
The growing success of Machine Learning (ML) is making significant improvements to
predictive models, facilitating their integration in various application fields. Despite its …
predictive models, facilitating their integration in various application fields. Despite its …
Online labour index: Measuring the online gig economy for policy and research
O Kässi, V Lehdonvirta - Technological forecasting and social change, 2018 - Elsevier
Labour markets are thought to be in the midst of a dramatic transformation, where standard
employment is increasingly supplemented or substituted by temporary work mediated by …
employment is increasingly supplemented or substituted by temporary work mediated by …
Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation
Optimal performance is desired for decision-making in any field with binary classifiers and
diagnostic tests, however common performance measures lack depth in information. The …
diagnostic tests, however common performance measures lack depth in information. The …
Facing imbalanced data--recommendations for the use of performance metrics
Recognizing facial action units (AUs) is important for situation analysis and automated video
annotation. Previous work has emphasized face tracking and registration and the choice of …
annotation. Previous work has emphasized face tracking and registration and the choice of …
Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures
In this paper, a new Computer-Aided Detection (CAD) system for the detection and
classification of dangerous skin lesions (melanoma type) is presented, through a fusion of …
classification of dangerous skin lesions (melanoma type) is presented, through a fusion of …
LightGBM: An effective and scalable algorithm for prediction of chemical toxicity–application to the Tox21 and mutagenicity data sets
J Zhang, D Mucs, U Norinder… - Journal of chemical …, 2019 - ACS Publications
Machine learning algorithms have attained widespread use in assessing the potential
toxicities of pharmaceuticals and industrial chemicals because of their faster speed and …
toxicities of pharmaceuticals and industrial chemicals because of their faster speed and …