Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification

A Rácz, D Bajusz, K Héberger - Molecules, 2021 - mdpi.com
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative
structure-activity/property (QSAR/QSPR) relationships and classification. However, the size …

TERL: classification of transposable elements by convolutional neural networks

MHP da Cruz, DS Domingues, PTM Saito… - Briefings in …, 2021 - academic.oup.com
Transposable elements (TEs) are the most represented sequences occurring in eukaryotic
genomes. Few methods provide the classification of these sequences into deeper levels …

A review on thermal imaging‐based breast cancer detection using deep learning

D Tsietso, A Yahya… - Mobile Information Systems, 2022 - Wiley Online Library
Breast cancer is the most common form of cancer in women. Its aggressive nature has made
it one of the chief factors of high female mortality. Therefore, this has motivated research to …

Machine learning and molecular docking prediction of potential inhibitors against dengue virus

G Hanson, J Adams, DIB Kepgang, LS Zondagh… - Frontiers in …, 2024 - frontiersin.org
Introduction Dengue Fever continues to pose a global threat due to the widespread
distribution of its vector mosquitoes, Aedes aegypti and Aedes albopictus. While the WHO …

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes

S Orozco-Arias… - Briefings in …, 2023 - academic.oup.com
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play
an important role in evolution and biodiversity. Their characterization is of great importance …

[HTML][HTML] Advanced bayesian network for task effort estimation in Agile software development

M Turic, S Celar, S Dragicevic, L Vickovic - Applied Sciences, 2023 - mdpi.com
Effort estimation is always quite a challenge, especially for agile software development
projects. This paper describes the process of building a Bayesian network model for effort …

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

S Orozco-Arias, MS Candamil-Cortés, PA Jaimes… - PeerJ, 2021 - peerj.com
Every day more plant genomes are available in public databases and additional massive
sequencing projects (ie, that aim to sequence thousands of individuals) are formulated and …

Machine learning in onco-pharmacogenomics: A path to precision medicine with many challenges

A Mondello, M Dal Bo, G Toffoli… - Frontiers in Pharmacology, 2024 - frontiersin.org
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the
approach to cancer research. Applications of NGS include the identification of tumor specific …

Wind limitations at madeira international airport: a deep learning prediction approach

D Alves, D Freitas, F Mendonça, S Mostafa… - IEEE …, 2024 - ieeexplore.ieee.org
The unique geographical and topographical features of Madeira International Airport in
Portugal significantly influence flight safety, primarily due to variable wind patterns. In this …

[HTML][HTML] Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review

W Kanyongo, AE Ezugwu - Informatics in Medicine Unlocked, 2023 - Elsevier
Non-adherence to prescribed medication is a major public health concern that escalates the
risk of morbidity and death as well as incurring extra expenses associated with …