Positive-unlabeled learning in bioinformatics and computational biology: a brief review
Conventional supervised binary classification algorithms have been widely applied to
address significant research questions using biological and biomedical data. This …
address significant research questions using biological and biomedical data. This …
Recent advances in network-based methods for disease gene prediction
Disease–gene association through genome-wide association study (GWAS) is an arduous
task for researchers. Investigating single nucleotide polymorphisms that correlate with …
task for researchers. Investigating single nucleotide polymorphisms that correlate with …
Learning from positive and unlabeled data: A survey
Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …
has access to positive examples and unlabeled data. The assumption is that the unlabeled …
GCN-MF: disease-gene association identification by graph convolutional networks and matrix factorization
Discovering disease-gene association is a fundamental and critical biomedical task, which
assists biologists and physicians to discover pathogenic mechanism of syndromes. With …
assists biologists and physicians to discover pathogenic mechanism of syndromes. With …
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data
Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target
not only microorganisms but also viruses and cancer cells. Due to their lower selection for …
not only microorganisms but also viruses and cancer cells. Due to their lower selection for …
Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods
Identification of disease-associated circular RNAs (circRNAs) is of critical importance,
especially with the dramatic increase in the amount of circRNAs. However, the availability of …
especially with the dramatic increase in the amount of circRNAs. However, the availability of …
Machine learning methods for microbiome studies
J Namkung - Journal of Microbiology, 2020 - Springer
Researches on the microbiome have been actively conducted worldwide and the results
have shown human gut bacterial environment significantly impacts on immune system …
have shown human gut bacterial environment significantly impacts on immune system …
Application of a two-step sampling strategy based on deep neural network for landslide susceptibility map**
J Yao, S Qin, S Qiao, X Liu, L Zhang, J Chen - Bulletin of Engineering …, 2022 - Springer
The selection of nonlandslide samples is a key issue in landslide susceptibility modeling
(LSM). In view of the potential subjectivity and randomness in random sampling, this paper …
(LSM). In view of the potential subjectivity and randomness in random sampling, this paper …
Class prior-free positive-unlabeled learning with Taylor variational loss for hyperspectral remote sensing imagery
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is
aimed at learning a binary classifier from positive and unlabeled data, which has broad …
aimed at learning a binary classifier from positive and unlabeled data, which has broad …
Mining insights on metal–organic framework synthesis from scientific literature texts
Identifying optimal synthesis conditions for metal–organic frameworks (MOFs) is a major
challenge that can serve as a bottleneck for new materials discovery and development. A …
challenge that can serve as a bottleneck for new materials discovery and development. A …