Few-shot contrastive learning for image classification and its application to insulator identification
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-021-02769-6
Abstract: This paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition. Most existing CNN methods facilitate classifiers to learn… read more here.
Keywords: image; learning image; shot; image classification ... See more keywords
Contrastive Learning-Based Embedder for the Representation of Tandem Mass Spectra.
Sign Up to like & getrecommendations! Published in 2023 at "Analytical chemistry"
DOI: 10.1021/acs.analchem.3c00260
Abstract: Tandem mass spectrometry (MS/MS) shows great promise in the research of metabolomics, providing an abundance of information on compounds. Due to the rapid development of mass spectrometric techniques, a large number of MS/MS spectral data… read more here.
Keywords: tandem mass; learning based; contrastive learning; spectra ... See more keywords
CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only.
Sign Up to like & getrecommendations! Published in 2023 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.3c00297
Abstract: Anticancer peptides (ACPs) recently have been receiving increasing attention in cancer therapy due to their low consumption, few adverse side effects, and easy accessibility. However, it remains a great challenge to identify anticancer peptides via… read more here.
Keywords: cacpp contrastive; identify anticancer; anticancer peptides; contrastive learning ... See more keywords
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
Sign Up to like & getrecommendations! Published in 2022 at "Nature Communications"
DOI: 10.1038/s41467-022-28543-x
Abstract: Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS)… read more here.
Keywords: density; contrastive learning; spectral properties; prediction ... See more keywords
Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.
Sign Up to like & getrecommendations! Published in 2023 at "Physics in medicine and biology"
DOI: 10.1088/1361-6560/acda78
Abstract: OBJECTIVE Multi-parametric MR image synthesis is an effective approach for several clinical applications where specific modalities may be unavailable to reach a diagnosis. While technical and practical conditions limit the acquisition of new modalities for… read more here.
Keywords: bidirectional feature; feature; synthesis; contrastive learning ... See more keywords
SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac545
Abstract: BACKGROUND Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine… read more here.
Keywords: cell; network; siamesecpp; cell penetrating ... See more keywords
Matching single cells across modalities with contrastive learning and optimal transport
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbad130
Abstract: Abstract Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening… read more here.
Keywords: optimal transport; learning optimal; cells across; single cell ... See more keywords
Supervised graph co-contrastive learning for drug-target interaction prediction
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac164
Abstract: MOTIVATION Identification of Drug-Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning based methods have aroused widespread… read more here.
Keywords: drug target; dti prediction; prediction; contrastive learning ... See more keywords
Molecular property prediction by contrastive learning with attention-guided positive sample selection
Sign Up to like & getrecommendations! Published in 2023 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btad258
Abstract: Abstract Motivation Predicting molecular properties is one of the fundamental problems in drug design and discovery. In recent years, self-supervised learning (SSL) has shown its promising performance in image recognition, natural language processing, and single-cell… read more here.
Keywords: sample; attention guided; contrastive learning; molecular property ... See more keywords
Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.
Sign Up to like & getrecommendations! Published in 2023 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btad342
Abstract: Abstract Motivation Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of… read more here.
Keywords: seq data; cell; prototypical contrastive; single cell ... See more keywords
Similarity measures based graph co-contrastive learning for drug-disease association prediction.
Sign Up to like & getrecommendations! Published in 2023 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btad357
Abstract: MOTIVATION An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet… read more here.
Keywords: graph contrastive; drug disease; prediction; contrastive learning ... See more keywords