contrastive learning

Exploring Protein-DNA Binding Residue Prediction and Consistent Interpretability Analysis Using Deep Learning

This study introduces a sequence-based deep learning method for predicting DNA-binding residues, outperforming existing models and demonstrating interpretability through contrastive learning and graph neural networks.

Single-Cell Data Integration and Cell Type Annotation through Contrastive Adversarial Open-set Domain Adaptation

SAFAARI integrates single-cell data, corrects batch effects, and annotates cell types using contrastive adversarial domain adaptation, outperforming existing methods in multiple benchmarks.

A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy

This study compares 2D and 3D U-Net for segmenting human aorta from CT images, finding 3D U-Net performs better but has geometry reconstruction errors.

Unlearning Virus Knowledge Toward Safe and Responsible Mutation Effect Predictions

This paper introduces PROEDIT, a method to remove virus-related knowledge from pre-trained protein models to improve safety in mutation effect predictions without affecting performance on non-virus proteins.

EmbedSimScore: Advancing Protein Similarity Analysis with Structural and Contextual Embeddings

EmbedSimScore improves protein similarity analysis by integrating structural and contextual embeddings to capture both local and global features.

Contrastive learning explains the emergence and function of visual category-selective regions

This paper introduces contrastive coding as a framework explaining category-selective regions in the human brain using neural network analyses.

Interpreting Sleep Activity Through Neural Contrastive Learning

The paper develops a neural network model, Sleep Interpreter (SI), to decode memory replay from sleep EEG and shows it can accurately detect reactivation during sleep that correlates with memory performance.

LOCAS: Multi-label mRNA Localization with Supervised Contrastive Learning

LOCAS improves mRNA subcellular localization by using supervised contrastive learning and a multi-label classification head, achieving state-of-the-art performance.

Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis

This paper introduces scSimGCL, a graph contrastive learning framework for scRNA-seq data that improves cell clustering through self-supervised pretraining and high-quality representation learning.

Parameter-free clear image deconvolution (CLID) technique for single-frame live-cell super-resolution imaging

This paper introduces CLID techniques for single-frame live-cell super-resolution imaging, achieving 65 nm resolution without complex parameters.

3D Molecular Pretraining via Localized Geometric Generation

This paper introduces LEGO, a self-supervised learning method for 3D molecular structures using tetrahedrons as building blocks, showing improved performance in molecular property prediction.

Unlocking cross-modal interplay of single-cell and spatial joint profiling with CellMATE

CellMATE is a method that captures both additive and synergistic benefits of multimodal joint profiling by integrating single-cell and spatial data into a unified latent space.

High-throughput Image-based Clustering of CAR-T/Tumor Cocultures for Rapid and Facile Hit Identification

This paper introduces a novel image clustering approach for CAR-T/tumor cocultures using U-net and clustering algorithms, demonstrating superior performance in classification and data interpretation.

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

This study applies FastSAM for automated algal cell segmentation and clustering in water samples, showing high accuracy for single cells and varying clustering performance.

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

This paper introduces DISCOVER, a generative model that disentangles latent spaces to provide interpretable explanations for image-based classification models in in vitro fertilization.

Optimizing biomedical information retrieval with a keyword frequency-driven Prompt Enhancement Strategy

This paper enhances biomedical question-answering using a keyword frequency-driven prompt strategy to improve LLM responses and reduce hallucinations.

Enhancing Challenging Target Screening via Multimodal Protein-Ligand Contrastive Learning

Uni-Clip improves protein-ligand interaction modeling for undruggable and druggable targets using multimodal contrastive learning, showing significant performance gains in benchmark datasets.

CLEAR-IT: Contrastive Learning to Capture the Immune Composition of Tumor Microenvironments

CLEAR-IT improves cell phenotyping in tumor microenvironments by using contrastive learning, outperforming existing methods across imaging modalities and requiring fewer labels.

Genetic underpinnings of predicted changes in cardiovascular function using self supervised learning

This paper uses self-supervised learning to analyze ECG data and identifies genetic associations with cardiovascular state changes.

LitGene: a transformer-based model that uses contrastive learning to integrate textual information into gene representations

LitGene integrates textual information into gene representations using a transformer-based model with contrastive learning, improving disease risk gene prediction and interpretability.

Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders

The paper introduces a masked autoencoder framework to analyze hierarchical behavior in motion capture data using synthetic and real benchmarks.

Image retrieval based on closed-loop visual-semantic neural decoding

This paper demonstrates that closed-loop brain-computer interfaces can retrieve intended images using a CLIP latent space, achieving over 80% accuracy, while failing with AlexNet.

Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction (laruGL-PPI)

This paper introduces laruGL-PPI, a label-aware hierarchical subgraph learning method for protein-protein interaction prediction that improves performance and efficiency by addressing topological shortcuts and computational costs.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

This study trains a conditional generative adversarial network to estimate tumor spheroid elasticity from phase difference images, showing improved robustness to noise compared to traditional methods.

Cross-species prediction of transcription factor binding by adversarial training of a novel nucleotide-level deep neural network

This study introduces a novel deep neural network for cross-species TF binding prediction and improves performance through adversarial training.

Weakly supervised learning uncovers phenotypic signatures in single-cell data

The paper introduces MultiMIL, a deep learning framework for phenotype prediction and cell state identification using single-cell data from COVID-19 patients and other datasets.

INTREPPPID - An Orthologue-Informed Quintuplet Network for Cross-Species Prediction of Protein-Protein Interaction

INTREPPPID improves cross-species protein-protein interaction prediction using orthologue-informed neural networks and outperforms existing methods on evaluation datasets.

Contrastive Learning for Graph-Based Biological Interaction Discovery: Insights from Oncologic Pathways

This study applies contrastive learning to isomorphic graphs to discover biological interactions in cancer pathways, identifying new connections and central nodes.

Scorpio : Enhancing Embeddings to Improve Downstream Analysis of DNA sequences

Scorpio improves DNA sequence embeddings using contrastive learning, enhancing performance in taxonomic classification, AMR gene detection, and promoter identification across diverse datasets.

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

This paper evaluates CNN models for brain age prediction and dementia classification using diffusion MRI, showing improved performance after dataset harmonization across Indian and North American cohorts.