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Found 9 papers, 5 papers with code
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Artificial intelligence (AI) is transforming scientific research, including proteomics.
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies.
CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction
We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former's interaction understanding.
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities.
Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images
G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology.
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology.
Multi-channel deep convolutional neural networks for multi-classifying thyroid disease
Therefore, this study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease.
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.
Feature Erasing and Diffusion Network for Occluded Person Re-Identification
With the guidance of the occlusion scores from OEM, the feature diffusion process is mainly conducted on visible body parts, which guarantees the quality of the synthesized NTP characteristics.
Ranked #3 on
Person Re-Identification
on Occluded REID
(Rank-1 metric)
