Abnormality-aware multimodal learning for WSI classification.

Whole slide images (WSIs) play a vital role in cancer diagnosis and prognosis. However, their gigapixel resolution, lack of pixel-level annotations, and reliance on unimodal visual data present challenges for accurate and efficient computational analysis. Existing methods typically divide WSIs into thousands of patches, which increases computational demands and makes it challenging to effectively focus on diagnostically relevant regions.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks.

Toward Robust Self-Training Paradigm for Molecular Prediction Tasks.

Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning.

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development.

NuSegDA: Domain adaptation for nuclei segmentation.

The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. To successfully train a nuclei segmentation network in a fully-supervised manner for a particular type of organ or cancer, we need the dataset with ground-truth annotations. However, such well-annotated nuclei segmentation datasets are highly rare, and manually labeling an unannotated dataset is an expensive, time-consuming, and tedious process.

Molecule Sequence Generation with Rebalanced Variational Autoencoder Loss.

Molecule generation is the procedure to generate initial novel molecule proposals for molecule design. Molecules are first projected into continuous vectors in chemical latent space, and then, these embedding vectors are decoded into molecules under the variational autoencoder (VAE) framework. The continuous latent space of VAE can be utilized to generate novel molecules with desired chemical properties and further optimize the desired chemical properties of molecules.

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction.

The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing.

Cross-dependent graph neural networks for molecular property prediction.

Motivation: The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through graph neural networks (GNNs). Both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model ought to exploit both node (atom) and edge (bond) information simultaneously.

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI.

Identification of Introgressed Alleles Conferring High Fiber Quality Derived From L. in Secondary Mapping Populations of L.

The improvement of fiber quality is an essential goal in cotton breeding. In our previous studies, several quantitative trait loci (QTLs) contributing to improved fiber quality were identified in different introgressed chromosomal regions from Sea Island cotton ( L.) in a primary introgression population (Pop.