Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

The discovery of diagnostic and therapeutic biomarkers for complex diseases, especially cancer, has always been a central and long-term challenge in molecular association prediction research, offering promising avenues for advancing the understanding of complex diseases. To this end, researchers have developed various network-based prediction techniques targeting specific molecular associations. However, limitations imposed by reductionism and network representation learning have led existing studies to narrowly focus on high prediction efficiency within single association type, thereby glossing over the discovery of unknown types of associations.

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

Exploring potential association between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is an essential part of prevention, diagnosis and treatment of diseases. Since determining these relationships experimentally is resource-intensive and time-consuming, therefore computational methods have emerged as an attractive way to address this issue. However, existing computational approaches for inferring lncRNA-disease associations (LDA), miRNA-disease associations (MDA) and lncRNA-miRNA interactions (LMI) tend to focus on single task, neglecting the benefits of leveraging multiple biomolecular interactions and domain-specific knowledge for multi-task prediction.

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

Background: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths.

Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data.

Deep learning has shown impressive diagnostic abilities in Alzheimer's disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling.

Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching.

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points.

The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions.

Watermelon, Citrullus lanatus, is an important cucurbit crop grown throughout the world. Here we report a high-quality draft genome sequence of the east Asia watermelon cultivar 97103 (2n = 2× = 22) containing 23,440 predicted protein-coding genes. Comparative genomics analysis provided an evolutionary scenario for the origin of the 11 watermelon chromosomes derived from a 7-chromosome paleohexaploid eudicot ancestor.

A high resolution genetic map anchoring scaffolds of the sequenced watermelon genome.

As part of our ongoing efforts to sequence and map the watermelon (Citrullus spp.) genome, we have constructed a high density genetic linkage map. The map positioned 234 watermelon genome sequence scaffolds (an average size of 1.