STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery.

Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and there exist rich relationships between objects (even between spatially disjoint objects), which makes it attractive to holistically conduct SGG in large-size very-high-resolution (VHR) SAI. However, there lack such SGG datasets.

KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions.

Cavity-Enhanced Circular Dichroism in a van der Waals Antiferromagnet.

Broken symmetry plays a pivotal role in determining the macroscopic electrical, optical, magnetic, and topological properties of materials. Circular dichroism (CD) has been widely employed to probe broken symmetry in various systems, from small molecules to bulk crystals, but designing CD responses on demand remains a challenge, especially for antiferromagnetic materials. Here we develop a cavity-enhanced CD technique to sensitively probe the magnetic order and broken symmetry in the van der Waals antiferromagnet FePS.

GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity.

Background: Drug-target binding affinity (DTA) prediction is vital in drug discovery and repositioning, more and more researchers are beginning to focus on this. Many effective methods have been proposed. However, some current methods have certain shortcomings in focusing on important nodes in drug molecular graphs and dealing with complex structural molecules.

CPI-GGS: A deep learning model for predicting compound-protein interaction based on graphs and sequences.

Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs.

Photoinduced hidden monoclinic metallic phase of VO driven by local nucleation.

The insulator-to-metal transition in VO has garnered extensive attention for its potential applications in ultrafast switches, neuronal network architectures, and storage technologies. However, the photoinduced insulator-to-metal transition remains controversial, especially whether a complete structural transformation from the monoclinic to rutile phase is necessary. Here we employ the real-time time-dependent density functional theory to track the dynamic evolution of atomic and electronic structures in photoexcited VO, revealing the emergence of a long-lived monoclinic metal phase under low electronic excitation.

HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.

Structural variation (SV) is an important component of the diversity of the human genome. Many studies have shown that SV has a significant impact on human disease and is strongly associated with the development of cancer. In recent years, the Hi-C sequencing technique has been shown to be useful for detecting large-scale SVs, and several methods have been proposed for identifying SVs from Hi-C data.

DeepHapNet: a haplotype assembly method based on RetNet and deep spectral clustering.

Gene polymorphism originates from single-nucleotide polymorphisms (SNPs), and the analysis and study of SNPs are of great significance in the field of biogenetics. The haplotype, which consists of the sequence of SNP loci, carries more genetic information than a single SNP. Haplotype assembly plays a significant role in understanding gene function, diagnosing complex diseases, and pinpointing species genes.

GTasm: a genome assembly method using graph transformers and HiFi reads.

Motivation: Genome assembly aims to reconstruct the whole chromosome-scale genome sequence. Obtaining accurate and complete chromosome-scale genome sequence serve as an indispensable foundation for downstream genomics analyses. Due to the complex repeat regions contained in genome sequence, the assembly results commonly are fragmented.

MPE-HRNet: A Lightweight High-Resolution Network for Multispecies Animal Pose Estimation.

Animal pose estimation is crucial for animal health assessment, species protection, and behavior analysis. It is an inevitable and unstoppable trend to apply deep learning to animal pose estimation. In many practical application scenarios, pose estimation models must be deployed on edge devices with limited resource.

Softening of the optical phonon by reduced interatomic bonding strength without depolarization.

Softening of the transverse optical (TO) phonon, which could trigger ferroelectric phase transition, can usually be achieved by enhancing the long-range Coulomb interaction over the short-range bonding force, for example, by increasing the Born effective charges. However, it suffers from depolarization effects as the induced ferroelectricity is suppressed on size reduction of the host materials towards high-density nanoscale electronics. Here, we present an alternative route to drive the TO phonon softening by showing that the abnormal soft TO phonon in rocksalt-structured ultrawide-bandgap BeO (ref.

MSFN: a multi-omics stacked fusion network for breast cancer survival prediction.

Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge.

GCphase: an SNP phasing method using a graph partition and error correction algorithm.

Background: The utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and sequencing errors in the reads, the recent methods still cannot yield satisfactory results.

Results: In this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing.

Predicting the satisfiability of Boolean formulas by incorporating gated recurrent unit (GRU) in the Transformer framework.

The Boolean satisfiability (SAT) problem exhibits different structural features in various domains. Neural network models can be used as more generalized algorithms that can be learned to solve specific problems based on different domain data than traditional rule-based approaches. How to accurately identify these structural features is crucial for neural networks to solve the SAT problem.

CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network.

The identification of cancer subtypes plays a very important role in the field of medicine. Accurate identification of cancer subtypes is helpful for both cancer treatment and prognosis Currently, most methods for cancer subtype identification are based on single-omics data, such as gene expression data. However, multi-omics data can show various characteristics about cancer, which also can improve the accuracy of cancer subtype identification.

DP-SSLoRA: A privacy-preserving medical classification model combining differential privacy with self-supervised low-rank adaptation.

Background And Objective: Concerns about patient privacy issues have limited the application of medical deep learning models in certain real-world scenarios. Differential privacy (DP) can alleviate this problem by injecting random noise into the model. However, naively applying DP to medical models will not achieve a satisfactory balance between privacy and utility due to the high dimensionality of medical models and the limited labeled samples.

Dynamical Symmetry-Reduction-Induced Giant Anharmonicity in IV-VI Compounds: Role of Cation Lone-Pair Electrons.

The low thermal conductivity of group IV-VI semiconductors is often attributed to the soft phonons and giant anharmonicity observed in these materials. However, there is still no broad consensus on the fundamental origin of this giant anharmonic effect. Utilizing first-principles calculations and group symmetry analysis, we find that the cation lone-pairs electrons in IV-VI materials cause a significant coupling between occupied cation orbitals and unoccupied cation orbitals due to the symmetry reduction when atoms vibrate away from their equilibrium positions under heating.

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance.

LcDel: deletion variation detection based on clustering and long reads.

Genomic structural variation refers to chromosomal level variations such as genome rearrangement or insertion/deletion, which typically involve larger DNA fragments compared to single nucleotide variations. Deletion is a common type of structural variants in the genome, which may lead to mangy diseases, so the detection of deletions can help to gain insights into the pathogenesis of diseases and provide accurate information for disease diagnosis, treatment, and prevention. Many tools exist for deletion variant detection, but they are still inadequate in some aspects, and most of them ignore the presence of chimeric variants in clustering, resulting in less precise clustering results.

CD-Loop: a chromatin loop detection method based on the diffusion model.

Motivation: In recent years, there have been significant advances in various chromatin conformation capture techniques, and annotating the topological structure from Hi-C contact maps has become crucial for studying the three-dimensional structure of chromosomes. However, the structure and function of chromatin loops are highly dynamic and diverse, influenced by multiple factors. Therefore, obtaining the three-dimensional structure of the genome remains a challenging task.

Learning to Holistically Detect Bridges From Large-Size VHR Remote Sensing Imagery.

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs.

Drug-drug interactions prediction based on deep learning and knowledge graph: A review.

Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations.

Direct bandgap emission from strain-doped germanium.

Germanium (Ge) is an attractive material for Silicon (Si) compatible optoelectronics, but the nature of its indirect bandgap renders it an inefficient light emitter. Drawing inspiration from the significant expansion of Ge volume upon lithiation as a Lithium (Li) ion battery anode, here, we propose incorporating Li atoms into the Ge to cause lattice expansion to achieve the desired tensile strain for a transition from an indirect to a direct bandgap. Our first-principles calculations show that a minimal amount of 3 at.

Light-induced ultrafast spin transport in multilayer metallic films originates from - spin exchange coupling.

Ultrafast interaction between the femtosecond laser pulse and the magnetic metal provides an efficient way to manipulate the magnetic states of matter. Numerous experimental advancements have been made on multilayer metallic films in the last two decades. However, the underlying physics remains unclear.