AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection.

Background: Single-cell RNA sequencing (scRNA-seq) is now essential for cellular-level gene expression studies and deciphering complex gene regulatory mechanisms. Deep learning methods, when combined with scRNA-seq technology, transform gene regulation research into graph link prediction tasks. However, these methods struggle to mitigate the impact of noisy data in gene regulatory networks (GRNs) and address the significant imbalance between positive and negative links.

Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms.

Motivation: Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance.

Trans-MoRFs: A Disordered Protein Predictor Based on the Transformer Architecture.

Intrinsically disordered regions (IDRs) of proteins are crucial for a wide range of biological functions, with molecular recognition features (MoRFs) being of particular significance in protein interactions and cellular regulation. However, the identification of MoRFs has been a significant challenge in computational biology owing to their disorder-to-order transition properties. Currently, only a limited number of experimentally validated MoRFs are known, which has prompted the development of computational methods for predicting MoRFs from protein chains.

Multiview Deep Learning-Based Molecule Design and Structural Optimization Accelerates Inhibitor Discover.

In this work, we propose MEDICO, a multiview deep generative model for molecule generation, structural optimization, and the SARS-CoV-2 inhibitor discovery. To the best of our knowledge, MEDICO is the first-of-this-kind graph generative model that can generate molecular graphs similar to the structure of targeted molecules, with a multiview representation learning framework to sufficiently and adaptively learn comprehensive structural semantics from targeted molecular topology and geometry. We show that our MEDICO significantly outperforms the state-of-the-art methods in generating valid, novel, and unique molecules under benchmarking comparisons, particularly achieving ˜85% improvement compared with the state-of-the-art methods in terms of validity.

DSANIB: drug-target interaction predictions with dual-view synergistic attention network and information bottleneck strategy.

Prediction of drug-target interactions (DTIs) is one of the crucial steps for drug repositioning. Identifying DTIs through bio-experimental manners is always expensive and time-consuming. Recently, deep learning-based approaches have shown promising advancements in DTI prediction, but they face two notable challenges: (i) how to explicitly capture local interactions between drug-target pairs and learn their higher-order substructure embeddings; (ii) How to filter out redundant information to obtain effective embeddings for drugs and targets.

Integration of Multi-Source Medical Data for Medical Diagnosis Question Answering.

Medical question answering aims to enhance diagnostic support, improve patient education, and assist in clinical decision-making by automatically answering medicalrelated queries, which is an important foundation for realizing intelligent healthcare. Existing methods predominantly focus on extracting key information from a single data source, e.g.

A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects and are of great significance for drug development and clinical research. However, predicting DDI through traditional clinical trials and experiments is not only costly but also time-consuming.

FORAlign: accelerating gap-affine DNA pairwise sequence alignment using FOR-blocks based on Four Russians approach with linear space complexity.

Pairwise sequence alignment (PSA) serves as the cornerstone in computational bioinformatics, facilitating multiple sequence alignment and phylogenetic analysis. This paper introduces the FORAlign algorithm, leveraging the Four Russians algorithm with identical upper-bound time and space complexity as the Hirschberg divide-and-conquer PSA algorithm, aimed at accelerating Hirschberg PSA algorithm in parallel. Particularly notable is its capability to achieve up to 16.

ACP-CLB: An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models.

Motivation: Cancer affects millions globally, and as research advances, our understanding and treatment of cancer evolve. Compared to conventional treatments with significant side effects, anticancer peptides (ACPs) have gained considerable attention. Validating ACPs through wet-lab experiments is time-consuming and costly.

iBitter-GRE: A Novel Stacked Bitter Peptide Predictor with ESM-2 and Multi-View Features.

Accurate identification of bitter peptides is essential for research. Although models using sequence information have evolved in the context of bitter peptides, there is still room for improvement in their predictive performance. In the present study, we introduced a novel predictive tool, iBitter-GRE, designed to improve the accuracy of bitter peptide identification.

Fast sequence alignment for centromere with RaMA.

The release of the first draft of the human pangenome has revolutionized genomic research by enabling access to complex regions like centromeres, composed of extra-long tandem repeats (ETRs). However, a significant gap remains as current methodologies are inadequate for producing sequence alignments that effectively capture genetic events within ETRs, highlighting a pressing need for improved alignment tools. Inspired by UniAligner, we develope Rare Match Aligner (RaMA), using rare matches as anchors and 2-piece affine gap cost to generate complete pairwise alignment that better capture genetic evolution.

lncRNA localization and feature interpretability analysis.

Subcellular localization is crucial for understanding the functions and regulatory mechanisms of biomolecules. Long non-coding RNAs (lncRNAs) have diverse roles in cellular processes, and their localization within specific subcellular compartments provides insights into their biological functions and implications in health and disease. The nucleolus and nucleoplasm are key hubs for RNA metabolism and cellular regulation.

Investigation of Intestinal Microbes of Five Zokor Species Based on 16S rRNA Sequences.

Zokor is a group of subterranean rodents that are adapted to underground life and feed on plant roots. Here, we investigated the intestinal microbes of five zokor species (, , , , and ) using 16S amplicon technology combined with bioinformatics. Microbial composition analysis showed similar intestinal microbes but different proportions among five zokor species, and their dominant bacteria corresponded to those of herbivores.

Identify potential drug candidates within a high-quality compound search space.

The identification of potential effective drug candidates is a fundamental step in new drug discovery, with profound implications for pharmaceutical research and the healthcare sector. While many computational methods have been developed for such predictions and have yielded promising results, two challenges persist: (i) The cold start problem of new drugs, which increases the difficulty of prediction due to lack of historical data or prior knowledge. (ii) The vastness of the compound search space for potential drug candidates.

TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.

Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer's, neurodegeneration, viral infection, and cancer. Therefore, correctly identifying clathrin protein functions is critical to unravel the mechanism of these fatal diseases and designing drug targets.

PROTCROWN: A Manually Curated Resource of Protein Corona Data for Unlocking the Potential of Protein-Nanoparticle Interactions.

The emergence of the "Protein Corona" is a pivotal concept in bioinformatics and nanotechnology, crucial for understanding nanomedicine delivery and nanoparticle-biological entity interactions. After entering a biological fluid, such as blood, nanoparticles (NPs, such as nanomedical carriers) are quickly coated with proteins, forming a protein interface layer called the protein corona. An in-depth investigation into the protein corona is essential for elucidating the biological ramifications of NPs and their prospective applications within the medical field and beyond.

ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.

Motivation: Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug-target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs' degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex.

Multimodal deep learning approaches for precision oncology: a comprehensive review.

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) as a cornerstone of precision oncology. This review provides an overview of MDL applications in this field, based on an extensive literature survey. In total, 651 articles published before September 2024 are included.

Functional Genomics and Comparative Genomics Analysis in Plants.

The study of plant genomics has significantly deepened our understanding of plant evolution and adaptation from a microscopic perspective [...

stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization.

Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges.

ReAlign-N: an integrated realignment approach for multiple nucleic acid sequence alignment, combining global and local realignments.

Ensuring accurate multiple sequence alignment (MSA) is essential for comprehensive biological sequence analysis. However, the complexity of evolutionary relationships often results in variations that generic alignment tools may not adequately address. Realignment is crucial to remedy this issue.

GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering.

Single-cell multi-omics refers to the various types of biological data at the single-cell level. These data have enabled insight and resolution to cellular phenotypes, biological processes, and developmental stages. Current advances hold high potential for breakthroughs by integrating multiple different omics layers.

DeepAIPs-Pred: Predicting Anti-Inflammatory Peptides Using Local Evolutionary Transformation Images and Structural Embedding-Based Optimal Descriptors with Self-Normalized BiTCNs.

Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating tissue repair by eradicating pathogenic microorganisms. However, when inflammation becomes chronic, it leads to numerous serious disorders, particularly in autoimmune diseases. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents due to their high specificity, potency, and low toxicity.