
The Galaxy bioinformatics platform significantly enhances workflow efficiency, diagnostic accuracy, and user satisfaction in clinical genetics laboratories.


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evaluation-of-galaxy-bioinformatics-platform-for-enhancing-clinical-diagnostics-in-genetics-laboratories


Evaluation of Galaxy Bioinformatics Platform for Enhancing Clinical Diagnostics in Genetics Laboratories


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scCDCG, a novel framework designed for efficient and accurate clustering of single-cell RNA-sequencing (scRNA-seq) data, simultaneously utilizes intercellular high-order structural information while overcoming the limitations of previous graph neural network-based methods.


efficient-deep-structural-clustering-for-single-cell-rna-sequencing-data-via-deep-cut-informed-graph-embedding


Efficient Deep Structural Clustering for Single-Cell RNA-Sequencing Data via Deep Cut-Informed Graph Embedding



AGAThA achieves significant speedup in GPU-based sequence alignment, outperforming existing baselines.


agatha-fast-and-efficient-gpu-acceleration-of-guided-sequence-alignment-for-long-read-mapping


AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping



Multiple kernel learning offers a natural framework for predictive models in multi-omics genomic data, providing a flexible and valid approach for integrating heterogeneous data sources.


supervised-multiple-kernel-learning-approaches-for-multi-omics-data-integration


Supervised Multiple Kernel Learning Approaches for Multi-Omics Data Integration



GPSite is a multi-task network that accurately predicts binding residues of various molecules on proteins, surpassing existing methods and enabling genome-scale annotations.


Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at <https://bio-web1.nscc-gz.cn/app/GPSite>.

### Competing Interest Statement

The authors have declared no competing interest.

Genome-scale annotation of protein binding sites via language model and geometric deep learning

genome-scale-annotation-of-protein-binding-sites-with-gpsite


Genome-scale Annotation of Protein Binding Sites with GPSite



Large language models can automate literature summarization for non-coding RNAs, improving curation efforts in life sciences.


automated-literature-summarization-for-non-coding-rnas-using-large-language-models


Automated Literature Summarization for Non-Coding RNAs Using Large Language Models



The application of ChatGPT in bioinformatics and biomedical informatics shows promise but also highlights limitations that can be addressed through strategic prompt engineering.


review-of-chatgpt-applications-in-bioinformatics-and-biomedical-informatics


Review of ChatGPT Applications in Bioinformatics and Biomedical Informatics



PTransIPs, a deep learning framework, outperforms existing methods in identifying phosphorylation sites by utilizing protein pre-trained language model (PLM) embeddings.


ptransips-identification-of-phosphorylation-sites-enhanced-by-protein-plm-embeddings


PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings



PROTLLM is a versatile large language model designed to handle both protein-centric and protein-language tasks efficiently.


protllm-a-versatile-protein-language-large-language-model-for-protein-centric-and-protein-language-tasks


PROTLLM: A Versatile Protein-Language Large Language Model for Protein-Centric and Protein-Language Tasks



Thought Graph introduces a novel framework for complex biological reasoning, surpassing existing methods in gene set analysis and semantic relationships.


thought-graph-a-novel-framework-for-biological-reasoning


Thought Graph: A Novel Framework for Biological Reasoning
