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Molecular Sequence Annotation

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MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.

Bioinformatics (Oxford, England)
MOTIVATION: As the biological roles and disease implications of non-coding RNAs continue to emerge, the need to thoroughly characterize previously unexplored non-coding RNAs becomes increasingly urgent. These molecules hold potential as biomarkers an...

AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning.

The plant genome
The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining p...

stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.

Nucleic acids research
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression leve...

UniProt: the Universal Protein Knowledgebase in 2025.

Nucleic acids research
The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe o...

ASpdb: an integrative knowledgebase of human protein isoforms from experimental and AI-predicted structures.

Nucleic acids research
Alternative splicing is a crucial cellular process in eukaryotes, enabling the generation of multiple protein isoforms with diverse functions from a single gene. To better understand the impact of alternative splicing on protein structures, protein-p...

scGO: interpretable deep neural network for cell status annotation and disease diagnosis.

Briefings in bioinformatics
Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making pr...

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

Bioinformatics (Oxford, England)
SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approache...

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

Bioinformatics (Oxford, England)
MOTIVATION: Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This man...

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and ...

From tradition to innovation: conventional and deep learning frameworks in genome annotation.

Briefings in bioinformatics
Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the...