AIMC Topic: Software

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deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.

Briefings in bioinformatics
MOTIVATION: Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current method...

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.

Briefings in bioinformatics
Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due to the complex and multi-omics nature of cancer data. Existing methods often struggle to capture the comprehensive range of informative features requir...

BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.

Briefings in bioinformatics
RNA biomarkers enable early and precise disease diagnosis, monitoring, and prognosis, facilitating personalized medicine and targeted therapeutic strategies. However, identification of RNA biomarkers is hindered by the challenge of analyzing relative...

TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Briefings in bioinformatics
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment ...

MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.

Briefings in bioinformatics
Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detectin...

PharaCon: a new framework for identifying bacteriophages via conditional representation learning.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. Ho...

COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive d...

AskBeacon-performing genomic data exchange and analytics with natural language.

Bioinformatics (Oxford, England)
MOTIVATION: Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables large language models (LLMs) to be applied to securely shared ...

MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Plasmids play an essential role in horizontal gene transfer, aiding their host bacteria in acquiring beneficial traits like antibiotic and metal resistance. There exist some plasmids that can transfer, replicate, or persist in multiple or...

DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.

Bioinformatics (Oxford, England)
MOTIVATION: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information i...