AIMC Topic: Computational Biology

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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...

Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.

Bioinformatics (Oxford, England)
MOTIVATION: S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration ...

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...

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

Bioinformatics (Oxford, England)
MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical out...

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...

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...

MolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately identifying and predicting molecular properties is a crucial task in molecular machine learning, and the key lies in how to extract effective molecular representations. Contrastive learning opens new avenues for representation ...

ParaSurf: a surface-based deep learning approach for paratope-antigen interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving ...

SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).