AIMC Topic: Computational Biology

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Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis.

Scientific reports
Renal stones (RS) are common urologic condition with unclear pathogenesis. Role of aging-related differentially expressed genes (ARDEGs) in RS remains poorly understood. This study aims to identify potential aging-related biomarkers for RS, explore t...

RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.

Nature communications
While RNA has recently been recognized as an interesting small-molecule drug target, many challenges remain to be addressed before we take full advantage of it. This emphasizes the necessity to improve our understanding of its structures and function...

Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Nature communications
Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and r...

Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.

Genome research
Recent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins ...

A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings.

PloS one
An important issue in biotechnology is predicting whether a piRNA and an mRNA will or will not bind. Research and treatment of diseases, drug discovery, and the silencing and regulation of genes, transposons, and genomic stability may all benefit fro...

DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.

PloS one
As one of the most critical post-replication modifications, N6-methylation (6mA) at adenine residue plays an important role in a variety of biological functions. Existing computational methods for identifying 6mA sites across large genomic regions te...

Next-generation cancer therapeutics: unveiling the potential of liposome-based nanoparticles through bioinformatics.

Mikrochimica acta
Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target releas...

AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding.

Science advances
B cell epitope prediction tools are crucial for designing vaccines and disease diagnostics. However, predicting which antigens a specific antibody binds to and their exact binding sites (epitopes) remains challenging. Here, we present AbEpiTope-1.0, ...

Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks.

Journal of chemical information and modeling
Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs' roles in complex diseases, providing a theoretical basis for disease mechanisms. Existing ceRNA-disease association prediction methods ar...