AIMC Topic: Computational Biology

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AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Nucleic acids research
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play im...

TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.

Genomics, proteomics & bioinformatics
Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is no...

Geno-GCN: A Genome-specific Graph Convolutional Network for Diabetes Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drawing inspiration from convolutional neural networks, graph convolutional networks (GCNs) have been implemented in various applications. Yet, the integration of GCNs into clinical settings, particularly in the context of complex health conditions l...

Predicting Functional Surface Topographies Combining Topological Data Analysis and Deep Learning Across the Human Protein Universe.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Characterizing geometric and topological properties of protein structures encompassing surface pockets, interior cavities, and cross channels is important for understanding their functions. Our knowledge of protein structures has been greatly advance...

Geometric epitope and paratope prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this article, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the ...

SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.

Bioinformatics (Oxford, England)
MOTIVATION: The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, techn...

DeepGSEA: explainable deep gene set enrichment analysis for single-cell transcriptomic data.

Bioinformatics (Oxford, England)
MOTIVATION: Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequenci...

A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

Bioinformatics (Oxford, England)
MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent v...

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...

Adaptive digital tissue deconvolution.

Bioinformatics (Oxford, England)
MOTIVATION: The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systema...