AIMC Topic: Computational Biology

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Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.

Microbiology spectrum
Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse pr...

Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the ...

TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics.

Journal of molecular biology
Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tissue behavior. Researchers have shown the capability of shallow neural networks in reconstructing CCI,...

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

The protein journal
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA b...

ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms.

Biochimica et biophysica acta. General subjects
BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic ...

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues.

Methods (San Diego, Calif.)
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection ov...

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

BMC bioinformatics
RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods fo...

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. There...

Generalization analysis of deep CNNs under maximum correntropy criterion.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) have gained immense popularity in recent years, finding their utility in diverse fields such as image recognition, natural language processing, and bio-informatics. Despite the remarkable progress made in deep lea...

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

The Journal of biological chemistry
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding R...