AIMC Topic: Animals

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Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...

Dynamic Modeling, Analysis, and Design Synthesis of a Reduced Complexity Quadruped with a Serpentine Robotic Tail.

Integrative and comparative biology
Serpentine tail structures are widely observed in the animal kingdom and are thought to help animals to handle various motion tasks. Developing serpentine robotic tails and using them on legged robots has been an attractive idea for robotics. This ar...

Inertial Tail Effects during Righting of Squirrels in Unexpected Falls: From Behavior to Robotics.

Integrative and comparative biology
Arboreal mammals navigate a highly three dimensional and discontinuous habitat. Among arboreal mammals, squirrels demonstrate impressive agility. In a recent "viral" YouTube video, unsuspecting squirrels were mechanically catapulted off of a track, i...

Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...

Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.

Briefings in bioinformatics
Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discrimina...

Machine learning approach to gene essentiality prediction: a review.

Briefings in bioinformatics
UNLABELLED: Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to...

Venn diagrams in bioinformatics.

Briefings in bioinformatics
Venn diagrams are widely used tools for graphical depiction of the unions, intersections and distinctions among multiple datasets, and a large number of programs have been developed to generate Venn diagrams for applications in various research areas...

Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Briefings in bioinformatics
Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis ...

Prediction and collection of protein-metabolite interactions.

Briefings in bioinformatics
Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors ...

XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.

Briefings in bioinformatics
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...