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Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

Proceedings of the National Academy of Sciences of the United States of America
The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Most significantly, we classify circadian genes using DNA sequence features genera...

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

The Plant journal : for cell and molecular biology
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments ar...

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...

A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Scientific reports
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions...

A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana.

BMC plant biology
BACKGROUND: Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to ...

A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.

Scientific reports
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell memb...

An automatic method to quantify trichomes in Arabidopsis thaliana.

Plant science : an international journal of experimental plant biology
Trichomes are unicellular or multicellular hair-like appendages developed on the aerial plant epidermis of most plant species that act as a protective barrier against natural hazards. For this reason, evaluating the density of trichomes is a valuable...

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Analytical biochemistry
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating c...

3D Visualization of Microtubules in Epidermal Pavement Cells.

Methods in molecular biology (Clifton, N.J.)
The plant cytoskeleton is instrumental in cellular processes such as cell growth, differentiation, and immune response. Microtubules, in particular, play a crucial role in morphogenesis by governing the deposition of plant cell wall polysaccharides a...

I-DNAN6mA: Accurate Identification of DNA N-Methyladenine Sites Using the Base-Pairing Map and Deep Learning.

Journal of chemical information and modeling
The recent discovery of numerous DNA N-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-ef...