Special issue on Statistics, Bioinformatics, and Machine Learning Methods in Phenomics

Plant Phenomics is pleased to announce a special issue on Statistics, Bioinformatics, and Machine Learning Methods in Phenomics.

Submission Deadline: April 1st, 2020

Dear Colleague,

The Science Partner JournalPlant Phenomics is pleased to announce a special issue on Statistics, Bioinformatics, and Machine Learning Methods in Phenomics.

Today, modern plant phenotyping applications are challenging our existing methods for statistical and computational analyses. There is a great need for new analytical approaches that can integrate multiple types of data or provide proper experimental design in observational contexts. This need will only grow with the development of imaging, sequencing, and sensing technologies. A recent push in this direction has been an emphasis on machine learning and artificial intelligence. However, to date most AI applications require further development in order to be useful in phenotyping contexts. This may involve tailored algorithms that incorporate physical and biological constraints, or a reduction in the need for vast amounts of training data. Advances in statistics, bioinformatics, and machine learning will enable the plant phenotyping community to address pressing societal challenges such as responsible ecological management and sustainable improvements in crop productivity. We are looking for papers that present novel analytical methodology and applications including standards of practice, experimental design, software, scientific reviews, and position pieces to be submitted to the special issue on “Statistics, Bioinformatics, and Machine Learning Methods in Phenomics”.

Plant Phenomics is an international open access journal published in affiliation with Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). The journal publishes novel research that can advance all aspects of plant phenotyping in whole plant or cellular levels through novel mathematical and computational technologies in phenotypic data analyses.

Interested in submitting your research for consideration for publication in Plant Phenomics? Learn more about Plant Phenomics and how to submit your manuscript here.


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