in silico Plants

Scope & Guideline

Unlocking the potential of plant science for a greener future.

Introduction

Welcome to the in silico Plants information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of in silico Plants, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN-
PublisherOXFORD UNIV PRESS
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationIN SILICO PLANTS / In Silico Plants
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND

Aims and Scopes

The journal 'In Silico Plants' primarily focuses on the integration of computational modeling and simulation techniques to advance the understanding of plant biology, agriculture, and environmental interactions. It aims to provide a platform for innovative research that leverages data-driven approaches to enhance plant science and crop productivity.
  1. Computational Modeling of Plant Systems:
    The journal emphasizes the development and application of computational models that simulate various plant physiological processes, growth patterns, and responses to environmental conditions.
  2. Integration of Genomics and Phenotyping:
    A core focus is on integrating genomic data with phenotypic traits through modeling approaches, enabling researchers to predict plant behavior and improve breeding strategies.
  3. Functional-Structural Plant Modeling:
    The journal highlights research on functional-structural models that examine the architecture and dynamics of plants, helping to understand how structure influences function and performance.
  4. Systems Biology Approaches:
    It promotes the use of systems biology to explore gene regulatory networks and interactions within plant systems, providing insights into genetic control of traits.
  5. Interdisciplinary Research:
    Encouraging collaboration across disciplines, the journal seeks contributions that combine plant sciences with computational techniques, machine learning, and data analytics.
Recent publications in 'In Silico Plants' reveal several emerging themes that reflect the evolving landscape of plant research, driven by technological advancements and interdisciplinary collaboration.
  1. Machine Learning and Artificial Intelligence in Plant Modeling:
    There is a growing trend towards utilizing machine learning and AI techniques to enhance predictive modeling of plant traits and responses, showcasing the potential for data-driven insights in plant science.
  2. High-Throughput Phenotyping:
    Research focusing on high-throughput phenotyping techniques is increasingly prevalent, driven by the need for rapid and efficient assessment of plant traits across diverse environments.
  3. Climate Change Resilience Modeling:
    Emerging themes include modeling plant responses to climate change, highlighting the importance of developing resilient crop varieties that can adapt to shifting environmental conditions.
  4. Integration of Remote Sensing Technologies:
    The use of remote sensing data in plant modeling is on the rise, allowing for improved monitoring of crop performance and environmental interactions at larger scales.
  5. Synthetic Data Generation for Model Training:
    A novel approach being explored is the generation of synthetic datasets from functional-structural models to train machine learning algorithms, enhancing the robustness of predictive models.

Declining or Waning

Over the years, certain themes within 'In Silico Plants' have seen a decline in focus, possibly indicating shifts in research priorities or advancements in methodologies that have rendered previous approaches less relevant.
  1. Traditional Empirical Models:
    There has been a noticeable decrease in the publication of papers relying solely on traditional empirical models for plant growth predictions, as researchers increasingly favor mechanistic and computational approaches.
  2. Basic Phenotypic Analysis:
    Research centered solely on basic phenotypic analysis without integration into broader modeling frameworks appears to be waning, reflecting a shift towards more complex analyses that incorporate genetic and environmental data.
  3. Standalone Genomic Studies:
    The journal has published fewer standalone studies focusing exclusively on genomic data without a modeling component, indicating a trend towards integrating genomic insights with functional modeling.

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