‘Manually Curated Database of Rice Proteins’ (MCDRP) available at http://www.genomeindia.org/biocuration is a unique curated database based on published experimental data. Semantic integration of scientific data is essential to gain a higher level of understanding of biological systems. Since the majority of scientific data is available as published literature, text mining is an essential step before the data can be integrated and made available for computer-based search in various databases. However, text mining is a tedious exercise and thus, there is a large gap in the data available in curated databases and published literature. Moreover, data in an experiment can be perceived from several perspectives, which may not reflect in the text-based curation. In order to address such issues, we have demonstrated the feasibility of digitizing the experimental data itself by creating a database on rice proteins based on in-house developed data curation models. Using these models data of individual experiments have been digitized with the help of universal ontologies. Currently, the database has data for over 1800 rice proteins curated from >4000 different experiments of over 400 research articles. Since every aspect of the experiment such as gene name, plant type, tissue and developmental stage has been digitized, experimental data can be rapidly accessed and integrated.

The divergence of natural stress tolerance mechanisms between species is an intriguing phenomenon. To study it in rice, a comparative transcriptome analysis was carried out in 'heading' stage tissue (flag leaf, panicles and roots) of Nagina 22 (N22; drought-tolerant) and IR64 (drought-sensitive) plants subjected to field drought.Interestingly, N22 showed almost double the number of differentially expressed genes (DEGs) than IR64. Many DEGs colocalized within drought-related QTLs responsible for grain yield and drought tolerance and also associated with drought tolerance and critical drought-related plant traits such as leaf rolling, trehalose content, sucrose and cellulose content. Besides, co-expression analysis of the DEGs revealed several 'hub' genes known to actively regulate drought stress response.Strikingly, 1366 DEGs, including 21 'hub' genes, showed a distinct opposite regulation in the two rice varieties under similar drought conditions. Annotation of these variety-specific DEGs (VS-DEGs) revealed that they are distributed in various biological pathways. Furthermore, 103 VS-DEGs were found to physically interact with over 1300 genes, including 32 that physically interact with other VS-DEGs as well. The promoter region of these genes has sequence variations among the two rice varieties, which might be in part responsible for their unique expression pattern.

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Background Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. Objective This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. Methods The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. Results We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

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