Dataset Information

Draw me Science: Multi-level and multi-scale reconstruction of knowledge dynamics with phylomemies.

ABSTRACT: In 1751, Jean le Rond d'Alembert had a dream: "to make a genealogical or encyclopedic tree which will gather the various branches of knowledge together under a single point of view and will serve to indicate their origin and their relationships to one another". In this paper, we address the question identifying the branches of science by taking advantage of the massive digitization of scientific production. In the framework of complex systems studies, we first formalize the notion of level and scale of knowledge dynamics. Then, we demonstrate how we can reconstruct a reasonably precise and concise multi-scale and multi-level approximation of the dynamical structures of Science: phylomemies. We introduce the notion of phylomemetic networks-projections of phylomemies in low dimensional spaces that can be grasped by the human mind-and propose a new algorithm to reconstruct both phylomemies and the associated phylomemetic networks. This algorithm offers, passing, a new temporal clustering on evolving semantic networks. Last, we show how phylomemy reconstruction can take into account users' preferences within the framework of embodied cognition, thus defining a third way between the quest for objective "ground truth" and the ad-hoc adaptation to a particular user's preferences. The robustness of this approach is illustrated by several case studies.

Supplementary information

The online version contains supplementary material available at 10.1007/s11192-021-04186-5.

SUBMITTER: Chavalarias D

PROVIDER: S-EPMC8606253 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis.ResultsThe multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration.ConclusionsThe multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.

Project description:Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.

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Draw me Science: Multi-level and multi-scale reconstruction of knowledge dynamics with phylomemies.

Supplementary information

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