This study investigates the feasibility of applying Gene Ontology (GO)-derived semantic similarity methods to the biological pathway analysis. The results derived from the analysis of human metabolic and regulatory pathways are consistent with the network biology. It suggests that the semantic similarity measurement may be used to help the pathway modeling.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560635PMC

Publication Analysis

Top Keywords

semantic similarity
12
metabolic regulatory
8
regulatory pathways
8
analysis metabolic
4
pathways gene
4
gene ontology-derived
4
ontology-derived semantic
4
similarity measures
4
measures study
4
study investigates
4

Similar Publications

This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection.

View Article and Find Full Text PDF

Neural dynamics of social verb processing: An MEG study.

Soc Cogn Affect Neurosci

December 2024

Cognitive Neuroscience Center (CNC), University of San Andres, Buenos Aires, C1011ACC, Argentina.

Human vocabularies include specific words to communicate interpersonal behaviors, a core linguistic function mainly afforded by social verbs (SVs). This skill has been proposed to engage dedicated systems subserving social knowledge. Yet, neurocognitive evidence is scarce, and no study has examined spectro-temporal and spatial signatures of SV access.

View Article and Find Full Text PDF

Modelling neural probabilistic computation using vector symbolic architectures.

Cogn Neurodyn

December 2024

Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave., Waterloo, ON N2L 3G1 Canada.

Distributed vector representations are a key bridging point between connectionist and symbolic representations in cognition. It is unclear how uncertainty should be modelled in systems using such representations. In this paper we discuss how bundles of symbols in certain Vector Symbolic Architectures (VSAs) can be understood as defining an object that has a relationship to a probability distribution, and how statements in VSAs can be understood as being analogous to probabilistic statements.

View Article and Find Full Text PDF

Introduction: The assessment of the severity of fruit disease is crucial for the optimization of fruit production. By quantifying the percentage of leaf disease, an effective approach to determining the severity of the disease is available. However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.

View Article and Find Full Text PDF

A predictive language model for SARS-CoV-2 evolution.

Signal Transduct Target Ther

December 2024

School of Basic Medical Science, Tsinghua University, 30 Shuangqing Rd., Haidian District, Beijing, 100084, China.

Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!