Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present Taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the TaBooN work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. TaBooN is an automated method for Boolean Network inference from.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TCBB.2021.3063817 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A NiAl-layered double hydroxides memristor with artificial synapse function and its Boolean logic applications.
J Chem Phys
January 2025
Department of Physics, College of Science, Qiqihar University, Qiqihar 161006, China.
In the era of artificial intelligence, there has been a rise in novel computing methods due to the increased demand for rapid and effective data processing. It is of great significance to develop memristor devices capable of emulating the computational neural network of the brain, especially in the realm of artificial intelligence applications. In this work, a memristor based on NiAl-layered double hydroxides is presented with excellent electrical performance, including analog resistive conversion characteristics and the effect of multi-level conductivity modulation.
View Article and Find Full Text PDFBiocomputing at the crossroad between emulating artificial intelligence and cellular supremacy.
Curr Opin Biotechnol
January 2025
Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Medicine and School of Life Sciences, Westlake University, 310024 Hangzhou, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, 310024 Hangzhou, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 310024 Hangzhou, Zhejiang, China; School of Engineering, Westlake University, 310030 Hangzhou, Zhejiang, China. Electronic address:
Biocomputation aims to create sophisticated biological systems capable of addressing important problems in (bio)medicine with a machine-like precision. At present, computational gene networks engineered by single- or multi-layered assembly of DNA-, RNA- and protein-level gene switches have allowed bacterial or mammalian cells to perform various regulation logics of interest, including Boolean calculation or neural network-like computing. This review highlights the molecular building blocks, design principles, and computational tasks demonstrated by current biocomputers, before briefly discussing possible fields where biological computers may ultimately outcompete their electronic counterparts and achieve cellular supremacy.
View Article and Find Full Text PDFPrediction of Preeclampsia Using Machine Learning: A Systematic Review.
Cureus
December 2024
Obstetrics and Gynecology, ESI Hospital and Postgraduate Institute of Medical Sciences and Research (PGIMER) Basaidarapur, New Delhi, IND.
Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis.
View Article and Find Full Text PDFMapping a Decade of Research: Bibliometric Analysis of Iron Overload in Chronic Kidney Disease.
Cureus
December 2024
Department of Hematology, Hamad Medical Corporation, Doha, QAT.
This study conducts a bibliometric analysis (BA) to map the research landscape surrounding chronic kidney disease (CKD) and iron overload over the past decade. Utilizing PubMed as the primary database, a systematic search strategy was developed using BA guidelines, incorporating keyword and MeSH term refinements for comprehensive data retrieval. A Boolean operator-based search strategy was applied, capturing literature from 2014 to the first quarter of 2024, with inclusion criteria focusing on articles and review articles published in English.
View Article and Find Full Text PDFDeep neural networks have an inbuilt Occam's razor.
Nat Commun
January 2025
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK.
The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components for supervised learning, we apply a Bayesian picture based on the functions expressed by a DNN. The prior over functions is determined by the network architecture, which we vary by exploiting a transition between ordered and chaotic regimes.
View Article and Find Full Text PDFWant 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!