J Theor Biol
Department of Computer Sciences, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
Published: May 2012
RNA-RNA interaction is used in many biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In this regard, some algorithms have been formed to predict the structure of the interaction between two RNA molecules. One common pitfall in the most algorithms is their high computational time. In this paper, we introduce a novel algorithm called TIRNA to accurately predict the secondary structure between two RNA molecules based on minimum free energy (MFE). The algorithm is stand on a heuristic approach which employs some dot matrices for finding the secondary structure of each RNA and between two RNAs. The proposed algorithm has been performed on some standard datasets such as CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS and IncRNA₅₄-RepZ in the Escherichia coli bacteria. The time and space complexity of the algorithm are 0(k² log k²) and 0(k²), respectively, where k indicates the sum of the length of two RNAs. The experimental results show the high validity and efficiency of the TIRNA.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jtbi.2012.01.025 | DOI Listing |
Curr Opin Crit Care
January 2025
Department of Critical Care Medicine.
Purpose Of Review: Neuroprognostication after acute brain injury (ABI) is complex. In this review, we examine the threats to accurate neuroprognostication, discuss strategies to mitigate the self-fulfilling prophecy, and how to approach the indeterminate prognosis.
Recent Findings: The goal of neuroprognostication is to provide a timely and accurate prediction of a patient's neurologic outcome so treatment can proceed in accordance with a patient's values and preferences.
Sensors (Basel)
December 2024
School of Cyber Science and Engineering, Liaoning University, Shenyang 110036, China.
Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Virginia Commonwealth University, Richmond, VA, United States.
Health care is undergoing a "revolution," where patients are becoming consumers and armed with apps, consumer review scores, and, in some countries, high out-of-pocket costs. Although economic analyses and health technology assessment (HTA) have come a long way in their evaluation of the clinical, economic, ethical, legal, and societal perspectives that may be impacted by new technologies and procedures, these approaches do not reflect underlying patient preferences that may be important in the assessment of "value" in the current value-based health care transition. The major challenges that come with the transformation to a value-based health care system lead to questions such as "How are economic analyses, often the basis for policy and reimbursement decisions, going to switch from a societal to an individual perspective?" and "How do we then assess (economic) value, considering individual preference heterogeneity, as well as varying heuristics and decision rules?" These challenges, related to including the individual perspective in cost-effectiveness analysis (CEA), have been widely debated.
View Article and Find Full Text PDFNat Commun
January 2025
Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end models that avoid explicit domain knowledge. These models assume no coverage bias in training and evaluation data, meaning the data are representative of the true distribution.
View Article and Find Full Text PDFPhys Life Rev
January 2025
Institute of Intelligent Systems and Robotics, CNRS, Sorbonne University, Paris, France.
The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!
© LitMetric 2025. All rights reserved.