Nanosized devices operating inside the human body open up new prospects in the healthcare domain. Invivo wireless nanosensor networks (iWNSNs) will result in a plethora of applications ranging from intrabody health-monitoring to drug-delivery systems.
Cell mechanics is a novel label-free biomarker for indicating cell states and pathological changes. The advent of atomic force microscopy (AFM) provides a powerful tool for quantifying the mechanical properties of single living cells in aqueous conditions.
In this paper, we analyze molecular communications (MCs) in a proposed artificial synapse (AS), whose main difference from biological synapses (BSs) is that it is closed, i.e., transmitter molecules cannot diffuse out from AS.
Bioinformatics and biomedicine research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences.
Complex networks are ubiquitous in nature. In biological systems, biomolecules interact with each other to form so-called biomolecular networks, which determine the cellular behaviors of living organisms.
In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, “feature-motion decorrelation” still remains as a challenge for the speckle tracking methods.
High dimensionality has become a typical feature of biomolecular data. In this paper, a novel dimension reduction method named p-norm singular value decomposition (PSVD) is proposed to seek the low-rank approximation matrix to the biomolecular data.
This paper reports a novel electrochemical method for detection of Glutathione (GSH) using Glutathione-S-Transferase (GST) – ZnO composite nanoparticles to investigate the prospects of the method for detection of cancer at an early stage.
Many recent efforts have been made for the development of machine learning based methods for fast and accurate phosphorylation site prediction. Currently, a majority of well-performing methods are based on hybrid information to build prediction models, such as evolutionary information, and disorder information, etc.
Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this work, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it