Related papers: Predicting the DNA Conductance using Deep Feed For…
Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resource-constrained mobile devices often requires the help from edge servers…
We present a short review of various experiments that measure charge transfer and charge transport in DNA. Some general comments are made on the possible connection between 'chemistry-style' charge transfer experiments that probe…
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and…
Most of the anticancer drugs bind to double-stranded DNA (dsDNA) by intercalative-binding mode. Although experimental studies have become available recently, a molecular-level understanding of the interactions between the drug and dsDNA…
We propose an aqueous functionalized molybdenum disulfide nanoribbon suspended over a solid electrode as the first capacitive displacement sensor aimed at determining the DNA sequence. The detectable sequencing events arise from the…
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between…
Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and…
We report a novel simulation strategy that enables us to identify key parameters controlling the experimentally measurable characteristics of structural protein tags on dsDNA construct translocating through a double nanopore setup. First,…
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in…
Training of deep neural networks (DNNs) frequently involves optimizing several millions or even billions of parameters. Even with modern computing architectures, the computational expense of DNN training can inhibit, for instance, network…
In recent years significant attention has been attracted to proposals which utilize DNA for nanotechnological applications. Potential applications of these ideas range from the programmable self-assembly of colloidal crystals, to biosensors…
A mechanism of double strand breaking (DSB) in DNA due to the action of two electrons is considered. These are the electrons produced in the vicinity of DNA molecules due to ionization of water molecules with a consecutive emission of two…
Cryptography is the science that secures data and communication over the network by applying mathematics and logic to design strong encryption methods. In the modern era of e-business and e-commerce the protection of confidentiality,…
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it…
We propose a simple nonlinear scaler displacement model to calculate the distribution of effect created by a shear stress on a double stranded DNA (dsDNA) molecule and the value of shear force $F_c$ which is required to separate the two…
Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate…
A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…
In this work we investigate the electronic transport along model DNA molecules using an effective tight-binding approach that includes the backbone on site energies. The localization length and participation number are examined as a…
This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localisation of multiple sources in reverberant environments. DNNs are used to learn the relationship…
We review recent advances in the DNA sequencing based on the measurement of transverse electrical currents. Device configurations proposed in the literature are classified according to whether the molecular fingerprints appear as the major…