Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has become one of the research priorities. In this paper, a new method, BIR-CNN, is proposed to classify of Android malware.

Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.

Background: Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted.

Results: In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information.

Novel Method of 3-Dimensional Graphical Representation for Proteins and Its Application.

In this article, we propose a 3-dimensional graphical representation of protein sequences based on 10 physicochemical properties of 20 amino acids and the BLOSUM62 matrix. It contains evolutionary information and provides intuitive visualization. To further analyze the similarity of proteins, we extract a specific vector from the graphical representation curve.

Disease Sequences High-Accuracy Alignment Based on the Precision Medicine.

High-accuracy alignment of sequences with disease information contributes to disease treatment and prevention. The results of multiple sequence alignment depend on the parameters of the objective function, including gap open penalties (GOP), gap extension penalties (GEP), and substitution matrix (SM). Firstly, the theory parameter formulas relating to GOP, GAP, and SM are inferred, combining unaligned sequence length, number, and identity.

OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids.

Hydroxylation of proline or lysine residues in proteins is a common post-translational modification event, and such modifications are found in many physiological and pathological processes. Nonetheless, the exact molecular mechanism of hydroxylation remains under investigation. Because experimental identification of hydroxylation is time-consuming and expensive, bioinformatics tools with high accuracy represent desirable alternatives for large-scale rapid identification of protein hydroxylation sites.

The graphical representation of protein sequences based on the physicochemical properties and its applications.

Based on the chaos game representation, a 2D graphical representation of protein sequences was introduced in which the 20 amino acids are rearranged in a cyclic order according to their physicochemical properties. The Euclidean distances between the corresponding amino acids from the 2-D graphical representations are computed to find matching (or conserved) fragments of amino acids between the two proteins. Again, the cumulative distance of the 2D-graphical representations is defined to compare the similarity of protein.

Similarity/dissimilarity studies of protein sequences based on a new 2D graphical representation.

A (two-dimensional) 2D graphical representation of protein sequences based on six physicochemical properties of amino acids is outlined. The numerical characterization of protein graphs is given as descriptors of protein sequences. It is not only useful for comparative study of proteins but also for encoding innate information about the structure of proteins.

Analysis of similarity/dissimilarity of protein sequences.

On the basis of a selected pair of physicochemical properties of amino acids, we introduce a dynamic 2D graphical representation of protein sequences. Then, we introduce and compare two numerical characterizations of protein graphs as descriptors to analyze the nine ND5 proteins. The approach is simple, convenient, and fast.

Analysis of similarity/dissimilarity of DNA sequences based on a class of 2D graphical representation.

On the basis of a class of 2D graphical representations of DNA sequences, sensitivity analysis has been performed, showing the high-capability of the proposed representations to take into account small modifications of the DNA sequences. And sensitivity analysis also indicates that the absolute differences of the leading eigenvalues of the L/L matrices associated with DNA increase with the increase of the number of the base mutations. Besides, we conclude that the similarity analysis method based on the correlation angles can better eliminate the effects of the lengths of DNA sequences if compared with the method using the Euclidean distances.

Realization of a super waveguide for high-power-density generation and transmission using right- and left-handed transmission-line circuits.

In an earlier work [Cheng and Cui, Phys. Rev. B 72, 113112 (2005)], we have shown theoretically that extremely high power densities can be generated and transmitted in a super waveguide which is filled with homogeneous bilayers of right- and left-handed materials.

A class of 2D graphical representations of RNA secondary structures and the analysis of similarity based on them.

Based on the concepts of cell and system of graphical representation, a class of 2D graphical representations of RNA secondary structures are given in terms of classifications of bases of nucleic acids. The representations can completely avoid loss of information associated with crossing and overlapping of the corresponding curve. As an application, we make quantitative comparisons for a set of RNA secondary structures at the 3'-terminus of different viruses based on the graphical representations.

Gray characteristic analysis of microtubules in cardiac myocytes.

Objective: To quantify the images of the microtubules in fetal rat cardiac myocytes under simulated microgravity by utilizing the characteristic parameters of image gray, and to study their morphological change.

Method: Gray characteristic of the microtubules in fetal rat cardiac myocytes was quantified in both simulated microgravity and control conditions by variance, skewness, and kurtosis.

Result: From feature analysis of 24 images, the characteristic parameters selected here were proved to be effective.

[Image analysis of cardiac muscle cytoskeleton under simulated microgravity based on gray-level co-occurrence matrix (GLCM)].

Objective: To study morphological changes of the cytoskeleton-microtubule (MT) of the fetal rat cardiac myocytes under simulated microgravity, and to quantify its image by utilizing the gray level co-occurrence matrix (GLCM) parameters of the image.

Method: Cytoskeleton images, including cellular microphotographs taken under normal or microgravity (clinostat) conditions, were quantified by gray level co-occurrence matrix parameters, and the pharmacological counter effect of quercetin against the influences of microgravity was estimated with these parameters.

Result: The results showed that the texture of microtubules in the image became worse under simulated microgravity environment.