Classification of DNA damages on segmented comet assay images using convolutional neural network.

BACKGROUND AND OBJECTIVE: Identification and quantification of DNA damage is a very significant subject in biomedical research area which still needs more robust and effective methods. One of the cheapest, easy to use and most successful method for DNA damage analyses is comet assay. In this study, performance of Convolutional Neural Network was examined on quantification of DNA damage using comet assay images and was compared to other methods in the literature. METHODS: 796 single comet grayscale images with 170 x 170 resolution labeled by an expert and classified into 4 classes each having approximately 200 samples as G0 (healthy), G1 (poorly defective), G2 (defective) and G3 (very defective) were utilized. 120 samples were used as test dataset and the rest were used in data augmentation process to achieve better performance with training of Convolutional Neural Network. The augmented data having a total of 9995 images belonging to four classes were used as network training data set. RESULTS: The proposed model, which was not dependent to pre-processing parameters of image processing for DNA damage classification, was able to classify comet images into 4 classes with an overall accuracy rate of 96.1%. CONCLUSIONS: This paper primarily focuses on features and usage of Convolutional Neural Network as a novel method to classify comet objects on segmented comet assay images.

A novel method to identify and grade DNA damage on comet images.

BACKGROUND AND OBJECTIVE: In recent years, development of software programs in medicine field has proceeded in a rapid manner. Comet assay is one of the research methods in medicine field that displays whether DNA has damage. In this study, it is aimed to share experience of dynamic time warping method and decision tree to decide whether DNA has damage and grade DNA damage by means of the software program. METHODS: The application analyzes manually extracted RGB single comet images whose centers are manually marked. The application performs pixel profile analysis at the directions of vertical and horizontal based on the center of comet. The bidirectional pixel profile results are used as inputs of dynamic time warping. Four novel and one conventional measurement parameters some of which are calculated by dynamic time warping are given to decision tree. RESULTS: The decision tree identifies whether DNA has damage and grades DNA damage categorizing four damage levels with accuracy of 99.03% using only two of five measurement parameters. CONCLUSIONS: This paper mainly focuses on features and usage of dynamic time warping and decision tree as a novel method to identify and grade DNA damage on comet images.

Uncovering the differences in linguistic network dynamics of book and social media texts.

Complex network studies span a large variety of applications including linguistic networks. To investigate the differences in book and social media texts in terms of linguistic typology, we constructed both sequential and sentence collocation networks of book, Facebook and Twitter texts with undirected and weighted edges. The comparisons are performed using the basic parameters like average degree, modularity, average clustering coefficient, average path length, diameter, average link weight etc. We also presented the distribution graphs for node degrees, edge weights and maximum degree differences of the pairing nodes. The degree difference occurrences are furtherly detailed with the grayscale percentile plots with respect to the edge weights. We linked the network analysis with linguistic aspects like word and sentence length distributions. We concluded that linguistic typology demonstrates a formal usage in book that slightly deviates to informal in Twitter. Facebook interpolates between these media by the means of network parameters, while the informality of Twitter is mostly influenced by the character limitations.