No abstract

In mammals, several studies have suggested that levels of methylation are higher in repetitive DNA than in nonrepetitive DNA, possibly reflecting a genome-wide defense mechanism against deleterious effects associated with transposable elements (TEs). To analyze the determinants of methylation patterns in primate repetitive DNA, we took advantage of the fact that the methylation rate in the germ line is reflected by the transition rate at CpG sites. We assessed the variability of CpG substitution rates in nonrepetitive DNA and in various TE and retropseudogene families. We show that, unlike other substitution rates, the rate of transition at CpG sites is significantly (37%) higher in repetitive DNA than in nonrepetitive DNA. Moreover, this rate of CpG transition varies according to the number of repeats, their length, and their level of divergence from the ancestral sequence (up to 2.7 times higher in long, lowly divergent TEs compared with unique sequences). This observation strongly suggests the existence of a homology-dependent methylation (HDM) mechanism in mammalian genomes. We propose that HDM is a direct consequence of interfering RNA-induced transcriptional gene silencing.RNA interference ͉ transposable element ͉ substitution rate ͉ CpG T ransposable element (TE) activity within genomes may have numerous deleterious consequences, such as their insertions into genes or regulatory elements, or genomic disorders resulting from ectopic recombination between homologous TE copies (1). Thus, specific mechanisms that limit such deleterious effects within genomes are expected to have arisen. Indeed, Selker et al.(2) discovered in Neurospora crassa a defense mechanism against TEs associated with DNA methylation [namely, RIP (Repeat Induced Point mutations)]. DNA methylation in the genome of N. crassa is exclusively confined to repeated sequences (3, 4) and causes their inactivation in no more than one generation (reviewed in ref. 3). Although this mechanism is not entirely understood, DNA methylation is triggered by the existence of two or more homologous DNA sequences longer than a few hundred base pairs (3). The immediate inactivation of methylated sequences in N. crassa is a result of massive C-to-T mutational events due to the deamination of methylated cytosines into thymines, which is likely to be induced by the methylase itself (5, 6). Another example indicating that DNA methylation controls the potential deleterious effects of TEs comes from plants: Only TEs exhibit high methylation levels, which prevents their expression (7,8). In mammals, several lines of experimental and theoretical evidence suggest the existence of a specific methylation pattern in TEs (9-14). For instance, TEs in Mus musculus retain their methylated state during early embryonic stages, whereas nonrepetitive DNA becomes nonmethylated (14). In the same species, Yates et al. (12) demonstrated that tandem B1 elements can induce strong de novo DNA methylation. In humans, Chesnokov and Schmid (11) discovered that Alu elements in the germ lin...

Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

No abstract

Abstract. Processed pseudogenes are generated by reverse transcription of a functional gene. They are generally nonfunctional after their insertion and, as a consequence, are no longer subjected to the selective constraints associated with functional genes. Because of this property they can be used as neutral markers in molecular evolution. In this work, we investigated the relationship between the evolution of GC content in recently inserted processed pseudogenes and the local recombination pattern in two mammalian genomes (human and mouse). We confirmed, using original markers, that recombination drives GC content in the human genome and we demonstrated that this is also true for the mouse genome despite lower recombination rates. Finally, we discussed the consequences on isochores evolution and the contrast between the human and the mouse pattern.

Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.

Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F$$_{1}$$ 1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of $$88\%\pm 5\%$$ 88 % ± 5 % , 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.