Interdisciplinary Sciences, Computational Life Sciences This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

An Algorithm for Gene Fragment Reconstruction
AbstractGene sequencing technology has been playing an important role in many aspects, such as life science, disease medicine and health medicine, particularly in the extremely tough process of fighting against 2019-novel coronavirus. Drawing DNA restriction map is a particularly important technology in genetic biology. The simplified partial digestion method (SPDP), a biological method, has been widely used to cut DNA molecules into DNA fragments and obtain the biological information of each fragment. In this work, we propose an algorithm based on 0 –1 planning for the location of restriction sites on a DNA molecule, wh...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 20, 2021 Category: Bioinformatics Source Type: research

Molecular Identification and Phylogenetic Analysis of the Traditional Chinese Medicinal Plant Kochia scoparia Using ITS2 Barcoding
In this study, 128 internal transcribed spacer 2 (ITS2) sequences were collected to distinguishK. scoparia from its closely related species and adulterants. Then, sequence alignment, sequence characteristics analysis, and genetic distance calculations were performed using MEGA 6.06 software, and the phylogenetic trees were reconstructed using both MEGA 6.06 and IQ-Tree software. Finally, the secondary structure of ITS2 was modeled using the prediction tool in the ITS2 database. The results showed that ITS2 sequences ofK. scoparia ranged in length from 226 to 227  bp, with a mean GC content of 55.3%. The maximum intraspeci...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 17, 2021 Category: Bioinformatics Source Type: research

DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images
This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN pro...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 15, 2021 Category: Bioinformatics Source Type: research

Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System
AbstractThe amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-obje...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 12, 2021 Category: Bioinformatics Source Type: research

Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection
AbstractThis work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time –frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for tr aining and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coeffi...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 10, 2021 Category: Bioinformatics Source Type: research

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort
AbstractCorona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doc...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 9, 2021 Category: Bioinformatics Source Type: research

Genomic sequence analysis of lung infections using artificial intelligence technique
AbstractAttributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services, various developments including Support Vector Machine (SVM), and profound learning. For example, Convolutional Neural systems (CNN) have prevalently engaged in a significant job of various classificational investigation in lung malignant growth, and different infections. In this paper, Parallel based SVM (P-SVM) and IoT has been utilized to examine the ideal order of lung infections caused by genomic sequence. The proposed method develops a new methodology to locate the ideal characterization of lung sicknesses and ...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 8, 2021 Category: Bioinformatics Source Type: research

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints
AbstractAn important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive models based on these representations. The predictive model based on features extracted by GCNNs can not only predict the mutagenicity of compounds but also identify the structure alerts in compounds. In fivefold cross...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 27, 2021 Category: Bioinformatics Source Type: research

Hantavirus: The Next Pandemic We Are Waiting For?
AbstractHantaviruses, albeit reported more than 40  years ago, are now considered emerging viruses’ because of their growing importance as human pathogens. Hantavirus created focal news when the paradoxical spread was reported during the world’s pandemic battle of the COVID-19, killing a man in Yunnan province of China, further jeopardizing the existing of the human race on the planet earth. In recent years an increasing number of infections and human-to-human transmission is creating a distressing situation. In this short communication, we have focused on the biology, pathogenesis, immunology, epidemiology and future...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 24, 2021 Category: Bioinformatics Source Type: research

Cardiac Severity Classification Using Pre Trained Neural Networks
This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN), by providing a pre-processed ECG signal as an excitation. The modified ECG signal is obtained through the combination of EMD (Empirical Mode Decomposition) and DWT (Discrete Wavelet Transform). In this proposed method, the input signal is first decomposed into the Intrinsic Mode Functions (IMF's) and the first three IMF's are combined to obtain a modified partially denoted ECG sample and then DWT is used to obtain an improved denoised signal. This pre-processed signal is classif...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 22, 2021 Category: Bioinformatics Source Type: research

Network Analysis Reveals Proteins Associated with Aortic Dilatation in Mucopolysaccharidoses
AbstractMucopolysaccharidoses are caused by a deficiency of enzymes involved in the degradation of glycosaminoglycans. Heart diseases are a significant cause of morbidity and mortality in MPS patients, even in conditions in which enzyme replacement therapy is available. In this sense, cardiovascular manifestations, such as heart hypertrophy, cardiac function reduction, increased left ventricular chamber, and aortic dilatation, are among the most frequent. However, the downstream events which influence the heart dilatation process are unclear. Here, we employed systems biology tools together with transcriptomic data to inve...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 21, 2021 Category: Bioinformatics Source Type: research

SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data
AbstractClustering is a common method to identify cell types in single cell analysis, but the increasing size of scRNA-seq datasets brings challenges to single cell clustering. Therefore, it is an urgent need to design a faster and more accurate clustering method for large-scale scRNA-seq data. In this paper, we proposed a new method for single cell clustering. First, a count matrix is constructed through normalization and gene filtration. Second, the raw data of gene expression matrix are projected to feature space constructed by secondary construction of feature space based on UMAP (Uniform Manifold Approximation and Pro...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 21, 2021 Category: Bioinformatics Source Type: research

Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision
In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 13, 2021 Category: Bioinformatics Source Type: research

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning
In this study, we wanted to contribute to the literature by proposing a novel protein-mapping method based on the AVL tree. The pro posed method was inspired by the fast search performance on the dictionary structure of AVL tree and was used to verify the protein interactions between SARS-COV-2 virus and human. First, protein sequences were mapped by both the proposed method and various protein-mapping methods. Then, the mapped protein sequences were normalized and classified by bidirectional recurrent neural networks. The performance of the proposed method was evaluated with accuracy, f1-score, precision, recall, and AUC ...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 12, 2021 Category: Bioinformatics Source Type: research

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions
AbstractAssessing pulmonary lesions using computed tomography (CT) images is of great significance to the severity diagnosis and treatment of coronavirus disease 2019 (COVID-19)-infected patients. Such assessment mainly depends on radiologists ’ subjective judgment, which is inefficient and presents difficulty for those with low levels of experience, especially in rural areas. This work focuses on developing a radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). We retrospectively analyzed 1160 COVID-19-infected pulmonary lesions from ...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 7, 2021 Category: Bioinformatics Source Type: research