Interdisciplinary Sciences, Computational Life Sciences 
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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.Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides
AbstractThe rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary structure, and function. However, despite recent advancements in rapid computational methods for AMP prediction from various mammalian, aquatic, and terrestrial species, there is limited information regarding their pres...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 12, 2021 Category: Bioinformatics Source Type: research
A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein –Protein Interaction Networks (PPINs)
In this study, we have compu ted a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Borut a feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIP...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 6, 2021 Category: Bioinformatics Source Type: research
A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases
AbstractOne important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein –protein interaction network and use them for inferring disease–disease associations. In this article, we propose a new framework including protein–protein networks, disease–gene associations and disease–complex pairs to cluster protein complexes and infer ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 27, 2021 Category: Bioinformatics Source Type: research
Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA –lncRNA Interaction Prediction
AbstractMicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their interactions play important roles in biological processes. Computational methods, such as machine learning and various bioinformatics tools, can predict potential miRNA –lncRNA interactions, which is significant for studying their mechanisms and biological functions. A growing number of RNA interaction predictors for animal have been reported, but they are unreliable for plant due to the differences of ncRNAs in animal and plant. It is urgent to build a reliable plant predictor, especially for cross-species. This pap...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 26, 2021 Category: Bioinformatics Source Type: research
COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review
AbstractThe recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, pre...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 22, 2021 Category: Bioinformatics Source Type: research
Computational Methods and Online Resources for Identification of piRNA-Related Molecules
AbstractpiRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 22, 2021 Category: Bioinformatics Source Type: research
i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites
AbstractDNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and lab orious. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one han...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 8, 2021 Category: Bioinformatics Source Type: research
Computer-Aided Diagnosis System for Alzheimer ’s Disease Using Positron Emission Tomography Images
AbstractAlzheimer ’s disease (AD) is a kind of neurological brain disease. It is an irretrievable, neurodegenerative brain disorder. There are no pills or drugs to cure AD. Therefore, an early diagnosis may help the physician to make accurate analysis and to provide better treatment. With the advent of computationa l intelligence techniques, machine learning models have made tremendous progress in brain images analysis using MRI, SPECT and PEI. However, accurate analysis of brain scans is an extremely challenging task. The main focus of this paper is to design a Computer Aided Diagnosis (CAD) system using Long -Term Shor...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 3, 2021 Category: Bioinformatics Source Type: research
CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information
AbstractEssential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining the features of the high throughput data. Appropriate integration of differential biological information based on protein –protein interaction (PPI) network has been proven useful in predicting essential proteins. The m...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 27, 2021 Category: Bioinformatics Source Type: research
CorGO: An Integrated Method for Clustering Functionally Similar Genes
In this study, an algorithm named CorGO is introduced, that specifically deals with the identification of functionally similar gene-clusters. Two types of relationships are calculated for this purpose. Firstly, the Correlation (Cor) between the genes are captured from the gene-expression data, which helps in deciphering the relationship between genes based on its expression across several diseased samples. Secondly, Gene Ontology (GO)-based semantic similarity information available for the genes is utilized, that helps in adding up biological relevance to the identified gene-clusters. A similarity measure is defined by int...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 24, 2021 Category: Bioinformatics Source Type: research
Mixed Distribution Models Based on Single-Cell RNA Sequencing Data
AbstractProgress in single-cell RNA sequencing (scRNA-seq) has yielded a lot of valuable data. Analysis of these data can provide a new perspective for studying the intratumoral heterogeneity and identifying gene markers. In this paper, the scRNA-seq data of colorectal cancer (CRC) are analyzed, and it is found that the shape of the gene expression difference (GED) data shows certain distribution regularity. To study the distribution regularity, mixed stable-normal distribution (MSND) model and mixed stable-exponential distribution (MSED) model are constructed to fit the GED data. And the estimated parameters of MSND and M...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 22, 2021 Category: Bioinformatics Source Type: research
Extracting Biomedical Entity Relations using Biological Interaction Knowledge
AbstractDiscovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures....
Source: Interdisciplinary Sciences, Computational Life Sciences - March 17, 2021 Category: Bioinformatics Source Type: research
Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach
ConclusionThe satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - March 6, 2021 Category: Bioinformatics Source Type: research
Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning
In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, p...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 27, 2021 Category: Bioinformatics Source Type: research
An In Silico Method for Predicting Drug Synergy Based on Multitask Learning
AbstractTo make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein –protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug–target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 21, 2021 Category: Bioinformatics Source Type: research
