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.

Combining SVM and ECOC for Identification of Protein Complexes from Protein Protein Interaction Networks by Integrating Amino Acids ’ Physical Properties and Complex Topology
AbstractProtein Complexes plays important role in key functional processes in cells by forming Protein Protein Interaction (PPI) networks. Conventionally, they were determined through experimental approaches. For the sake of saving time and cost reduction, many computational methods have been proposed. Fewer computational approaches take into account significant biological information contained within protein amino acid sequence and identified dense sub graphs as complexes from PPI network by considering density and degree statistics. Biological information evaluate the common features for performing a particular biologica...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 20, 2020 Category: Bioinformatics Source Type: research

Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm
AbstractDue to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are i...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 20, 2020 Category: Bioinformatics Source Type: research

Finding Community Modules for Brain Networks Combined Uniform Design with Fruit Fly Optimization Algorithm
AbstractThere are a huge amount of neural units in brain networks. Some of the neural units have tight connection and form neural unit modules. These unit modules are helpful to the disease detection and target therapy. A good method can find neural unit modules accurately and effectively. The study proposes a new algorithm to analyze a brain network and obtain its neural unit modules. The proposed algorithm combines the uniform design and the fruit fly optimization algorithm (FOA); therefore, we called it as UFOA. It makes the utmost of their respective merits of the uniform design and the FOA, so as to acquire the feasib...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 17, 2020 Category: Bioinformatics Source Type: research

An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases
In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which based on Convolutional Neural Networks (CNN). The ADCN is inspired by the ideas of residual and dense networks and combines with the attention mechanism. We train and evaluate our proposed model on a publicly available red blood cells (RBC) dataset and compare ADCN with several well-established CNN models. Compared to other best performing CNN model in our experiments, ADCN shows superiority in all performance criteria such as accuracy (97.47% vs 94.61%), sensitivity (97.86% vs 95.20%) and specificity (97...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 10, 2020 Category: Bioinformatics Source Type: research

Identification of CpG Islands in DNA Sequences Using Short-Time Fourier Transform
AbstractIn the era of big data analysis, genomics data analysis is highly needed to extract the hidden information present in the DNA sequences. One of the important hidden features present in the DNA sequences is CpG islands. CpG Islands are important as these are used as gene markers and also these are associated with cancer etc. Therefore, various methods have been reported for the identification of CpG islands in DNA sequences. The key contributions of this work are (i) extraction of the periodicity feature associated with CpG islands using Short-time Fourier transform (ii) a short-time Fourier transform-based algorith...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 10, 2020 Category: Bioinformatics Source Type: research

Metatax: Metataxonomics with a Compi-Based Pipeline for Precision Medicine
AbstractThe human body immune system, metabolism and homeostasis are affected by microbes. Dysbiosis occurs when the homeostatic equilibrium is disrupted due to an alteration in the normal microbiota of the intestine. Dysbiosis can cause cancer, and also affect a patient ’s ability to respond to treatment. Metataxonomics seeks to identify the bacteria present in a biological sample, based on the sequencing of the 16S rRNA genetic marker. Precision medicine attempts to find relationships between the microbiota and the risk of acquiring cancer, and design new thera pies targeting bacteria. Flexible and portable bioinforma...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 29, 2020 Category: Bioinformatics Source Type: research

Gene Biomarkers Derived from Clinical Data of Hepatocellular Carcinoma
This study, therefore, suggests that the abnormal expression of those eight genes may be taken as gene biomarkers of HCC. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - April 14, 2020 Category: Bioinformatics Source Type: research

Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions
AbstractThe activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid a...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 29, 2020 Category: Bioinformatics Source Type: research

Towards Predicting Risk of Coronary Artery Disease from Semi-Structured Dataset
AbstractMany kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of information loss during mining such data. Mining medical data is challenging as predicting the non-accurate result is often not acceptable in this domain. In this paper, we have analyzed a partially annotated coronary artery disease (CAD) dataset which was originally in a semi-structured form. We have created a set of some well...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 18, 2020 Category: Bioinformatics Source Type: research

A Novel Index of Contact Frequency from Noise Protein –Protein Interaction Data Help for Accurate Interface Residue Pair Prediction
AbstractProtein –protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein–protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein–protein interaction interface r esidue pairs. Here, we extract the interface residue–residue contacts from the decoys in the ZDOCK protein–protein complex decoy set with RMSD mostly larger than 3 Å. To accurately compute the interface residue–residue contacts, we define a new constant called interface residue pairs frequen cy, which co...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 16, 2020 Category: Bioinformatics Source Type: research

iPseU-Layer: Identifying RNA Pseudouridine Sites Using Layered Ensemble Model
AbstractPseudouridine represents one of the most prevalent post-transcriptional RNA modifications. The identification of pseudouridine sites is an essential step toward understanding RNA functions, RNA structure stabilization, translation process, and RNA stability; however, high-throughput experimental techniques remain expensive and time-consuming in lab explorations and biochemical processes. Thus, how to develop an efficient pseudouridine site identification method based on machine learning is very important both in academic research and drug development. Motived by this, we present an effective layered ensemble model ...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 12, 2020 Category: Bioinformatics Source Type: research

Single-Cell Clustering Based on Shared Nearest Neighbor and Graph Partitioning
AbstractClustering of single-cell RNA sequencing (scRNA-seq) data enables discovering cell subtypes, which is helpful for understanding and analyzing the processes of diseases. Determining the weight of edges is an essential component in graph-based clustering methods. While several graph-based clustering algorithms for scRNA-seq data have been proposed, they are generally based on k-nearest neighbor (KNN) and shared nearest neighbor (SNN) without considering the structure information of graph. Here, to improve the clustering accuracy, we present a novel method for single-cell clustering, called structural shared nearest n...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 21, 2020 Category: Bioinformatics Source Type: research

An Integrated Systems Biology and Network-Based Approaches to Identify Novel Biomarkers in Breast Cancer Cell Lines Using Gene Expression Data
AbstractBreast cancer is the most common cause of death in women worldwide. Approximately 5% –10% of instances are attributed to mutations acquired from the parents. Therefore, it is highly recommended to design more potential drugs and drug targets to eradicate such complex diseases. Network-based gene expression profiling is a suggested tool for discovering drug targets by incorporating various factors such as disease states, intensities based on gene expression as well as protein–protein interactions. To find prospective biomarkers in breast cancer, we first identified differentially expressed genes (DEGs) statistic...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 12, 2020 Category: Bioinformatics Source Type: research

A Network Pharmacology-Based Study of the Molecular Mechanisms of Shaoyao-Gancao Decoction in Treating Parkinson ’s Disease
AbstractParkinson ’s disease (PD) is another major neurodegenerative disorder following Alzheimer’s disease, which not only seriously reduces the survival in patients, affecting patient’s quality of life, but also imposes a tremendous burden on families and even the whole society. It is urgent to find out effec tive drugs without side effects. The present study applied a creative approach called network pharmacology to explore the active compounds and therapeutic targets of Shaoyao-Gancao Decoction (SYGCD) for treating PD. We identified a total of 48 active compounds mediating 30 PD-related targets to exer t synergis...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 30, 2020 Category: Bioinformatics Source Type: research

Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer ’s Disease
This study aims to identify the potential therapeutic target genes and related pathways in PFC of AD. First, differential expression analyses were performed on transcriptome microarray of PFC between AD specimens and non-AD controls. Second, protein–protein interaction net works were constructed based on the identified differentially expressed genes to explore candidate therapeutic target genes. Finally, these candidate genes were validated through biological experiments. The enrichment analyses showed that the differentially expressed genes were significantly enriche d in protein functions and pathways related to AD. Fu...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 30, 2020 Category: Bioinformatics Source Type: research