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.

PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches
In this study, we provided a computational method, termed PredAPP (Prediction of Anti-Parasitic Peptides) that could effectively identify APPs using an ensemble of well-performed machine learning (ML) classifiers. Firstly, to solve the class imbalance problem, a balanced training dataset was generated by the undersampling method. We found that the balanced dataset based on cluster centroid achieved the best performance. Then, nine groups of features and six ML algorithms were combined to generate 54 classifiers and the output of these classifiers formed 54 feature representations, and in each feature group, we selected the...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 4, 2021 Category: Bioinformatics Source Type: research

OTNet: A CNN Method Based on Hierarchical Attention Maps for Grading Arteriosclerosis of Fundus Images with Small Samples
AbstractThe severity of fundus arteriosclerosis can be determined and divided into four grades according to fundus images. Automatically grading of the fundus arteriosclerosis is helpful in clinical practices, so this paper proposes a convolutional neural network (CNN) method based on hierarchical attention maps to solve the automatic grading problem. First, we use the retinal vessel segmentation model to separate the important vascular region and the non-vascular background region from the fundus image and obtain two attention maps. The two maps are regarded as inputs to construct a two-stream CNN (TSNet), to focus on fea...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 18, 2021 Category: Bioinformatics Source Type: research

Discovery of a Natural Product with Potent Efficacy Against SARS-CoV-2 by Drug Screening
This study demonstrated a drug screening for AVPs against SARS-CoV-2 and discovered a peptide with inspiring antiviral properties, which provided a promising strategy for the COVID-19 therapeutic approach.Graphic Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - September 12, 2021 Category: Bioinformatics Source Type: research

Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm
AbstractInferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the ...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 8, 2021 Category: Bioinformatics Source Type: research

Integrating Protein –Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer
This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal dr...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 7, 2021 Category: Bioinformatics Source Type: research

A Review of Parallel Implementations for the Smith –Waterman Algorithm
AbstractThe rapid advances in sequencing technology have led to an explosion of sequence data. Sequence alignment is the central and fundamental problem in many sequence analysis procedure, while local alignment is often the kernel of these algorithms. Usually, Smith –Waterman algorithm is used to find the best subsequence match between given sequences. However, the high time complexity makes the algorithm time-consuming. A lot of approaches have been developed to accelerate and parallelize it, such as vector-level parallelization, thread-level parallelization , process-level parallelization, and heterogeneous accelerati...
Source: Interdisciplinary Sciences, Computational Life Sciences - September 6, 2021 Category: Bioinformatics Source Type: research

Correction to: HD5 and LL ‑37 Inhibit SARS‑CoV and SARS‑CoV‑2 Binding to Human ACE2 by Molecular Simulation
(Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - September 3, 2021 Category: Bioinformatics Source Type: research

Biomarker Identification in Membranous Nephropathy Using a Long Non-coding RNA-Mediated Competitive Endogenous RNA Network
ConclusionOur study indicated dysregulation of immune- and apoptosis-associated functions and taste transduction and lysosome pathways may play important roles in MN progression. Deregulated ceRNAs, such as LINC00052-hsa-miR-145-5p-EPB41L5, LINC00052-hsa-miR-148a-3p-FAM43A and LINC00641-hsa-497-5p-PRKG1, may be associated with MN development. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - September 1, 2021 Category: Bioinformatics Source Type: research

A Hierarchical Error Correction Strategy for Text DNA Storage
AbstractDNA storage has been a thriving interdisciplinary research area because of its high density, low maintenance cost, and long durability for information storage. However, the complexity of errors in DNA sequences including substitutions, insertions and deletions hinders its application for massive data storage. Motivated by the divide-and-conquer algorithm, we propose a hierarchical error correction strategy for text DNA storage. The basic idea is to design robust codes for common characters which have one-base error correction ability including insertion and/or deletion. The errors are gradually corrected by the cod...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 31, 2021 Category: Bioinformatics Source Type: research

Discovery of Genetic Biomarkers for Alzheimer ’s Disease Using Adaptive Convolutional Neural Networks Ensemble and Genome-Wide Association Studies
ConclusionThis approach overcomes the limitations associated with the impact of subjective factors and dependence on prior knowledge while adaptively achieving more robust and effective candidate biomarkers in a data-driven way.SignificanceThe approach is promising to facilitate discovering effective candidate genetic biomarkers for brain disorders, as well as to help improve the effectiveness of identified candidate neuroimaging biomarkers for brain diseases. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - August 19, 2021 Category: Bioinformatics Source Type: research

Rhythmic Component Analysis Tool (RCAT): A Precise, Efficient and User-Friendly Tool for Circadian Clock Genes Analysis
AbstractHigh-throughput next-generation sequencing (NGS) technologies and real-time circadian dynamics reporting systems produce large amounts of experimental data on RNA and protein levels in the field of circadian rhythm and therefore require statistical knowledge and computational skills for quantitative analysis. Although there are many software applications that can process these data, they are often difficult to use and computationally inefficient. Hence, a convenient, user-friendly tool that can accurately acquire rhythmic components (period, amplitude, and phase) of circadian clock genes is necessary. Here, we deve...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 9, 2021 Category: Bioinformatics Source Type: research

Prediction of Potential MicroRNA –Disease Association Using Kernelized Bayesian Matrix Factorization
AbstractMicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18 –22 nucleotides in length and make up a type of non-coding RNA. Predicting disease-related microRNAs is crucial for understanding the pathogenesis of disease and for diagnosis, treatment, and prevention of diseases. Many computational techniques have been studied and developed, as the experimental techniques used to find novel miRNA–disease associations in biology are costly. In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miR...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 9, 2021 Category: Bioinformatics Source Type: research

HD5 and LL-37 Inhibit SARS-CoV and SARS-CoV-2 Binding to Human ACE2 by Molecular Simulation
AbstractThe coronavirus (COVID-19) pandemic is still spreading all over the world. As reported, angiotensin-converting enzyme-2 (ACE2) is a receptor of SARS-CoV-2 spike protein that initializes viral entry into host cells. Previously, the human defensin 5 (HD5) has been experimentally confirmed to be functional against the SARS-CoV-2. The present study proposes a human cathelicidin known as LL37 that strongly binds to the carboxypeptidase domain of human ACE2 compared to HD5. Therefore, LL37 bears a great potential to be tested as an anti-SARS-CoVD-2 peptide. We investigated the molecular interactions formed between the LL...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 7, 2021 Category: Bioinformatics Source Type: research

Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses
AbstractRecent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population ’s health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus’s o rigin, behavior, and structure, which might help produce/develop ...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 6, 2021 Category: Bioinformatics Source Type: research

Lung Cancer Detection and Improving Accuracy Using Linear Subspace Image Classification Algorithm
AbstractThe ability to identify lung cancer at an early stage is critical, because it can help patients live longer. However, predicting the affected area while diagnosing cancer is a huge challenge. An intelligent computer-aided diagnostic system can be utilized to detect and diagnose lung cancer by detecting the damaged region. The suggested Linear Subspace Image Classification Algorithm (LSICA) approach classifies images in a linear subspace. This methodology is used to accurately identify the damaged region, and it involves three steps: image enhancement, segmentation, and classification. The spatial image clustering t...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 5, 2021 Category: Bioinformatics Source Type: research