SMART v1.0: A Database for Small Molecules with Functional Implications in Plants
AbstractWe developed SMART v1.0 (http://smart.omicstudio.cloud), the first database for small molecules with functional implications in plants. The SMART database is devoted to providing and managing small molecules and their associated structural data, chemoinformatic data, protein targets, pathways and induced phenotype/function information. Currently, SMART v1.0 encompasses 1218 unique small molecules which are involved in multiple biological pathways. SMART v1.0 is featured with user-friendly interfaces, through which pathway-centered visualization of small molecules can be efficiently performed, and multiple types of ...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 14, 2021 Category: Bioinformatics Source Type: research

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks
AbstractThe diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory net...
Source: Interdisciplinary Sciences, Computational Life Sciences - October 12, 2021 Category: Bioinformatics Source Type: research

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 accele...
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 a...
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/...
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

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images
AbstractBreast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast c...
Source: Interdisciplinary Sciences, Computational Life Sciences - August 2, 2021 Category: Bioinformatics Source Type: research

In Silico Mutagenesis-Based Remodelling of SARS-CoV-1 Peptide (ATLQAIAS) to Inhibit SARS-CoV-2: Structural-Dynamics and Free Energy Calculations
AbstractThe prolific spread of COVID-19 caused by a novel coronavirus (SARS-CoV-2) from its epicenter in Wuhan, China, to every nook and cranny of the world after December 2019, jeopardize the prevailing health system in the world and has raised serious concerns about human safety. Multi-directional efforts are made to design small molecule inhibitors, and vaccines and many other therapeutic options are practiced, but their final therapeutic potential is still to be tested. Using the old drug or vaccine or peptides could aid this process to avoid such long experimental procedures. Hence, here, we have repurposed a small pe...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 29, 2021 Category: Bioinformatics Source Type: research

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images
AbstractCoronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machin...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 27, 2021 Category: Bioinformatics Source Type: research

LncRNA-Encoded Short Peptides Identification Using Feature Subset Recombination and Ensemble Learning
This study can be extended to the research on SEPs from other species and have crucial implications for further findings and studies of functional genomics. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - July 25, 2021 Category: Bioinformatics Source Type: research

Target-Based In Silico Screening for Phytoactive Compounds Targeting SARS-CoV-2
AbstractCoronavirus disease 2019 (COVID-19), resulting from infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can cause severe and fatal pneumonia along with other life-threatening complications. The COVID-19 pandemic has taken a heavy toll on the healthcare system globally and has hit the economy hard in all affected countries. As a result, there is an unmet medical need for both the prevention and treatment of COVID-19 infection. Several herbal remedies have claimed to show promising clinical results, but the mechanisms of action are not clear. We set out to identify the anti-viral natural pr...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 25, 2021 Category: Bioinformatics Source Type: research

Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications
AbstractWith the proliferation of IoMT (Internet of Medical Things), billions of connected medical devices are constantly producing oceans of time series sensor data, dubbed as time series for short. Considering these time series reflect various functional states of the human body, how to effectively detect the corresponding abnormalities is of great significance for smart healthcare. Accordingly, we develop a horizontal visibility graph-based temporal classification model for disease diagnosis. We conduct extensive comparison experiments on the benchmark datasets to justify the superiority of our method in term of accurac...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 14, 2021 Category: Bioinformatics Source Type: research

RSCMDA: Prediction of Potential miRNA –Disease Associations Based on a Robust Similarity Constraint Learning Method
In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA –disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 10, 2021 Category: Bioinformatics Source Type: research

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN
ConclusionThe satisfactory performance of DDcCNN model reveals that these features and flexible computational methodologies can reinforce the existing prediction models for better prediction of protein solubility could be applied in several applications, such as to preselect initial targets that are soluble or to alter solubility of target proteins, thus can help to reduce the production cost. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - July 8, 2021 Category: Bioinformatics Source Type: research

Using Network Distance Analysis to Predict lncRNA –miRNA Interactions
In this study, we developed a network distance analysis model for lncRNA–miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similari ty networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network al gorithm...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 7, 2021 Category: Bioinformatics Source Type: research

Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis
AbstractIn traditional sequencing techniques, the different functions of cells and the different roles they play in differentiation are often ignored. With the advancement of single-cell RNA sequencing (scRNA-seq) techniques, scientists can measure the gene expression value at the single-cell level, and it is helping to understand the heterogeneity hidden in cells. One of the most powerful ways to find heterogeneity is using the unsupervised clustering method to get separate subpopulations. In this paper, we propose a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF)...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 6, 2021 Category: Bioinformatics Source Type: research

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease
AbstractThe disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer ’s disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi M edical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer...
Source: Interdisciplinary Sciences, Computational Life Sciences - July 5, 2021 Category: Bioinformatics Source Type: research

Predicting circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion
AbstractRecently, circRNAs with covalently closed loops have been discovered to play important parts in the progression of diseases. Nevertheless, the study of circRNA-disease associations is highly dependent on biological experiments, which are time-consuming and expensive. Hence, a computational approach to predict circRNA-disease associations is urgently needed. In this paper, we presented an approach that is based on deep matrix factorization with multi-source fusion (DMFMSF). In DMFMSF, several useful circRNA and disease similarities were selected and then combined by similarity kernel fusion. Then, linear and non-lin...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 29, 2021 Category: Bioinformatics Source Type: research

Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network
AbstractPredicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for  revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framewor k based on heterogeneous netw...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 25, 2021 Category: Bioinformatics Source Type: research

SBTD: A Novel Method for Detecting Topological Associated Domains from Hi-C Data
AbstractThe development of Hi-C technology has generated terabytes of chromatin interaction data, which bring possibilities for insight study of chromatin structure. Several studies revealed that mammalian chromosomes are folded into topological associated domains (TADs), which are conserved across cell types. Accurate detection of topological associated domains is now a vital process for revealing the relationship between the structure and function of genome organization. Unfortunately, the current TAD detection methods require massive computing resources, careful parameter adjustment and/or encounter inconsistent results...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 23, 2021 Category: Bioinformatics Source Type: research

Classification of Breast Cancer and Breast Neoplasm Scenarios Based on Machine Learning and Sequence Features from lncRNAs –miRNAs-Diseases Associations
AbstractThe influence of non-coding RNAs, such as lncRNAs (long non-coding RNAs) and miRNAs (microRNAs), is undeniable in several diseases, for example, in the formation of neoplasms and cancer scenarios. However, there are challenges due to the scarcity of validated datasets and the imbalance in the data. We found that the research of associations between miRNAs-lncRNAs and diseases is limited or done separately. In addition, those investigations, which use Machine Learning models joined with genomic sequence features extracted from miRNAs and lncRNAs, are few compared with using some methods such as genomic expression or...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 21, 2021 Category: Bioinformatics Source Type: research

Application of Immune Checkpoint Inhibitors in Solid Organ Transplantation Recipients: A Systematic Review
ConclusionsOur systematic review summarizes the use of ICIs in SOTRs and evaluates the efficacy of ICIs and the risk factors that induce HVGR. Through risk factor analysis, we found that mTOR inhibitors and calcineurin inhibitors may help to reduce the occurrence of HVGR;  hormones and anti-metabolic drugs may have adverse effects on the efficacy of ICIs. In addition, there is a contradictory relationship between the occurrence of HVGR and the efficacy of ICIs. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - June 21, 2021 Category: Bioinformatics Source Type: research

Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity
AbstractTransmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, name...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 18, 2021 Category: Bioinformatics Source Type: research

LPCANet: Classification of Laryngeal Cancer Histopathological Images Using a CNN with Position Attention and Channel Attention Mechanisms
AbstractLaryngeal cancer is one of the most common malignant tumors in otolaryngology, and histopathological image analysis is the gold standard for the diagnosis of laryngeal cancer. However, pathologists have high subjectivity in their diagnoses, which makes it easy to miss diagnoses and misdiagnose. In addition, according to a literature search, there is currently no computer-aided diagnosis (CAD) algorithm that has been applied to the classification of histopathological images of laryngeal cancer. Convolutional neural networks (CNNs) are widely used in various other cancer classification tasks. However, the potential g...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 17, 2021 Category: Bioinformatics Source Type: research

Molecular Dynamics Simulations Reveal the Modulated Mechanism of STING Conformation
AbstractStimulator of interferon genes (STING), which is an integral ER-membrane protein, could induce an antiviral state and boost antitumor immunity. Recent experiments reported that different small molecules could modulate the conformation of the STING. However, the mechanism of small molecules modulating the conformation of STING is still unknown. To illustrate the conformational modulated mechanism of STING by small molecules at atomic level, we investigated the interactions between STING and the small molecules: cGAMP and diABZI with molecular dynamics (MD) simulations method. Interestingly, we found that the residue...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 17, 2021 Category: Bioinformatics Source Type: research

HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data
AbstractIn recent years, clustering analysis of cancer genomics data has gained widespread attention. However, limited by the dimensions of the matrix, the traditional methods cannot fully mine the underlying geometric structure information in the data. Besides, noise and outliers inevitably exist in the data. To solve the above two problems, we come up with a new method which uses tensor to represent cancer omics data and applies hypergraph to save the geometric structure information in original data. This model is called hypergraph regularized tensor robust principal component analysis (HTRPCA). The data processed by HTR...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 11, 2021 Category: Bioinformatics Source Type: research

TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data
AbstractSingle-cell RNA-seq technology provides an unprecedented opportunity to allow researchers to study the biological heterogeneity during cell differentiation and development with higher resolution. Although many computational methods have been proposed to infer cell lineages from single-cell RNA-seq data, constructing accurate cell trajectories remains a challenge. We develop a novel trajectory inference method-based probability distribution (TIPD) to describe the heterogeneity of cell population. TIPD combines signalling entropy and clustering results of the gene expression profile to describe the probability distri...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 9, 2021 Category: Bioinformatics Source Type: research

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment
In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI. (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - June 9, 2021 Category: Bioinformatics Source Type: research

Preclinical Western Blot in the Era of Digital Transformation and Reproducible Research, an Eastern Perspective
AbstractThe current research is an interdisciplinary endeavor to develop a necessary tool in preclinical protein studies of diseases or disorders through western blotting. In the era of digital transformation and open access principles, an interactive cloud-based database called East –West Blot (https://rancs-lab.shinyapps.io/WesternBlots) is designed and developed. The online interactive subject-specific database built on the R shiny platform facilitates a systematic literature search on the specific subject matter, here set to western blot studies of protein regulation in the preclinical model of TBI. The tool summ...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 2, 2021 Category: Bioinformatics Source Type: research

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data
AbstractHigh-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell ’s heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called A daptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can ...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 2, 2021 Category: Bioinformatics Source Type: research

Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles
AbstractUnderstanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine‘Brain Region Connectivity’ information from published articles to build a centralized connectivity...
Source: Interdisciplinary Sciences, Computational Life Sciences - June 2, 2021 Category: Bioinformatics Source Type: research

Are the Allergic Reactions of COVID-19 Vaccines Caused by mRNA Constructs or Nanocarriers? Immunological Insights
AbstractThe Food and Drug Administration (FDA) has recently authorized the two messenger RNA (mRNA) vaccines BNT162b2 and mRNA-1273 for emergency use against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the COVID-19 coronavirus disease. BNT162b2 and mRNA-1273 vaccines were developed by Pfizer-BioNTech and Moderna, respectively, in 2020. The United Kingdom, Bahrain, Canada, Mexico, United States, Singapore, Oman, Saudi Arabia, Kuwait, and European Union began their vaccination programs with the BNT162b2 vaccine, while the United States and Canada also started the mRNA-1273 vaccination program in ...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 22, 2021 Category: Bioinformatics Source Type: research

On the Identification of Clinically Relevant Bacterial Amino Acid Changes at the Whole Genome Level Using Auto-PSS-Genome
AbstractThe identification of clinically relevant bacterial amino acid changes can be performed using different methods aimed at the identification of genes showing positively selected amino acid sites (PSS). Nevertheless, such analyses are time consuming, and the frequency of genes showing evidence for PSS can be low. Therefore, the development of a pipeline that allows the quick and efficient identification of the set of genes that show PSS is of interest. Here, we present Auto-PSS-Genome, a Compi-based pipeline distributed as a Docker image, that automates the process of identifying genes that show PSS using three diffe...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 19, 2021 Category: Bioinformatics Source Type: research

The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria
This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA–target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, an d Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and g ene expression datasets follow scale-free topology. Th...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 18, 2021 Category: Bioinformatics Source Type: research

TUPDB: Target-Unrelated Peptide Data Bank
AbstractThe isolation of target-unrelated peptides (TUPs) through biopanning remains as a major problem of phage display selection experiments. These TUPs do not have any actual affinity toward targets of interest, which tend to be mistakenly identified as target-binding peptides. Therefore, an information portal for storing TUP data is urgently needed. Here, we present a TUP data bank (TUPDB), which is a comprehensive, manually curated database of approximately 73 experimentally verified TUPs and 1963 potential TUPs collected from TUPScan, the BDB database, and public research articles. The TUPScan tool has been integrate...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 16, 2021 Category: Bioinformatics Source Type: research

Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction
AbstractCardiovascular Disease (CVD) is one among the main factors for the increase in mortality rate worldwide. The analysis and prediction of this disease is yet a highly formidable task in medical data analysis. Recent advancements in technology such as Big Data, Artificial Intelligence and the need for automated models have paved the way for developing a more reliable and efficient model for predicting heart disease. Several researches have been carried out in predicting heart diseases but the focus on choosing the important attributes that play a significant role in predicting CVD is inadequate. Hence the choice of ri...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 14, 2021 Category: Bioinformatics Source Type: research

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 ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 27, 2021 Category: Bioinformatics Source Type: research