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.

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 sup...
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 gorithms, a...
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
In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.Graphic abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
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 network ...
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