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.

Consistent Clustering Pattern of Prokaryotic Genes Based on Base Frequency at the Second Codon Position and its Association with Functional Category Preference
AbstractIn 2002, our research group observed a gene clustering pattern based on the base frequency of A versus T at the second codon position in the genome ofVibrio cholera and found that the functional category distribution of genes in the two clusters was different. With the availability of a large number of sequenced genomes, we performed a systematic investigation of A2–T2 distribution and found that 2694 out of 2764 prokaryotic genomes have an optimal clustering number of two, indicating a consistent pattern. Analysis of the functional categories of the coding genes in each cluster in 1483 prokaryotic genomes indica...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 21, 2022 Category: Bioinformatics Source Type: research

A Visual Phenotype-Based Differential Diagnosis Process for Rare Diseases
ConclusionCompared to genetic and molecular analysis, phenotype-based diagnosis is faster, cheaper, and easier. The differential diagnosis process we designed can optimize the phenotype information of patients and better locate the target disease. It can also help to make screening decisions before genetic testing.Graphical Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - May 21, 2022 Category: Bioinformatics Source Type: research

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data
AbstractWith the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF (https://github.com/cma2015/easyMF), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibili...
Source: Interdisciplinary Sciences, Computational Life Sciences - May 18, 2022 Category: Bioinformatics Source Type: research

Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge
ConclusionIn conclusion, our computational framework of FLMTS for improving the prediction of potential kinase inhibitors successfully aggregates feature information from multisource knowledge, yielding better prediction performance than existing state-of-the-art methods.Graphical Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - May 10, 2022 Category: Bioinformatics Source Type: research

PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest
In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.Graphical abs...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 30, 2022 Category: Bioinformatics Source Type: research

U-shaped Retinal Vessel Segmentation Based on Adaptive Aggregation of Feature Information
AbstractDetection and analysis of retinal blood vessels contribute to the clinical diagnosis of many ophthalmic diseases. In this paper, aiming on achieving more accurate segmentation of retinal vessels and enhance the ability of the algorithm to identify microvessels, we propose a U-shaped network based on adaptive aggregation of feature information. The introduced feature selection module, which could strengthen feature transmission and selectively emphasize feature information. To effectively capture the characteristics of vessels at different scales, generate richer and denser context information, and DenseASPP is embe...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 29, 2022 Category: Bioinformatics Source Type: research

Gene Selection in a Single Cell Gene Space Based on D –S Evidence Theory
AbstractIf the samples, features and information values in a real-valued information system are cells, genes and gene expression values, respectively, then for convenience, this system is said to be a single cell gene space. In the era of big data, people are faced with high dimensional gene expression data with redundancy and noise causing its strong uncertainty. D –S evidence theory excels at tackling the problem of uncertainty, and its conditions to be met are weaker than Bayesian probability theory. Therefore, this paper studies the gene selection in a single cell gene space to remove noise and redundancy with D–S ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 28, 2022 Category: Bioinformatics Source Type: research

Feature Generalization for Breast Cancer Detection in Histopathological Images
AbstractRecent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient a...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 28, 2022 Category: Bioinformatics Source Type: research

Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit
AbstractHypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. The...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 27, 2022 Category: Bioinformatics Source Type: research

Pharmacogenomic Cluster Analysis of Lung Cancer Cell Lines Provides Insights into Preclinical Model Selection in NSCLC
In this study, we assessed the drug response consistency between lung cell lines and NSCLC tumors in The Cancer Genome Atlas by hierarchical clustering using copy number variations in driver genes, and profiled the molecular patterns and correlations in cell lines. We found that some frequently used cell lines of NSCLC subtypes were not clustered with their matched subtypes of tumor. Mutation profiles in the oxidative stress response and squamous differentiation pathway in lung cell lines were in concordance with lung squamous cell carcinoma. Furthermore, lung cell lines and tumors in the same sub-cluster had very similar ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 27, 2022 Category: Bioinformatics Source Type: research

Development of Electrochemical Biosensor for miR204-Based Cancer Diagnosis
We report an electrochemical biosensor for quantification of miRNA-204 (miR-204) biomarker that is dysregulated in most of the cancers. The proposed methodology uses the gold nanoparticles-modified carbon screen-printed electrode for immobilization of single-stranded DNA probe against miR-204. Colloidal gold nanoparticles were synthesized usingl-glutamic acid as reducing agent. Nanoparticles were characterized by UV –visible spectroscopy and transmission electron microscopy. Spherical gold nanoparticles were of 7–28 nm in size. Biosensor fabricated using these nanoparticles was characterized by cyclic voltammetry afte...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 26, 2022 Category: Bioinformatics Source Type: research

MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes
This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes.Methods:First, we use two data augmentation methods, edge perturbation and node dropping, to remove the data noise in the preprocessing stage. Second, we calculate Gaussian interaction profile kernel similarity and cosine similarity. Therefore, the Graph Convolutional Network(GCN) can fully use multi-view features. Then, the multi-view features are fed into the multi-attention block to learn the weights of different features adaptively. Finally, the embedding results are obtained using a Con...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 15, 2022 Category: Bioinformatics Source Type: research

DNRLCNN: A CNN Framework for Identifying MiRNA –Disease Associations Using Latent Feature Matrix Extraction with Positive Samples
AbstractEmerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about the miRNA –disease associations (MDAs) only provide the known MDAs, which can be regarded as positive samples. However, the unknown MDAs are not sufficient to regard as reliable negative samples. To deal with thi...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 15, 2022 Category: Bioinformatics Source Type: research

Adaptive Weighted Neighbors Method for Sensitivity Analysis
AbstractIdentifying key factors from observational data is important for understanding complex phenomena in many disciplines, including biomedical sciences and biology. However, there are still some limitations in practical applications, such as severely nonlinear input –output relationships and highly skewed output distributions. To acquire more reliable sensitivity analysis (SA) results in these extreme cases, inspired by the weightedk-nearest neighbors algorithm, we propose a new method called adaptive weighted neighbors (AWN). AWN makes full use of the information contained in all training samples instead of limited ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 15, 2022 Category: Bioinformatics Source Type: research

Computational Study of Methionine Methylation Process Catalyzed by SETD3
AbstractThe SETD3 enzyme has been identified as the methyltransferase for the His73 methylation inβ-actin, and such methylation plays an important role in regulating the actin ’s biochemical properties and finetuning the protein’s cellular roles. Further studies have demonstrated that SETD3 may be able to methylase some other residues, including lysine and methionine, that substitute His73 in theβ-actin peptide. The activity of SETD3 on the Met73 peptide is low without turnover. Interestingly, it has been shown that the N255V and N255A mutations of SETD3 can increase the activity by about 3-fold for the methionine ...
Source: Interdisciplinary Sciences, Computational Life Sciences - April 13, 2022 Category: Bioinformatics Source Type: research