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.

dbMisLoc: A Manually Curated Database of Conditional Protein Mis-localization Events
AbstractOver the last few years, an increasing number of protein mis-localization events have been reported under various conditions. It is important to understand these events and their relationship with complex disorders. Although many efforts had been made in establishing models with statistical or machine learning algorithms, a comprehensive database resource is still missing. Since the records of experimental-validated protein mis-localization events spread across many literatures, a collection of all these reports in a unique website is demanded. In this paper, we created the dbMisLoc database by manually curating co...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 31, 2023 Category: Bioinformatics Source Type: research

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images
In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.Graphical Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - March 28, 2023 Category: Bioinformatics Source Type: research

Cell Features Reconstruction from Gene Association Network of Single Cell
AbstractGene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusi...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 28, 2023 Category: Bioinformatics Source Type: research

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection
This article also aims to develop an automated server to capture fourteen thoracic pathology disease results using a tensor processing unit (TPU). The results of this study demonstrate that our dataset can be used to train models with high diagnostic accuracy for predicting the likelihood of 14 different diseases in abnormal chest radiographs, enabling accurate and efficient discrimination between different types of chest radiographs. This has the potential to bring benefits to v arious stakeholders and improve patient care.Graphical Abstract (Source: Interdisciplinary Sciences, Computational Life Sciences)
Source: Interdisciplinary Sciences, Computational Life Sciences - March 26, 2023 Category: Bioinformatics Source Type: research

Deep Learning-Based Modeling of Drug –Target Interaction Prediction Incorporating Binding Site Information of Proteins
This study aims to predict unknown ligand –target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs . We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 26, 2023 Category: Bioinformatics Source Type: research

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network
AbstractDrug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied t...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 21, 2023 Category: Bioinformatics Source Type: research

DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy
AbstractDNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change th...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 15, 2023 Category: Bioinformatics Source Type: research

Predicting Potential Drug –Disease Associations Based on Hypergraph Learning with Subgraph Matching
AbstractThe search for potential drug –disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emer ging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data. To better predict DDA, we propose a computational approa...
Source: Interdisciplinary Sciences, Computational Life Sciences - March 11, 2023 Category: Bioinformatics Source Type: research

Analysis of CRISPR-Cas Loci and their Targets in Levilactobacillus brevis
AbstractThe CRISPR ‒Cas system acts as a bacterial defense mechanism by conferring adaptive immunity and limiting genetic reshuffling. However, under adverse environmental hazards, bacteria can employ their CRISPR‒Cas system to exchange genes that are vital for adaptation and survival.Levilactobacillus brevis is a lactic acid bacterium with great potential for commercial purposes because it can be genetically manipulated to enhance its functionality and nutritional value. Nevertheless, the CRISPR ‒Cas system might interfere with the genetic modification process. Additionally, little is known about the CRISPR‒Cas sy...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 28, 2023 Category: Bioinformatics Source Type: research

LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes
AbstractSomatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes. In this experiment, firstly, the polynucleotide mutation rate ...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 27, 2023 Category: Bioinformatics Source Type: research

Design and Simulation of an Autonomous Molecular Mechanism Using Spatially Localized DNA Computation
AbstractAs a well-established technique, DNA synthesis offers interesting possibilities for designing multifunctional nanodevices. The micro-processing system of modern semiconductor circuits is dependent on strategies organized on silicon chips to achieve the speedy transmission of substances or information. Similarly, spatially localized structures allow for fixed DNA molecules in close proximity to each other during the synthesis of molecular circuits, thus providing a different strategy that of opening up a remarkable new area of inquiry for researchers. Herein, the Visual DSD (DNA strand displacement) modeling languag...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 10, 2023 Category: Bioinformatics Source Type: research

sORFPred: A Method Based on Comprehensive Features and Ensemble Learning to Predict the sORFs in Plant LncRNAs
AbstractLong non-coding RNAs (lncRNAs) are important regulators of biological processes. It has recently been shown that some lncRNAs include small open reading frames (sORFs) that can encode small peptides of no more than 100 amino acids. However, existing methods are commonly applied to human and animal datasets and still suffer from low feature representation capability. Thus, accurate and credible prediction of sORFs with coding ability in plant lncRNAs is imperative. This paper proposes a new method termed sORFPred, in which we design a model named MCSEN by combining multi-scale convolution and Squeeze-and-Excitation ...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 27, 2023 Category: Bioinformatics Source Type: research

Differential Diagnosis of DCIS and Fibroadenoma Based on Ultrasound Images: a Difference-Based Self-Supervised Approach
This study aims to investigate the feasibility of differentially diagnosing DCIS and FA using ultrasound radiomics-based AI techniques and further explore a novel approach that can reduce labeling efforts without sacrificing diagnostic performance. We included 461 DCIS and 651 FA patients, of whom 139 DCIS and 181 FA patients constituted a prospective test cohort. First, various feature engineering-based machine learning (FEML) and deep learning (DL) approaches were developed. Then, we designed a difference-based self-supervised (DSS) learning approach that only required FA samples to participate in training. The DSS appro...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 19, 2023 Category: Bioinformatics Source Type: research

MSResG: Using GAE and Residual GCN to Predict Drug –Drug Interactions Based on Multi-source Drug Features
AbstractDrug –drug interaction refers to taking the two drugs may produce certain reaction which may be a threat to patients’ health, or enhance the efficacy helpful for medical work. Therefore, it is necessary to study and predict it. In fact, traditional experimental methods can be used for drug–drug int eraction prediction, but they are time-consuming and costly, so we prefer to use more accurate and convenient calculation methods to predict the unknown drug–drug interaction. In this paper, we proposed a deep learning framework called MSResG that considers multi-sources features of drugs and comb ines them with ...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 17, 2023 Category: Bioinformatics Source Type: research

LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings
AbstractAccurately detecting linear B-cell epitopes (BCEs) makes great sense in vaccine design, immunodiagnostic test, antibody production, disease prevention and treatment. Wet-lab experiments for determining linear BCEs are both expensive and laborious, which are not able to meet the recognition needs of modern massive protein sequence data. Instead, computational methods can efficiently identify linear BCEs with low cost. Although several computational methods are available, the performance is still not satisfactory. Thus, we propose a new method, LBCE-XGB, to forecast linear BCEs based on XGBoost algorithm. To represen...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 16, 2023 Category: Bioinformatics Source Type: research