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.

Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset
This study aims to build a novel, well-annotated dataset containing 44,880 US images. In addition, we propose a novel training scheme that utilizes the encoder and decoder parts of a state-of-the-art segmentation algorithm. In the pre-processing step, pixel intensity normalization improves contrast and facilitates model convergence. The modified encoder –decoder architecture improves pyramid-shaped hole pooling, cascaded multiple-hole convolutions, and batch normalization. The pre-processing step gradually reconstructs spatial information, including the capture of complete object boundaries, and the post-processing modul...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 27, 2024 Category: Bioinformatics Source Type: research

Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network
AbstractCircular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding ta...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 21, 2024 Category: Bioinformatics Source Type: research

A Review of the Application of Spatial Transcriptomics in Neuroscience
AbstractSince spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we fi...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 20, 2024 Category: Bioinformatics Source Type: research

scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data
AbstractWith the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we review...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 18, 2024 Category: Bioinformatics Source Type: research

Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs
AbstractSince gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF) –target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural inform...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 11, 2024 Category: Bioinformatics Source Type: research

A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types —gene, variant, disease and species. Both a BERT-based large name entity recognition (NER)...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 10, 2024 Category: Bioinformatics Source Type: research

Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction
AbstractMicroRNA (miRNA) serves as a pivotal regulator of numerous cellular processes, and the identification of miRNA-disease associations (MDAs) is crucial for comprehending complex diseases. Recently, graph neural networks (GNN) have made significant advancements in MDA prediction. However, these methods tend to learn one type of node representation from a single heterogeneous network, ignoring the importance of multiple network topologies and node attributes. Here, we propose SMDAP (Sequence hierarchical modeling-based Mirna-Disease Association Prediction framework), a novel GNN-based framework that incorporates multip...
Source: Interdisciplinary Sciences, Computational Life Sciences - February 4, 2024 Category: Bioinformatics Source Type: research

PDDGCN: A Parasitic Disease –Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network
In this study, we reorganized a fundamental dataset of parasitic disease–drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-vi ew heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from eac...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 31, 2024 Category: Bioinformatics Source Type: research

Predicting miRNA –Disease Associations by Combining Graph and Hypergraph Convolutional Network
In this study, we propose a novel computational method for predicting miRNA–disease associations. The proposed method combines the graph convolutional network and the hypergraph convolutional network. The graph convolutional network is utilized to extract the information from miRNA-similarity data as well as disease-similarity da ta. Based on the representations of miRNAs and diseases learned by the graph convolutional network, we further use the hypergraph convolutional network to capture the complex high-order interactions in the known miRNA–disease associations. We conduct comprehensive experiments with different da...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 29, 2024 Category: Bioinformatics Source Type: research

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information
AbstractThe protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological function mechanisms. In this document, we have constructed a predictor, named PPSNO, forecasting protein SNO sites using stacked integrated learning. PPSNO integrates multiple machine learning techniques into an ensemble model, enhancing its predictive accuracy. First, we established benchmark datasets by collecting SNO sites from various sources, including literature, d...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 11, 2024 Category: Bioinformatics Source Type: research

LPI-SKMSC: Predicting LncRNA –Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering
Abstract Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA–protein interactions (LPIs) are expensive and time-consuming, many computational methods for predicti ng LPIs have been proposed as alternatives. In the LPIs prediction problem, there commonly exists the imbalance in the distribution of positive and negative samples. However, there are few existing methods that give specific consideration to this problem. In this paper, we proposed a new clustering- based LPIs predictio...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 11, 2024 Category: Bioinformatics Source Type: research

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm
AbstractThe breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation str...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 8, 2024 Category: Bioinformatics Source Type: research

A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer
AbstractThe exploration of drug combinations presents an opportunity to amplify therapeutic effectiveness while alleviating undesirable side effects. Nevertheless, the extensive array of potential combinations poses challenges in terms of cost and time constraints for experimental screening. Thus, it is crucial to narrow down the search space. Deep learning approaches have gained widespread popularity in predicting synergistic drug combinations tailored for specific cell lines in vitro settings. In the present study, we introduce a novel method termed GTextSyn, which utilizes the integration of gene expression data and che...
Source: Interdisciplinary Sciences, Computational Life Sciences - January 6, 2024 Category: Bioinformatics Source Type: research

Drug Repositioning Based on Deep Sparse Autoencoder and Drug –Disease Similarity
AbstractDrug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug –disease heterogeneous networks to extract drug–disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The me thod utilizes a deep sparse autoencoder and integrates drug–disease similarities. First, the researchers constructed a drug–disease feature network by incorporating information from drug chemical structure...
Source: Interdisciplinary Sciences, Computational Life Sciences - December 16, 2023 Category: Bioinformatics Source Type: research

Hessian Regularized $$L_{2,1}$$ -Nonnegative Matrix Factorization and Deep Learning for miRNA –Disease Associations Prediction
AbstractSince the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA –disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA–disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matr ix factorizatio...
Source: Interdisciplinary Sciences, Computational Life Sciences - December 15, 2023 Category: Bioinformatics Source Type: research