journal
Journals Interdisciplinary Sciences, Co...

Interdisciplinary Sciences, Computational Life Sciences

https://read.qxmd.com/read/38536590/bdm-an-assessment-metric-for-protein-complex-structure-models-based-on-distance-difference-matrix
#1
JOURNAL ARTICLE
Jiaqi Zhai, Wenda Wang, Ranxi Zhao, Daiwen Sun, Da Lu, Xinqi Gong
Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD)...
March 27, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38530613/dotad-a-database-of-therapeutic-antibody-developability
#2
JOURNAL ARTICLE
Wenzhen Li, Hongyan Lin, Ziru Huang, Shiyang Xie, Yuwei Zhou, Rong Gong, Qianhu Jiang, ChangCheng Xiang, Jian Huang
The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models...
March 26, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38489147/resdeepsurv-a-survival-model-for-deep-neural-networks-based-on-residual-blocks-and-self-attention-mechanism
#3
JOURNAL ARTICLE
Yuchen Wang, Xianchun Kong, Xiao Bi, Lizhen Cui, Hong Yu, Hao Wu
Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling...
March 15, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38483753/chl-dti-a-novel-high-low-order-information-convergence-framework-for-effective-drug-target-interaction-prediction
#4
JOURNAL ARTICLE
Shudong Wang, Yingye Liu, Yuanyuan Zhang, Kuijie Zhang, Xuanmo Song, Yu Zhang, Shanchen Pang
Recognizing drug-target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets...
March 14, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38472692/sefilter-dia-squeeze-and-excitation-network-for-filtering-high-confidence-peptides-of-data-independent-acquisition-proteomics
#5
JOURNAL ARTICLE
Qingzu He, Huan Guo, Yulin Li, Guoqiang He, Xiang Li, Jianwei Shuai
Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability...
March 12, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38457109/graphsformercpi-graph-transformer-for-compound-protein-interaction-prediction
#6
JOURNAL ARTICLE
Jun Ma, Zhili Zhao, Tongfeng Li, Yunwu Liu, Jun Ma, Ruisheng Zhang
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations...
March 8, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38457108/identifying-protein-phosphorylation-site-disease-associations-based-on-multi-similarity-fusion-and-negative-sample-selection-by-convolutional-neural-network
#7
JOURNAL ARTICLE
Qian Deng, Jing Zhang, Jie Liu, Yuqi Liu, Zong Dai, Xiaoyong Zou, Zhanchao Li
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases...
March 8, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38436840/predicting-microbe-disease-associations-based-on-a-linear-neighborhood-label-propagation-method-with-multi-order-similarity-fusion-learning
#8
JOURNAL ARTICLE
Ruibin Chen, Guobo Xie, Zhiyi Lin, Guosheng Gu, Yi Yu, Junrui Yu, Zhenguo Liu
Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations...
March 4, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38424397/singular-value-decomposition-driven-non-negative-matrix-factorization-with-application-to-identify-the-association-patterns-of-sarcoma-recurrence
#9
JOURNAL ARTICLE
Jin Deng, Kaijun Li, Wei Luo
Sarcomas are malignant tumors from mesenchymal tissue and are characterized by their complexity and diversity. The high recurrence rate making it important to understand the mechanisms behind their recurrence and to develop personalized treatments and drugs. However, previous studies on the association patterns of multi-modal data on sarcoma recurrence have overlooked the fact that genes do not act independently, but rather function within signaling pathways. Therefore, this study collected 290 whole solid images, 869 gene and 1387 pathway data of over 260 sarcoma samples from UCSC and TCGA to identify the association patterns of gene-pathway-cell related to sarcoma recurrences...
March 1, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38416364/machine-learning-accelerates-de-novo-design-of-antimicrobial-peptides
#10
JOURNAL ARTICLE
Kedong Yin, Wen Xu, Shiming Ren, Qingpeng Xu, Shaojie Zhang, Ruiling Zhang, Mengwan Jiang, Yuhong Zhang, Degang Xu, Ruifang Li
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr...
February 28, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38413547/transformative-deep-neural-network-approaches-in-kidney-ultrasound-segmentation-empirical-validation-with-an-annotated-dataset
#11
JOURNAL ARTICLE
Rashid Khan, Chuda Xiao, Yang Liu, Jinyu Tian, Zhuo Chen, Liyilei Su, Dan Li, Haseeb Hassan, Haoyu Li, Weiguo Xie, Wen Zhong, Bingding Huang
Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US images is challenging due to low contrast, speckle noise, fluid, variations in kidney shape, and modality artifacts. Moreover, well-annotated US datasets for renal segmentation and detection are scarce. This study aims to build a novel, well-annotated dataset containing 44,880 US images...
February 27, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38381315/predicting-circrna-rbp-binding-sites-using-a-hybrid-deep-neural-network
#12
JOURNAL ARTICLE
Liwei Liu, Yixin Wei, Zhebin Tan, Qi Zhang, Jianqiang Sun, Qi Zhao
Circular 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...
February 21, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38374297/a-review-of-the-application-of-spatial-transcriptomics-in-neuroscience
#13
REVIEW
Le Zhang, Zhenqi Xiong, Ming Xiao
Since 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...
February 20, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38368575/scem-a-new-ensemble-framework-for-predicting-cell-type-composition-based-on-scrna-seq-data
#14
JOURNAL ARTICLE
Xianxian Cai, Wei Zhang, Xiaoying Zheng, Yaxin Xu, Yuanyuan Li
With 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...
February 18, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38342857/inference-of-gene-regulatory-networks-based-on-multi-view-hierarchical-hypergraphs
#15
JOURNAL ARTICLE
Songyang Wu, Kui Jin, Mingjing Tang, Yuelong Xia, Wei Gao
Since 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...
February 11, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38340264/a-combined-manual-annotation-and-deep-learning-natural-language-processing-study-on-accurate-entity-extraction-in-hereditary-disease-related-biomedical-literature
#16
JOURNAL ARTICLE
Dao-Ling Huang, Quanlei Zeng, Yun Xiong, Shuixia Liu, Chaoqun Pang, Menglei Xia, Ting Fang, Yanli Ma, Cuicui Qiang, Yi Zhang, Yu Zhang, Hong Li, Yuying Yuan
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...
February 10, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38310628/synchronous-mutual-learning-network-and-asynchronous-multi-scale-embedding-network-for-mirna-disease-association-prediction
#17
JOURNAL ARTICLE
Weicheng Sun, Ping Zhang, Weihan Zhang, Jinsheng Xu, Yanrong Huang, Li Li
MicroRNA (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 multiple network topologies and various node attributes including miRNA seed and full-length sequences to predict potential MDAs...
February 4, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38294648/pddgcn-a-parasitic-disease-drug-association-predictor-based-on-multi-view-fusion-graph-convolutional-network
#18
JOURNAL ARTICLE
Xiaosong Wang, Guojun Chen, Hang Hu, Min Zhang, Yuan Rao, Zhenyu Yue
The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. 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...
January 31, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38286905/predicting-mirna-disease-associations-by-combining-graph-and-hypergraph-convolutional-network
#19
JOURNAL ARTICLE
Xujun Liang, Ming Guo, Longying Jiang, Ying Fu, Pengfei Zhang, Yongheng Chen
miRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to various human diseases and can be potential biomarkers or therapeutic targets for some diseases, such as cancers. Therefore, accurately predicting miRNA-disease associations is of great importance for understanding and curing diseases. However, how to efficiently utilize the characteristics of miRNAs and diseases and the information on known miRNA-disease associations for prediction is still not fully explored...
January 29, 2024: Interdisciplinary Sciences, Computational Life Sciences
https://read.qxmd.com/read/38206558/lpi-skmsc-predicting-lncrna-protein-interactions-with-segmented-k-mer-frequencies-and-multi-space-clustering
#20
JOURNAL ARTICLE
Dian-Zheng Sun, Zhan-Li Sun, Mengya Liu, Shuang-Hao Yong
 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 predicting 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...
January 11, 2024: Interdisciplinary Sciences, Computational Life Sciences
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