Article
Mathematics, Applied
Leandro Tortosa, Jose F. Vicent, Gevorg Yeghikyan
Summary: A new algorithm is proposed for calculating node centrality in attributed multiplex networks, addressing the issue of isolated nodes in any layer. The algorithm allows for different parameter values to be chosen for each layer, modulating the importance of data in the network. By utilizing a dataset containing car mobility data and attribute data describing city locations, the algorithm's capabilities and characteristics are demonstrated.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Biochemical Research Methods
Khandakar Tanvir Ahmed, Jiao Sun, Sze Cheng, Jeongsik Yong, Wei Zhang
Summary: This study introduces omicsGAN, a generative adversarial network model that integrates two omics data and their interaction network to generate synthetic data with improved performance in cancer outcome classification and patients' survival prediction compared to original datasets. The integrity of the interaction network is crucial for creating synthetic data with higher predictive quality.
Article
Microbiology
Daniel Ruiz-Perez, Jose Lugo-Martinez, Natalia Bourguignon, Kalai Mathee, Betiana Lerner, Ziv Bar-Joseph, Giri Narasimhan
Summary: Developed a computational pipeline PALM that integrates multi-omics data from longitudinal microbiome studies using dynamic Bayesian networks (DBNs), accurately identifying both known and novel interactions between microbial taxa. Experimental validations further supported predicted novel metabolite-taxon interactions, demonstrating the method's ability to identify new relationships and their impact.
Article
Biochemical Research Methods
Hongli Gao, Bin Zhang, Long Liu, Shan Li, Xin Gao, Bin Yu
Summary: In this study, a universal framework called GCN-SC is proposed for integrating single-cell multi-omics data. GCN-SC selects one dataset as the reference and the rest as the query datasets, and uses mutual nearest neighbor algorithm to identify cell-pairs that connect cells within and across datasets. Then, a GCN algorithm adjusts the count matrices from query datasets, followed by dimension reduction using non-negative matrix factorization.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Johanna Zoppi, Jean-Francois Guillaume, Michel Neunlist, Samuel Chaffron
Summary: MiBiOmics is a web-based application that facilitates multi-omics data visualization, exploration, integration, and analysis through interactive protocols. It helps mine complex biological systems and identify robust biomarkers linked to specific contextual parameters or biological states.
BMC BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Antoine Bodein, Marie-Pier Scott-Boyer, Olivier Perin, Kim-Anh Le Cao, Arnaud Droit
Summary: This text introduces the R package timeOmics, a generic analytical framework for integrating longitudinal multi-omics data. The package includes functions for pre-processing, modeling, and clustering to help researchers identify molecular features strongly associated with time. It is demonstrated in a case study to detect seasonal patterns of various molecular and clinical variables in patients with diabetes mellitus.
Article
Biochemical Research Methods
Bridget A. Tripp, Hasan H. Otu
Summary: This study introduces an algorithm called OBaNK, which utilizes Bayesian networks and external knowledge to model interactions between heterogeneous high-dimensional biological data, aiming to elucidate complex functional clusters and emergent relationships associated with an observed phenotype. The results demonstrate that OBaNK successfully learns accurate interaction networks from data integrating external knowledge and identifies heterogeneous functional networks from real data.
CURRENT BIOINFORMATICS
(2022)
Article
Biology
Ming Zhang, Xiaoyang Wang, Nan Yang, Xu Zhu, Zequn Lu, Yimin Cai, Bin Li, Ying Zhu, Xiangpan Li, Yongchang Wei, Shaokai Zhang, Jianbo Tian, Xiaoping Miao
Summary: By integrating multi-omics data, we identified 105 high-confidence risk genes (HRGs) that play important roles in colorectal cancer (CRC) and are enriched in CRC-related biological processes. Taking CEBPB as an example, it acts as a transcription factor in CRC, promoting CRC cell proliferation by regulating multiple oncogenic pathways. We also found that a putative functional variant, rs1810503, regulating CEBPB is associated with CRC risk, and mechanistically, it decreases CRC risk by weakening enhancer activity and reducing CEBPB expression.
SCIENCE CHINA-LIFE SCIENCES
(2023)
Review
Biochemistry & Molecular Biology
Takoua Jendoubi
Summary: Metabolomics explores the intricate chemical reactions within living organisms and how they are affected by internal and external factors, providing valuable insights into biological functions and mechanisms. Integrating metabolomics data with genomics, transcriptomics, and proteomics information offers unprecedented opportunities for in-depth analysis and discovery of hidden relationships between omics variables.
Article
Biochemistry & Molecular Biology
Su Yon Jung
Summary: The study investigated the molecular processes and key regulatory factors of the IGF-I/IR axis by integrating genomics and multi-omics data. The findings revealed potential genetic targets and mechanisms associated with diseases such as diabetes and cancer.
Article
Psychiatry
Dan He, Cong Fan, Mengling Qi, Yuedong Yang, David N. Cooper, Huiying Zhao
Summary: A new risk gene predictor, rGAT-omics, has been proposed, integrating multi-omics data to predict a series of high-risk genes related to schizophrenia, providing new insights into the molecular mechanisms underlying schizophrenia.
TRANSLATIONAL PSYCHIATRY
(2021)
Article
Genetics & Heredity
Xiaoqing Chen, Mingfei Han, Yingxing Li, Xiao Li, Jiaqi Zhang, Yunping Zhu
Summary: Multi-omics data integration is a promising approach for identifying patient subgroups, but current methods for grouping genes into co-expression modules have limitations. In this study, we present a novel data integration framework called CLAM which addresses these limitations by integrating multi-omics datasets and known molecular interactions to construct a trans-omics neighborhood matrix, and using a local approximation procedure to define gene modules from the matrix. Applying CLAM to colorectal cancer and B-cell differentiation data, we demonstrated its ability to recover biologically relevant modules and gene ontology terms.
FRONTIERS IN GENETICS
(2023)
Review
Forestry
Mingcheng Wang, Rui Li, Qi Zhao
Summary: In recent years, the ecological and economic values of forest plants have gained recognition worldwide. Conventional breeding methods cannot meet the increasing global demand for improved forest plant varieties. However, the development of omics technologies such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics has provided powerful tools for precision genetic breeding of forest plants. Multi-omics integration has become a valuable tool for genome-wide functional element identification in forest plant breeding. This review summarizes the recent progress in omics technologies and their applications in genetic studies on forest plants to provide forest plant breeders with fundamental knowledge of multi-omics techniques for future breeding programs.
Article
Biology
Yonghui Ni, Jianghua He, Prabhakar Chalise
Summary: Differential expression (DE) analysis and differential network (DN) analysis are usually conducted independently. However, this article proposes an integrative analysis method called DNrank, which considers both DE and DN, to identify disease-associated molecular features. The proposed method has been demonstrated to be effective in several experiments.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemical Research Methods
Sehwan Moon, Hyunju Lee
Summary: This article introduces a multi-task attention learning algorithm, MOMA, for multi-omics data, which achieves high diagnostic performance and interpretability by capturing important biological processes. Experimental results demonstrate the superior performance of MOMA in various classification tasks, and its utility is verified through comparison experiments and biological analysis.
Article
Biochemical Research Methods
Yijuan Wang, Zhi-Ping Liu
Summary: The research presents a bioinformatics method for identifying potential biomarkers based on network rewiring in different states. By constructing a differential gene regulatory network (D-GRN) and applying community detection technique, the study successfully selects biomarker genes with the ability to distinguish normal samples from controls.
BMC BIOINFORMATICS
(2022)
Article
Mathematical & Computational Biology
Xu Qiao, Xianru Zhang, Wei Chen, Xin Xu, Yen-Wei Chen, Zhi-Ping Liu
Summary: Detecting significant signaling pathways in disease progression is important for understanding complex disease development. This paper introduces a tensor-based gene set enrichment analysis method, called tensorGSEA, that identifies relevant pathways during disease development by reconstructing multi-dimensional gene expression data. The experiments show that tensorGSEA is efficient in identifying critical pathways with diabetes-specific functions.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Biochemistry & Molecular Biology
Haixia Shang, Zhi-Ping Liu
Summary: This paper presents an ensemble method for reliable network-based biomarker discovery, using supervised module detection and module prioritization. The authors successfully identify hepatocellular carcinoma (HCC) network modules as diagnostic biomarkers and validate their effectiveness on gene regulatory networks. The results demonstrate the ability of the method to find effective network biomarkers for cancer diagnosis with fewer false positives.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Physics, Multidisciplinary
Dawei Li, Zhi-Ping Liu
Summary: In this work, a machine learning-based method is proposed for predicting the box-office revenue of movies, and the best prediction performance is achieved through empirical comparisons. The validation and consecutive predictions demonstrate the effectiveness and accuracy of the proposed method.
Article
Biochemical Research Methods
Guangyi Chen, Zhi-Ping Liu
Summary: In this paper, the authors propose GENELink, a graph attention network-based method for inferring latent interactions between transcription factors (TFs) and target genes in gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data. They demonstrate that GENELink achieves comparable or better performance than existing methods on seven scRNA-seq datasets with different types of ground-truth networks. Additionally, they apply GENELink to scRNA-seq data of human breast cancer metastasis and uncover regulatory heterogeneity between primary tumors and lung metastasis. The study also validates the functional importance of mitochondrial oxidative phosphorylation (OXPHOS) during the seeding step of the cancer metastatic cascade.
Article
Biology
Na Yu, Zhi-Ping Liu, Rui Gao
Summary: This paper proposes a novel method based on tensor factorization and label propagation to improve the prediction of miRNA-disease associations. By combining multiple types of data and methods, the algorithm achieves improved prediction performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Zishuang Zhang, Chenxi Sun, Zhi-Ping Liu
Summary: This study proposes a game theoretic method to discover gene modules serving as biomarkers on the gene regulatory network (GRN) for better distinguishing hepatocellular carcinoma (HCC) samples from healthy ones. The method achieves relatively better classification performances and the enriched dysfunctions in biomarkers are consistent with prior knowledge of HCC occurrence and development.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Biochemical Research Methods
Lingyu Li, Yousif A. Algabri, Zhi-Ping Liu
Summary: This study proposes an Ensemble Feature Selection method (EFSmarker) to screen biomarkers for breast cancer (BRCA) from publicly available gene expression data. Twelve filter feature selection methods are employed to calculate the importance of all features, and a logistic regression classifier is applied to evaluate the classification AUC value of each feature subset. The identified biomarkers are validated through gene and protein expression validation, functional enrichment analysis, literature checking, and independent dataset validation.
CURRENT BIOINFORMATICS
(2023)
Article
Computer Science, Information Systems
Chuanyuan Wang, Shiyu Xu, Zhi-Ping Liu
Summary: This study proposes a framework called Network Activity Evaluation (NAE) that evaluates the activity of gene regulatory events by measuring the consistency between gene expression data and network structure. The efficiency and advantages of the NAE framework are demonstrated through multiple experiments and comparison studies.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Pengpai Li, Zhi-Ping Liu
Summary: GeoBind is a geometric deep learning method that predicts nucleic binding sites on protein surfaces, outperforming other existing predictors. It can also be applied to other types of ligand binding site prediction tasks.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Lingyu Li, Zhi-Ping Liu
Summary: In this paper, the authors propose a CNet-SVM method for discovering biomarker genes from high-throughput omics data. This method can maintain the connectivity between genes while selecting features, and has shown good classification and prediction capabilities on simulation datasets and real-world breast cancer datasets. The results demonstrate the effectiveness of CNet-SVM in selecting connected-network-structured features from high-throughput data.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Genetics & Heredity
Chen Shen, Yi Cao, Guoqiang Qi, Jian Huang, Zhi-Ping Liu
Summary: By using a bioinformatics method based on dynamic network entropy analysis, we can identify potential pathway biomarkers for the occurrence and development of hepatocellular carcinoma. This method integrates transcriptomic and interactomic data, calculates the dynamic network entropy of individual pathways, and evaluates their activities and dysfunctionalities during the disease progression. Machine learning classification methods are then employed to screen out pathway biomarkers with the ability to distinguish different-stage samples of HCC progression.
Article
Cell Biology
Nannan Ning, Ziqi Shang, Zhiping Liu, Zhizhou Xia, Yang Li, Ruibao Ren, Hongmei Wang, Yi Zhang
Summary: Our study reveals the mechanisms by which MP-HJ-1b affects tumors, showing that it promotes ferroptosis by inhibiting ribosomal component proteins. In contrast, colchicine promotes ferroptosis by enhancing autophagy. Clinical research demonstrates altered biochemical markers associated with ferroptosis in cervical cancer patients.
CELL DEATH DISCOVERY
(2023)
Article
Biochemical Research Methods
Chuanyuan Wang, Shiyu Xu, Duanchen Sun, Zhi-Ping Liu
Summary: In this study, a framework called ActivePPI is proposed to evaluate the activity of PPI networks in different cellular conditions. ActivePPI measures the consistency between network architecture and protein measurement data by estimating the probability density of protein mass spectrometry abundance and modeling PPIN using a Markov-random-field-based method. The likelihood significance of the match between PPIN structure and protein abundance data is quantified using a nonparametric permutation test to derive empirical P-values. Extensive numerical experiments demonstrate the superior performance of ActivePPI in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. In summary, ActivePPI is a versatile tool for evaluating PPI networks that uncovers the functional significance of protein interactions in crucial cellular biological processes and offers further insights into physiological phenomena.
Article
Immunology
Hong Yu, Li Li, Anthony Huffman, John Beverley, Junguk Hur, Eric Merrell, Hsin-hui Huang, Yang Wang, Yingtong Liu, Edison Ong, Liang Cheng, Tao Zeng, Jingsong Zhang, Pengpai Li, Zhiping Liu, Zhigang Wang, Xiangyan Zhang, Xianwei Ye, Samuel K. Handelman, Jonathan Sexton, Kathryn Eaton, Gerry Higgins, Gilbert S. Omenn, Brian Athey, Barry Smith, Luonan Chen, Yongqun He
Summary: This paper proposes a set of postulates and a framework for understanding the complexity of host-pathogen interactions at the molecular and cellular levels in diseases like COVID-19. By establishing host-pathogen interaction postulates and an ontology framework, it enables data standardization, sharing, and analysis, providing new approaches for drug and vaccine design.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
