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There are intensive computational efforts to discover large-scale microbial interactions from metagenomic abundance data, however, it is often difficult to validate such inferred interactions without a manually curated dataset. There are also a number of small-scale microbial interactions reported in massive literature with experimental confidence. Text mining can be employed to extract such microbial...
The human oral cavity is an important habitat of microbes in the human body. It includes the colonization of various microorganisms such as bacteria, archaea, fungi, protozoa and viruses. Although oral diseases have been studied for decades, we have limited understanding of the boundaries of a healthy oral ecosystem and ecological shift toward dysbiosis. Here, we analyzed salivary microbiomes from...
Cyanobacteria bloom is a serious public health threat and a global challenge. Literature on the bloom prediction and forecasting has been accumulating and the emphasis appears to have been on the relation between the blooms and environmental factors, whilst the complexity of the bloom mechanism makes it difficult to reach adequate output of the models. Rapid development of next generation sequencing...
From a biological standpoint, due to the special combination of complex symptoms, some type of complex diseases is difficult to be accurately diagnosed. Known as phenotypic overlap, these sets of disease-related symptoms reveal a common pathological and physiological mechanism. Researchers attempt to visualize the phenotypic relationships between different human diseases from the perspective of machine...
Gene (microRNA) identification is a key step in understanding the cellular mechanisms. Compared with biological experiments, computational prediction of disease genes is cheaper and more effortless. In this study, we analyzed the properties of tumor-associated microRNA in mouse and found that tumor-associated genes display 8distinguishingfeatures when compared with genes not yet known to be involved...
Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified...
The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes...
While much progress has been made on the genetic analysis of osteoporosis in the past 20 years, there are a lot of genes and SNPs that are associated with osteoporosis through GWAS. In this paper, we aim to identify the risky SNPs associated with osteoporosis by algorithms based on the known osteoporosis GWAS-associated SNPs. The whole framework of our prediction method includes two steps: Firstly,...
Detection of protein complexes and functional modules plays a crucial role for strengthening the comprehension of cellular organization and biological functions on the dynamic protein-protein interaction network. In this article, we put forward a new strategy to identify temporal protein complexes. Integrating time-course gene expression data into static protein interaction data, a series of time-sequenced...
Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. For example, microbiome datasets can be represented by metabolic paths, taxonomic assignment or gene families. To integrate information from multiple views, data integration approaches such as methods based on nonnegative matrix factorization (NMF)...
Predicting disease genes in PPI network has attracted a lot of attention over the years. Based on the assumption that the phenotypes of the genes in the same complex where candidate gene located in are more similar to disease, the candidate gene is more possible to be disease gene, we propose a new disease gene identification method based on protein complex phenotype similarity. First, our method...
The detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, we propose a novel clustering algorithm CNC (Clustering based on Neighbor Closeness)...
Microbial abundance dynamics along time axis can be used to explore complex interactions among microorganisms. This is very important to use time series data for understanding the structure and function of a microbial community and its dynamic characteristics with the purturbations of external environment and physiology. Species with Time Delay regulatory network of relationships will be more suitable...
Visualization is an important method in microbiome data analysis, and dimensionality reduction is a necessary procedure to achieve it. Multidimensional Scaling (MDS) is a popular method, which is necessary to compute the distance matrix. The Unifrac distance is very reasonable and biologically meaningful in the analysis of microbiome data. Due to the complexity of the phylogenetic tree and the high...
Network is an exceptional way of depicting biological information. In biology, many different biological processes are represented by network, such as regulatory network, metabolic network and food web. In biology, network is a powerful supplement to the standard numerical data such as profile or count data. By absorbing network information, Vector autoregressive (VAR) model was proved to be an efficient...
Microbiome datasets are often comprised of different representations or views which provide complementary information, such as metabolic pathways, taxonomic assignments and gene families. Computational methods for integration of multi-view information combine these data to create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) provides a candidate to solve this problem...
In recent years, there are growing interests in developing novel approaches for inferring dynamic interactions in biological systems including gene transcription network and microbial interaction networks. Multivariate Vector Autoregression (MVAR) model is one of these efficient methods. Variants of MVAR with different penalties or regularizations can avoid the problem of over-fitting and provide...
Visualization of large-scale data is the first step to acquire preliminary insight into complex biological data. In recent years, many statistical visualization methods have been designed to support data visualization. Stochastic Neighbor Embedding (SNE) is one of these efficient approaches, which uses the probabilistic distance to model differences among data points within the data space. SNE and...
Dimensionality reduction and visualization are two important procedures in microbiome data analysis. With the intrinsic high dimensionality of the feature space in raw microbiome sequencing data, such as 16S rRNA, it requires proper simplification for possible further analysis. The explosively increasing size of data from large-scale microbiome studies inevitably and exponentially raises the computational...
Microbial interaction, such as species competition and symbiotic relationships, plays important role to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. Thanks to the recent accumulation of large-scale high-throughput sequencing data of complex microbial communities, there are increasing interests in identifying microbial interactions...
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