Influx evidences show that red long non-coding RNAs (lncRNAs) play important roles in various critical biological processes, and they afffect the development and progression of various human diseases. Therefore, it is necessary to precisely identify the lncRNA-disease associations. The identification precision can be improved by developing data integrative models. However, current models mainly need to project heterogeneous data onto the homologous networks, and then merge these networks into a composite one for integrative prediction. We recognize that this projection overrides the individual structure of the heterogeneous data, and the combination is impacted by noisy networks. As a result, the performance is compromised. Given that, we introduce a weighted matrix factorization model on multi-relational data to predict LncRNA-disease associations (WMFLDA). WMFLDA firstly uses a heterogeneous network to capture the inter(intra)-associations between different types of nodes (including genes, lncRNAs, and Disease Ontology terms). Then, it presets weights to these inter-association and intra-association matrices of the network, and cooperatively decomposes these matrices into low-rank ones to explore the underlying relationships between nodes. Next, it jointly optimizes the low-rank matrices and the weights. After that, WMFLDA approximates the lncRNA-disease association matrix using the optimized matrices and weights, and thus to achieve the prediction. WMFLDA obtains a much better performance than related data integrative solutions across different experiment settings and evaluation metrics. It can not only respect the intrinsic structures of individual data sources, but can also fuse them with selection.

Motivation:Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be.Results:To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities.Availability and implementation:The source code for MFLDA is available at: http://mlda.swu.edu.cn/codes.php? name = MFLDA.Contact:gxyu@swu.edu.cn.Supplementary information:Supplementary data are available at Bioinformatics online.

Increasing biomedical studies have demonstrated important associations between lncRNAs and various human complex diseases. Developing data integrative models can boost the performance of lncRNA-disease association identification. However, existing models generally have to transform heterogenous data into homologous networks, and then sum up these networks into a composite network for integrative prediction. The transformation may conceal the intrinsic structure of the heterogeneous data, and the summation process may suffer from noisy networks. Both these issues compromise the performance. In this paper, we introduce a Weighted Matrix Factorization based data fusion solution to predict LncRNA-Disease Associations (WMFLDA). WMFLDA first directly encodes the inter-associations between different types of biological entities (such as genes, lncRNAs, and Disease Ontology terms) via a heterogeneous network, which also encodes multiple intra-association networks of entities of the same type. Next, it assigns weights to these inter-association and intra-association matrices, and performs collaborative low-rank matrix factorization to explore the latent relationships between entities. After that, it simultaneously optimizes these weights and low-rank matrices. In the end, it uses the optimized low-rank matrices and weights to reconstruct the lncRNA-disease association matrix and accomplish the prediction. WMFLDA achieves a larger area under the receiver operating curve (by at least 7.61%), and a larger area under the precision-recall curve (by at least 5.49%) than competitive data fusion approaches in different experimental scenarios. WMFLDA can not only maintain the intrinsic structure of the association matrices, but can also selectively and differentially combine them. The codes and datasets are available at http: //mlda.swu.edu.cn/codes.php?name=WMFLDA.

Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic graph. A gene is generally annotated with several of these GO terms. Therefore, accurately predicting the association between genes and massive terms is a difficult challenge. To combat with this challenge, we propose an matrix factorization based approach called NMFGO. NMFGO stores the available GO annotations of genes in a gene-term association matrix and adopts an ontological structure based taxonomic similarity measure to capture the GO hierarchy. Next, it factorizes the association matrix into two low-rank matrices via nonnegative matrix factorization regularized with the GO hierarchy. After that, it employs a semantic similarity based k nearest neighbor classifier in the low-rank matrices approximated subspace to predict gene functions. Empirical study on three model species (S. cerevisiae, H. sapiens, and A. thaliana) shows that NMFGO is robust to the input parameters and achieves significantly better prediction performance than GIC, TO, dRW-kNN, and NtN, which were re-implemented based on the instructions of the original papers. The supplementary file and demo codes of NMFGO are available at http://mlda.swu.edu.cn/codes.php?name=NMFGO.

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.

蛋白质是生命活动的重要物质基础,对其功能的准确标注可以极大地促进生命科学的研究与发展.已有的蛋白质功能预测方法通常仅关注利用蛋白质具有某些功能的信息(正样例),并没有关注利用蛋白质不相关的功能信息(负样例).已有研究表明,结合蛋白质负样例可以降低蛋白质功能预测的复杂度并提高预测精度.本文提出一种基于降维的蛋白质不相关功能预测方法 (predicting irrelevant functions of proteins based on dimensionality reduction,IFDR).IFDR通过在蛋白质互作网邻接矩阵和蛋白质–功能标记关联矩阵上分别进行随机游走,挖掘蛋白质之间的内在关系和预估蛋白质的缺失功能标记,再分别利用奇异值分解将上述2个矩阵投影降维为低维实数矩阵,最后利用半监督回归预测负样例.在酵母菌、人类和拟南芥的蛋白质数据集上的实验表明,IFDR比已有相关算法能够更准确地预测负样例,对互作网络和功能标记空间的降维均可以提高负样例预测精度.

1 Datasets description Ten heterogeneous relational data sources having direct or indirect relevance toward lncRNA-disease association are collected for experiments. The sources of these data sources and statistics of these data sources are listed in Table S1 and Table S2. 2 Optimizing G, S and W This section elaborates on how to iteratively optimize G, S and W in the objective function of MFLDA. Before elaborating on the updating rule, we introduce the Lagrangian multipliers {λi}i=1 for Gi ≥ 0, and reformulate the objective function of MFLDA as follows: min G≥0 L(G,S,W,λ) = ∑ Rij∈R Wijtr(R T ijRij −2Gj RijGiSij + Gi GiSijGj GjSij)

A remaining key challenge of modern biology is annotating the functional roles of proteins. Various computational models have been proposed for this challenge. Most of them assume the annotations of annotated proteins are complete. But in fact, many of them are incomplete. We proposed a method called NewGOA to predict new Gene Ontology (GO) annotations for incompletely annotated proteins and for completely un-annotated ones. NewGOA employs a hybrid graph, composed of two types of nodes (proteins and GO terms), to encode interactions between proteins, hierarchical relationships between terms and available annotations of proteins. To account for structural difference between GO terms subgraph and proteins subgraph, NewGOA applies a bi-random walks algorithm, which executes asynchronous random walks on the hybrid graph, to predict new GO annotations of proteins. Experimental study on archived GO annotations of two model species (H. Sapiens and S. cerevisiae) shows that NewGOA can more accurately and efficiently predict new annotations of proteins than other related methods. Experimental results also indicate the bi-random walks can explore and further exploit the structural difference between GO terms subgraph and proteins subgraph. The supplementary files and codes of NewGOA are available at: http://mlda.swu.edu.cn/codes.php?name=NewGOA.

Accurately and automatically predicting biological functions of proteins is one of the fundamental tasks in bioinformatics,and it is also one of the key applications of artificial intelligence in biological data analysis.The wide application of high throughput technologies produces various functional association networks of molecules.Integrating these networks contributes to more comprehensive view for understanding the functional mechanism of proteins and to improve the performance of protein function prediction.However,existing network integration based solutions cannot apply to a large number of functional labels,ignore the correlation between labels,or cannot differentially integrate multiple networks.This paper proposes a protein function prediction approach based on multiple networks collaborative matrix factorization (ProCMF).To explore the latent relationship between proteins and between labels,ProCMF firstly applies nonnegative matrix factorization to factorize the protein-label association matrix into two low-rank matrices.To employ the correlation between labels and to guide the collaborative factorization with proteomic data,it defines two smoothness terms on these two low-rank matrices.To differentially integrate these networks,ProCMF sets different weights to them.In the end,ProCMF combines these goals into a unified objective function and introduces an alternative optimization technique to jointly optimize the low-rank matrices and weights.Experimental results on three model species (yeast,human and mouse) with multiple functional networks show that ProCMF outperforms other related competitive methods.ProCMF can effectively and efficiently handle massive labels and differentially integrate multiple networks.

Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations. However, most of them neglect the structural difference between lncRNAs network and diseases network, hierarchical relationships between diseases and pattern of newly discovered associations. In this study, we developed a model that performs Bi-Random Walks to predict novel LncRNA-Disease Associations (BRWLDA in short). This model utilizes multiple heterogeneous data to construct the lncRNA functional similarity network, and Disease Ontology to construct a disease network. It then constructs a directed bi-relational network based on these two networks and available lncRNAs-disease associations. Next, it applies bi-random walks on the network to predict potential associations. BRWLDA achieves reliable and better performance than other comparing methods not only on experiment verified associations, but also on the simulated experiments with masked associations. Case studies further demonstrate the feasibility of BRWLDA in identifying new lncRNA-disease associations.

Proteins are the foundation for many life processes and accurately annotating their biological functions can significantly boost the development of life sciences. Current function prediction models focus on employing the knowledge that proteins perform specific functions (positive examples), but ignore the knowledge that some functions are irrelevant for a protein (negative examples). Recent research indicates that incorporating negative examples can reduce the complexity and improve the accuracy of protein function prediction. In this paper, we propose an approach for predicting irrelevant functions of proteins based on dimensionality reduction (IFDR). Initially, IFDR performs random walks through matrices in a protein-protein interactions (PPI) network, as well as the corresponding protein-function association matrices, in order to explore the underlying relationships between proteins and model the missing functional annotations of proteins. Next, IFDR uses single value decomposition to project these matrices into low-dimensional numerical matrices. Finally, IFDR uses semi-supervised regression to predict negative examples of proteins. Experiments on S. cerevisiae, H. sapiens, and A. thaliana data demonstrate that IFDR can more accurately predict negative examples when compared to related methods. Dimensionality reduction in the network space and label space can both improve the accuracy of negative example prediction.

大多数学生的科研训练生涯都是从研究生阶段开始的,我有幸在学院提供的导师与科研助理双选会这个平台的帮助下,在本科学习阶段就进入实验室,开始了我的科研学习生活.我接触过的领域内的很多优秀导师都指出,本科生提早进行科研训练对日后不论是在工作中还是科研学习中取得创新性成果的机会将大大增加[1].不过,与绝大多数刚接触科研的在读本科生类似,我在从事科研训练中也曾产生过疑惑和迷茫,经历了不少挫折.在此,我将过去两年从事科研训练的经历和体会与大家分享.

Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

BACKGROUND:Gene Ontology (GO) is a collaborative project that maintains and develops controlled vocabulary (or terms) to describe the molecular function, biological roles and cellular location of gene products in a hierarchical ontology. GO also provides GO annotations that associate genes with GO terms. GO consortium independently and collaboratively annotate terms to gene products, mainly from model organisms (or species) they are interested in. Due to experiment ethics, research interests of biologists and resources limitations, homologous genes from different species currently are annotated with different terms. These differences can be more attributed to incomplete annotations of genes than to functional difference between them.RESULTS:Semantic similarity between genes is derived from GO hierarchy and annotations of genes. It is positively correlated with the similarity derived from various types of biological data and has been applied to predict gene function. In this paper, we investigate whether it is possible to replenish annotations of incompletely annotated genes by using semantic similarity between genes from two species with homology. For this investigation, we utilize three representative semantic similarity metrics to compute similarity between genes from two species. Next, we determine the k nearest neighborhood genes from the two species based on the chosen metric and then use terms annotated to k neighbors of a gene to replenish annotations of that gene. We perform experiments on archived (from Jan-2014 to Jan-2016) GO annotations of four species (Human, Mouse, Danio rerio and Arabidopsis thaliana) to assess the contribution of semantic similarity between genes from different species. The experimental results demonstrate that: (1) semantic similarity between genes from homologous species contributes much more on the improved accuracy (by 53.22%) than genes from single species alone, and genes from two species with low homology; (2) GO annotations of genes from homologous species are complementary to each other.CONCLUSIONS:Our study shows that semantic similarity based interspecies gene function annotation from homologous species is more prominent than traditional intraspecies approaches. This work can promote more research on semantic similarity based function prediction across species.

MOTIVATION Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space. RESULTS In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods. AVAILABILITY AND IMPLEMENTATION The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa CONTACT gxyu@swu.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Predicting protein function is one of the key challenges in the post genome era .Functional annotation databases of proteins mainly provide the knowledge of positive examples that proteins carrying out a given function ,and rarely record the knowledge of negative examples that proteins not carrying out a given function .Current computational models almost only focus on utilizing the positive examples for function prediction and seldom pay attention to these scarce but informative negative examples .It is well recognized that both positive and negative examples should be used to achieve a discriminative predictor .Motivated by this recognition ,in this paper ,we propose a protein function prediction approach using positive and negative examples (ProPN ) to bridge this gap . ProPN first utilizes a direct signed hybrid graph to describe the positive examples ,negative examples ,interactions between proteins and correlations between functions ;and then it employs label propagation on the graph to predict protein function . The experimental results on several public available proteomic datasets demonstrate that ProPN not only makes better performance in predicting negative examples of proteins whose functional annotations are partially known than state‐of‐the‐art algorithms ,but also performs better than other related approaches in predicting functions of proteins w hose functional annotations are completely unknow n .

Proteins carry out various important activities in an organism. Accurately annotating their functions can boost the advance of life-science research and application. High-throughput techniques generate such a large volume of proteomic and genomic data that it is beyond the capability of low-throughput wet-lab based techniques. Thus, computational model-based large-scale protein-function prediction is one of the key tasks in the post-genomic era. Current machine-learning based methods often focus on predicting the functions of completely unlabeled proteins. These methods ignore the incomplete labels of the labeled proteins, and hence have low accuracy. In this paper, we design a directed Hybrid Graph (dHG) based on the gene ontology hierarchy and the protein-protein interaction network. Next, we use the dHG to predict novel functions by performing a random walk with restart on it. The proposed dHG can predict not only new functions for partially labeled proteins, but also new functions for completely unlabeled proteins. Experimental results on proteins of yeast and humans show that dHG, across various evaluation metrics, achieves better results than other related methods, and costs less time than these methods.

In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.

西南大学在线评测系统是为了满足西南大学计算机与信息科学学院在课程改革过程中提出的新需求而设计开发的一款在线评测系统.该系统借鉴了开源项目GDOJ,对其进行二次定制性开发,确保满足学院C语言课程组教学实验改革过程中的各种需要.在设计开发以及实际使用的过程中发现,该系统在我院对参加重庆市ACM程序设计大赛的选手培训也能发挥重要的作用,于是将其逐步引入相关课程教学.本文就将其在教学改革过程中起到的优良作用简要描述.

简单介绍了弱信号在竞争情报领域中的特点,总结竞争弱信号在国内外的研究现状,进而分析现有研究所存在的问题.基于当前的大数据环境,在已有的竞争弱信号定性识别方法基础上尝试性提出了基于大数据环境竞争弱信号定量识别方法.从组织的决策视角出发,探讨从海量数据中捕捉影响组织发展的隐性的、非主流的但极可能在未来对组织产生重大影响的弱信号的技术方法并构建相应的理论模型;建立弱信号与主体的关联模型,并通过构建关联函数来定量反映该关联的强弱程度.