Hypergraph-based Logistic Matrix Factorization for Metabolite-disease Interaction Prediction

Functional Annotations ◽

Multiple Networks ◽

Novel Approach ◽

Interactive Network

Abstract Motivation Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships. Results In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Lastly, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite-disease interactions. In computational experiments, HGLMF accurately predicted the metabolite-disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be employed to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies. Availability The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF. Supplementary information Supplementary data are available at Bioinformatics online.

BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems

IEEE Transactions on Computers ◽

10.1109/tc.2020.2997051 ◽

2020 ◽

pp. 1-1

Author(s):

Ruixin Guo ◽

Feng Zhang ◽

Lizhe Wang ◽

Wusheng Zhang ◽

Xinya Lei ◽

...

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Load Balance ◽

Interrogative Nam in Early Latin

Mnemosyne ◽

10.1163/156852511x547802 ◽

2012 ◽

Vol 65 (2) ◽

pp. 203-218

Author(s):

Nigel Holmes

Keyword(s):

Close Association ◽

Supply Information

Abstract The article examines the use of nam in close association with a question word (e.g. quisnam, nam quis) in early Latin. As Kroon (1995, 165-5) observes, the use mirrors explicative nam, in that it is found when a speaker seeks supplementary information, while explicative nam is used to provide it. If interrogative nam arose from a sarcastic use of explicative nam to comment on a dialogue partner’s failure to supply information, this could account for several nuances that commentators have found in nam questions.

PathScore: a web tool for identifying altered pathways in cancer data

10.1101/067090 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen G. Gaffney ◽

Jeffrey P. Townsend

Keyword(s):

Web Application ◽

Somatic Mutations ◽

Web Tool ◽

Cancer Data ◽

Link Type ◽

Novel Approach ◽

Supplementary Material ◽

User Friendly ◽

Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

Integrating hypertension phenotype and genotype with hybrid non-negative matrix factorization

Bioinformatics ◽

10.1093/bioinformatics/bty804 ◽

2018 ◽

Vol 35 (8) ◽

pp. 1395-1403 ◽

Cited By ~ 3

Author(s):

Yuan Luo ◽

Chengsheng Mao ◽

Yiben Yang ◽

Fei Wang ◽

Faraz S Ahmad ◽

...

Keyword(s):

Matrix Factorization ◽

Cardiac Mechanics ◽

Approximation Problem ◽

Joint Analysis ◽

Learning Approaches ◽

Patient Stratification ◽

Projected Gradient Method ◽

Genotype Information ◽

Non Negative Matrix Factorization

Abstract Motivation Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements with the goal of identifying subtypes of patients who share similar pathophysiologic mechanisms and may respond more uniformly to targeted treatments. Existing machine learning approaches often face challenges in integrating phenotype and genotype information and presenting to clinicians an interpretable model. We aim to provide informed patient stratification based on phenotype and genotype features. Results In this article, we present a hybrid non-negative matrix factorization (HNMF) method to integrate phenotype and genotype information for patient stratification. HNMF simultaneously approximates the phenotypic and genetic feature matrices using different appropriate loss functions, and generates patient subtypes, phenotypic groups and genetic groups. Unlike previous methods, HNMF approximates phenotypic matrix under Frobenius loss, and genetic matrix under Kullback-Leibler (KL) loss. We propose an alternating projected gradient method to solve the approximation problem. Simulation shows HNMF converges fast and accurately to the true factor matrices. On a real-world clinical dataset, we used the patient factor matrix as features and examined the association of these features with indices of cardiac mechanics. We compared HNMF with six different models using phenotype or genotype features alone, with or without NMF, or using joint NMF with only one type of loss We also compared HNMF with 3 recently published methods for integrative clustering analysis, including iClusterBayes, Bayesian joint analysis and JIVE. HNMF significantly outperforms all comparison models. HNMF also reveals intuitive phenotype–genotype interactions that characterize cardiac abnormalities. Availability and implementation Our code is publicly available on github at https://github.com/yuanluo/hnmf. Supplementary information Supplementary data are available at Bioinformatics online.

Studying 3D genome evolution using genomic sequence

Bioinformatics ◽

10.1093/bioinformatics/btz775 ◽

2019 ◽

Author(s):

Raphaël Mourad

Keyword(s):

Genome Evolution ◽

High Throughput Sequencing ◽

Genomic Sequence ◽

Regulation Of Gene Expression ◽

Replication Timing ◽

Three Dimensions ◽

3D Genome ◽

Chromatin Looping ◽

Abstract Motivation The three dimensions (3D) genome is essential to numerous key processes such as the regulation of gene expression and the replication-timing program. In vertebrates, chromatin looping is often mediated by CTCF, and marked by CTCF motif pairs in convergent orientation. Comparative high-throughput sequencing technique (Hi-C) recently revealed that chromatin looping evolves across species. However, Hi-C experiments are complex and costly, which currently limits their use for evolutionary studies over a large number of species. Results Here, we propose a novel approach to study the 3D genome evolution in vertebrates using the genomic sequence only, e.g. without the need for Hi-C data. The approach is simple and relies on comparing the distances between convergent and divergent CTCF motifs by computing a ratio we named the 3D ratio or ‘3DR’. We show that 3DR is a powerful statistic to detect CTCF looping encoded in the human genome sequence, thus reflecting strong evolutionary constraints encoded in DNA and associated with the 3D genome. When comparing vertebrate genomes, our results reveal that 3DR which underlies CTCF looping and topologically associating domain organization evolves over time and suggest that ancestral character reconstruction can be used to infer 3DR in ancestral genomes. Availability and implementation The R code is available at https://github.com/morphos30/PhyloCTCFLooping. Supplementary information Supplementary data are available at Bioinformatics online.

PHANOTATE: a novel approach to gene identification in phage genomes

Bioinformatics ◽

10.1093/bioinformatics/btz265 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4537-4542 ◽

Cited By ~ 24

Author(s):

Katelyn McNair ◽

Carol Zhou ◽

Elizabeth A Dinsdale ◽

Brian Souza ◽

Robert A Edwards

Keyword(s):

Gene Prediction ◽

Optimal Path ◽

Genome Structure ◽

Weighted Graph ◽

Open Reading Frames ◽

Functional Protein ◽

Protein Database ◽

Protein Coding ◽

Abstract Motivation Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. Results We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. Availability and implementation https://github.com/deprekate/PHANOTATE Supplementary information Supplementary data are available at Bioinformatics online.

Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

Bioinformatics ◽

10.1093/bioinformatics/btz769 ◽

2019 ◽

Cited By ~ 3

Author(s):

Runpu Chen ◽

Le Yang ◽

Steve Goodison ◽

Yijun Sun

Keyword(s):

Deep Learning ◽

Cluster Structure ◽

Data Representation ◽

High Dimensional ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Cancer Subtypes ◽

Novel Approach ◽

Open Source Software Package

Abstract Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.

Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

Bioinformatics ◽

10.1093/bioinformatics/btaa439 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i516-i524

Author(s):

Midori Iida ◽

Michio Iwata ◽

Yoshihiro Yamanishi

Keyword(s):

Large Scale ◽

Expression Patterns ◽

Therapeutic Targets ◽

Molecular Networks ◽

New Associations ◽

Disease States ◽

Molecular Features ◽

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

Differentiating isoform functions with collaborative matrix factorization

Bioinformatics ◽

10.1093/bioinformatics/btz847 ◽

2019 ◽

Author(s):

Keyao Wang ◽

Jun Wang ◽

Carlotta Domeniconi ◽

Xiangliang Zhang ◽

Guoxian Yu

Keyword(s):

Matrix Factorization ◽

Characteristic Curve ◽

Function Prediction ◽

Low Rank ◽

Data Matrix ◽

Genomic Databases ◽

Gene Level ◽

The Matrix ◽

Level Function

Abstract Motivation Isoforms are alternatively spliced mRNAs of genes. They can be translated into different functional proteoforms, and thus greatly increase the functional diversity of protein variants (or proteoforms). Differentiating the functions of isoforms (or proteoforms) helps understanding the underlying pathology of various complex diseases at a deeper granularity. Since existing functional genomic databases uniformly record the annotations at the gene-level, and rarely record the annotations at the isoform-level, differentiating isoform functions is more challenging than the traditional gene-level function prediction. Results Several approaches have been proposed to differentiate the functions of isoforms. They generally follow the multi-instance learning paradigm by viewing each gene as a bag and the spliced isoforms as its instances, and push functions of bags onto instances. These approaches implicitly assume the collected annotations of genes are complete and only integrate multiple RNA-seq datasets. As such, they have compromised performance. We propose a data integrative solution (called DisoFun) to Differentiate isoform Functions with collaborative matrix factorization. DisoFun assumes the functional annotations of genes are aggregated from those of key isoforms. It collaboratively factorizes the isoform data matrix and gene-term data matrix (storing Gene Ontology (GO) annotations of genes) into low-rank matrices to simultaneously explore the latent key isoforms, and achieve function prediction by aggregating predictions to their originating genes. In addition, it leverages the PPI network and GO structure to further coordinate the matrix factorization. Extensive experimental results show that DisoFun improves the AUROC (area under the receiver-operating characteristic curve) and AUPRC (area under the precision-recall curve) of existing solutions by at least 7.7% and 28.9%, respectively. We further investigate DisoFun on four exemplar genes (LMNA, ADAM15, BCL2L1, and CFLAR) with known functions at the isoform-level, and observed that DisoFun can differentiate functions of their isoforms with 90.5% accuracy. Availability The code of DisoFun is available at mlda.swu.edu.cn/codes.php?name=DisoFun. Supplementary information Supplementary data are available at Bioinformatics online.

MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization

Bioinformatics ◽

10.1093/bioinformatics/btaa577 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5061-5067

Author(s):

Ali Akbar Jamali ◽

Anthony Kusalik ◽

Fang-Xiang Wu

Keyword(s):

Drug Interaction ◽

Drug Interactions ◽

Matrix Factorization ◽

Structural Information ◽

Superior Performance ◽

Similarity Matrix ◽

Interaction Prediction ◽

Prediction Approach ◽

Drug Similarity

Abstract Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.