Heterogeneous graph inference with matrix completion for computational drug repositioning

2020 ◽  
Author(s):  
Mengyun Yang ◽  
Lan Huang ◽  
Yunpei Xu ◽  
Chengqian Lu ◽  
Jianxin Wang
Keyword(s):  
De Novo ◽  
Classical Method ◽  
Drug Repositioning ◽  
Matrix Completion ◽  
Critical Role ◽  
Similarity Measures ◽  
Supplementary Information ◽  
Disease Network ◽  
Computation Efficiency ◽  
Graph Inference

Abstract Motivation Emerging evidence presents that traditional drug discovery experiment is time-consuming and high costs. Computational drug repositioning plays a critical role in saving time and resources for drug research and discovery. Therefore, developing more accurate and efficient approaches is imperative. Heterogeneous graph inference is a classical method in computational drug repositioning, which not only has high convergence precision, but also has fast convergence speed. However, the method has not fully considered the sparsity of heterogeneous association network. In addition, rough similarity measure can reduce the performance in identifying drug-associated indications. Results In this article, we propose a heterogeneous graph inference with matrix completion (HGIMC) method to predict potential indications for approved and novel drugs. First, we use a bounded matrix completion (BMC) model to prefill a part of the missing entries in original drug–disease association matrix. This step can add more positive and formative drug–disease edges between drug network and disease network. Second, Gaussian radial basis function (GRB) is employed to improve the drug and disease similarities since the performance of heterogeneous graph inference more relies on similarity measures. Next, based on the updated drug–disease associations and new similarity measures of drug and disease, we construct a novel heterogeneous drug–disease network. Finally, HGIMC utilizes the heterogeneous network to infer the scores of unknown association pairs, and then recommend the promising indications for drugs. To evaluate the performance of our method, HGIMC is compared with five state-of-the-art approaches of drug repositioning in the 10-fold cross-validation and de novo tests. As the numerical results shown, HGIMC not only achieves a better prediction performance but also has an excellent computation efficiency. In addition, cases studies also confirm the effectiveness of our method in practical application. Availabilityand implementation The HGIMC software and data are freely available at https://github.com/BioinformaticsCSU/HGIMC, https://hub.docker.com/repository/docker/yangmy84/hgimc and http://doi.org/10.5281/zenodo.4285640. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion

2020 ◽  
Vol 36 (9) ◽  
pp. 2839-2847 ◽  
Author(s):  
Wenjuan Zhang ◽  
Hunan Xu ◽  
Xiaozhong Li ◽  
Qiang Gao ◽  
Lin Wang
Keyword(s):  
Drug Repositioning ◽  
Matrix Completion ◽  
Disease Association ◽  
Data Sources ◽  
Supplementary Information ◽  
Similarity Matrix ◽  
Latent Factors ◽  
Discovery Research ◽  
Disease Associations ◽  
Novel Drug

Abstract Motivation One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using the global structure of the drug-disease association data may introduce noise, and consequently limit the prediction power. Results In this work, we propose a novel drug repositioning approach by using Bayesian inductive matrix completion (DRIMC). First, we embed four drug data sources into a drug similarity matrix and two disease data sources in a disease similarity matrix. Then, for each drug or disease, its feature is described by similarity values between it and its nearest neighbors, and these features for drugs and diseases are mapped onto a shared latent space. We model the association probability for each drug-disease pair by inductive matrix completion, where the properties of drugs and diseases are represented by projections of drugs and diseases, respectively. As the known drug-disease associations have been manually verified, they are more trustworthy and important than the unknown pairs. We assign higher confidence levels to known association pairs compared with unknown pairs. We perform comprehensive experiments on three benchmark datasets, and DRIMC improves prediction accuracy compared with six stat-of-the-art approaches. Availability and implementation Source code and datasets are available at https://github.com/linwang1982/DRIMC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Drug repositioning based on bounded nuclear norm regularization

2019 ◽  
Vol 35 (14) ◽  
pp. i455-i463 ◽  
Author(s):  
Mengyun Yang ◽  
Huimin Luo ◽  
Yaohang Li ◽  
Jianxin Wang
Keyword(s):  
Recommendation System ◽  
Drug Repositioning ◽  
Matrix Completion ◽  
Approximation Error ◽  
Disease Association ◽  
Nuclear Norm ◽  
Low Rank ◽  
Supplementary Information ◽  
Disease Associations ◽  
Nuclear Norm Regularization

Abstract Motivation Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug–disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug–disease associations. Results In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug–disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug–drug and disease–disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug–disease network, which integrates the drug–drug, drug–disease and disease–disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug–disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. Availability and implementation The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. Supplementary information Supplementary data are available at Bioinformatics online.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MUM&Co: accurate detection of all SV types through whole-genome alignment

2020 ◽  
Vol 36 (10) ◽  
pp. 3242-3243 ◽  
Author(s):  
Samuel O’Donnell ◽  
Gilles Fischer
Keyword(s):  
De Novo ◽  
Supplementary Information ◽  
Genome Alignment ◽  
Whole Genome ◽  
Structural Variations ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Human Genomes ◽  
Whole Genome Alignment ◽  
Primary Output

Abstract Summary MUM&Co is a single bash script to detect structural variations (SVs) utilizing whole-genome alignment (WGA). Using MUMmer’s nucmer alignment, MUM&Co can detect insertions, deletions, tandem duplications, inversions and translocations greater than 50 bp. Its versatility depends upon the WGA and therefore benefits from contiguous de-novo assemblies generated by third generation sequencing technologies. Benchmarked against five WGA SV-calling tools, MUM&Co outperforms all tools on simulated SVs in yeast, plant and human genomes and performs similarly in two real human datasets. Additionally, MUM&Co is particularly unique in its ability to find inversions in both simulated and real datasets. Lastly, MUM&Co’s primary output is an intuitive tabulated file containing a list of SVs with only necessary genomic details. Availability and implementation https://github.com/SAMtoBAM/MUMandCo. Supplementary information Supplementary data are available at Bioinformatics online.

AStrap: identification of alternative splicing from transcript sequences without a reference genome

2018 ◽  
Vol 35 (15) ◽  
pp. 2654-2656 ◽  
Author(s):  
Guoli Ji ◽  
Wenbin Ye ◽  
Yaru Su ◽  
Moliang Chen ◽  
Guangzao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Reference Genome ◽  
De Novo ◽  
Supplementary Information ◽  
Model Organisms ◽  
Sequencing Data ◽  
Extensive Evaluation ◽  
Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Distinct histone H3–H4 binding modes of sNASP reveal the basis for cooperation and competition of histone chaperones

2021 ◽  
Author(s):  
Chao-Pei Liu ◽  
Wenxing Jin ◽  
Jie Hu ◽  
Mingzhu Wang ◽  
Jingjing Chen ◽  
...  
Keyword(s):  
De Novo ◽  
Critical Role ◽  
Histone H3 ◽  
Coiled Coil ◽  
Dynamic Network ◽  
Histone Chaperones ◽  
Binding Modes ◽  
Nucleosome Assembly ◽  
Partially Unfolded

Chromosomal duplication requires de novo assembly of nucleosomes from newly synthesized histones, and the process involves a dynamic network of interactions between histones and histone chaperones. sNASP and ASF1 are two major histone H3–H4 chaperones found in distinct and common complexes, yet how sNASP binds H3–H4 in the presence and absence of ASF1 remains unclear. Here we show that, in the presence of ASF1, sNASP principally recognizes a partially unfolded Nα region of histone H3, and in the absence of ASF1, an additional sNASP binding site becomes available in the core domain of the H3–H4 complex. Our study also implicates a critical role of the C-terminal tail of H4 in the transfer of H3–H4 between sNASP and ASF1 and the coiled-coil domain of sNASP in nucleosome assembly. These findings provide mechanistic insights into coordinated histone binding and transfer by histone chaperones.

scholarly journals BET bromodomain proteins regulate enhancer function during adipogenesis

2018 ◽  
Vol 115 (9) ◽  
pp. 2144-2149 ◽  
Author(s):  
Jonathan D. Brown ◽  
Zachary B. Feldman ◽  
Sean P. Doherty ◽  
Jaime M. Reyes ◽  
Peter B. Rahl ◽  
...  
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Critical Role ◽  
Regulatory Elements ◽  
Developmental Transitions ◽  
Adipocyte Cell ◽  
Bromodomain Proteins ◽  
Enhancer Function ◽  
Distal Regulatory Elements ◽  
Expression Program

Developmental transitions are guided by master regulatory transcription factors. During adipogenesis, a transcriptional cascade culminates in the expression of PPARγ and C/EBPα, which orchestrate activation of the adipocyte gene expression program. However, the coactivators controlling PPARγ and C/EBPα expression are less well characterized. Here, we show the bromodomain-containing protein, BRD4, regulates transcription of PPARγ and C/EBPα. Analysis of BRD4 chromatin occupancy reveals that induction of adipogenesis in 3T3L1 fibroblasts provokes dynamic redistribution of BRD4 to de novo super-enhancers proximal to genes controlling adipocyte differentiation. Inhibition of the bromodomain and extraterminal domain (BET) family of bromodomain-containing proteins impedes BRD4 occupancy at these de novo enhancers and disrupts transcription of Pparg and Cebpa, thereby blocking adipogenesis. Furthermore, silencing of these BRD4-occupied distal regulatory elements at the Pparg locus by CRISPRi demonstrates a critical role for these enhancers in the control of Pparg gene expression and adipogenesis in 3T3L1s. Together, these data establish BET bromodomain proteins as time- and context-dependent coactivators of the adipocyte cell state transition.

Epigenetic reprogramming of host and viral genes by Human Cytomegalovirus infection in Kasumi-3 myeloid progenitor cells at early times post-infection

2021 ◽  
Author(s):  
Eleonora Forte ◽  
Fatma Ayaloglu Butun ◽  
Christian Marinaccio ◽  
Matthew J. Schipma ◽  
Andrea Piunti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Post Infection ◽  
De Novo ◽  
Critical Role ◽  
Myeloid Cells ◽  
Viral Gene Expression ◽  
Chromatin Accessibility ◽  
Viral Gene ◽  
Dynamic Changes ◽  
Pol Ii

HCMV establishes latency in myeloid cells. Using the Kasumi-3 latency model, we previously showed that lytic gene expression is activated prior to establishment of latency in these cells. The early events in infection may have a critical role in shaping establishment of latency. Here, we have used an integrative multi-omics approach to investigate dynamic changes in host and HCMV gene expression and epigenomes at early times post infection. Our results show dynamic changes in viral gene expression and viral chromatin. Analyses of Pol II, H3K27Ac and H3K27me3 occupancy of the viral genome showed that 1) Pol II occupancy was highest at the MIEP at 4 hours post infection. However, it was observed throughout the genome; 2) At 24 hours, H3K27Ac was localized to the major immediate early promoter/enhancer and to a possible second enhancer in the origin of replication OriLyt; 3) viral chromatin was broadly accessible at 24 hpi. In addition, although HCMV infection activated expression of some host genes, we observed an overall loss of de novo transcription. This was associated with loss of promoter-proximal Pol II and H3K27Ac, but not with changes in chromatin accessibility or a switch in modification of H3K27. Importance. HCMV is an important human pathogen in immunocompromised hosts and developing fetuses. Current anti-viral therapies are limited by toxicity and emergence of resistant strains. Our studies highlight emerging concepts that challenge current paradigms of regulation of HCMV gene expression in myeloid cells. In addition, our studies show that HCMV has a profound effect on de novo transcription and the cellular epigenome. These results may have implications for mechanisms of viral pathogenesis.

scholarly journals Lead Optimization of Dehydroemetine for Repositioned Use in Malaria

2020 ◽  
Vol 64 (4) ◽  
Author(s):  
Priyanka Panwar ◽  
Kepa K. Burusco ◽  
Muna Abubaker ◽  
Holly Matthews ◽  
Andrey Gutnov ◽  
...  
Keyword(s):  
De Novo ◽  
Antimalarial Activity ◽  
Drug Repositioning ◽  
Multidrug Resistant ◽  
Cross Resistance ◽  
Synthetic Analogue ◽  
80S Ribosome ◽  
Content Type ◽  
Resistant Strains

ABSTRACT Drug repositioning offers an effective alternative to de novo drug design to tackle the urgent need for novel antimalarial treatments. The antiamoebic compound emetine dihydrochloride has been identified as a potent in vitro inhibitor of the multidrug-resistant strain K1 of Plasmodium falciparum (50% inhibitory concentration [IC50], 47 nM ± 2.1 nM [mean ± standard deviation]). Dehydroemetine, a synthetic analogue of emetine dihydrochloride, has been reported to have less-cardiotoxic effects than emetine. The structures of two diastereomers of dehydroemetine were modeled on the published emetine binding site on the cryo-electron microscopy (cryo-EM) structure with PDB code 3J7A (P. falciparum 80S ribosome in complex with emetine), and it was found that (−)-R,S-dehydroemetine mimicked the bound pose of emetine more closely than did (−)-S,S-dehydroisoemetine. (−)-R,S-dehydroemetine (IC50 71.03 ± 6.1 nM) was also found to be highly potent against the multidrug-resistant K1 strain of P. falciparum compared with (−)-S,S-dehydroisoemetine (IC50, 2.07 ± 0.26 μM), which loses its potency due to the change of configuration at C-1′. In addition to its effect on the asexual erythrocytic stages of P. falciparum, the compound exhibited gametocidal properties with no cross-resistance against any of the multidrug-resistant strains tested. Drug interaction studies showed (−)-R,S-dehydroemetine to have synergistic antimalarial activity with atovaquone and proguanil. Emetine dihydrochloride and (−)-R,S-dehydroemetine failed to show any inhibition of the hERG potassium channel and displayed activity affecting the mitochondrial membrane potential, indicating a possible multimodal mechanism of action.

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