Motivation Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic (PD) and/or pharmacokinetic (PK) processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of drug-food interaction data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting drug-food interactions. Results In this paper, we first collect drug-food interaction data from DrugBank and PubMed respectively to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. The source code is available at https://github.com/23AIBox/23AIBox-DFinder Supplementary information Supplementary data are available at Bioinformatics online.

The packing of genomic DNA from double helix into highly-order hierarchical assemblies has a great impact on chromosome flexibility, dynamics and functions. The open and accessible regions of chromosomes are primary binding positions for regulatory elements and are crucial to nuclear processes and biological functions. Motivated by the success of flexibility-rigidity index (FRI) in biomolecular flexibility analysis and drug design, we propose an FRI-based model for quantitatively characterizing chromosome flexibility. Based on Hi-C data, a flexibility index for each locus can be evaluated. Physically, flexibility is tightly related to packing density. Highly compacted regions are usually more rigid, while loosely packed regions are more flexible. Indeed, a strong correlation is found between our flexibility index and DNase and ATAC values, which are measurements for chromosome accessibility. In addition, the genome regions with higher chromosome flexibility have a higher chance to be bound by transcription factors. Recently, the Gaussian network model (GNM) is applied to analyze the chromosome accessibility and a mobility profile has been proposed to characterize chromosome flexibility. Compared with GNM, our FRI is slightly more accurate (1% to 2% increase) and significantly more efficient in both computational time and costs. For a 5Kb resolution Hi-C data, the flexibility evaluation process only takes FRI a few minutes on a single-core processor. In contrast, GNM requires 1.5 hours on 10 CPUs. Moreover, interchromosome interactions can be easily combined into the flexibility evaluation, thus further enhancing the accuracy of our FRI. In contrast, the consideration of interchromosome information into GNM will significantly increase the size of its Laplacian (or Kirchhoff) matrix, thus becoming computationally extremely challenging for the current GNM. The software and supplementary document are available at https://github.com/jiajiepeng/FRI_chrFle.

Motivation: The packing of genomic DNA from double string into highly-order hierarchial assemblies has great impact on chromosome flexibility, dynamics and functions. The open and accessible regions of chromosome are the primary binding positions for regulatory elements and are crucial to nuclear processes and biological functions. Results: Motivated by the success of flexibility-rigidity index (FRI) in biomolecular flexibility analysis and drug design, we propose a FRI based model for quantitatively characterizing the chromosome flexibility. Based on the Hi-C data, a flexibility index for each locus can be evaluated. Physically, the flexibility is tightly related to the packing density. Highly compacted regions are usually more rigid, while loosely packed regions are more flexible. Indeed, a strong correlation is found between our flexibility index and DNase and ATAC values, which are measurements for chromosome accessibility. Recently, Gaussian network model (GNM) is applied to analyze the chromosome accessibility and a mobility profile has been proposed to characterize the chromosome flexibility. Compared with GNM, our FRI is slightly more accurate (1% to 2% increase) and significantly more efficient in both computational time and costs. For a 5kb resolution Hi-C data, the flexibility evaluation process only takes FRI a few minutes on a single-core processor. In contrast, GNM requires 1.5 hours on 10 CPUs. Moreover, interchromosome information can be easily incorporated into the flexibility evaluation, thus further enhance the accuracy of our FRI. In contrast, the consideration of interchromosome information into GNM will significantly increase the size of its Laplacian matrix, thus computationally extremely challenging for the current GNM.