DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources

BACKGROUND AND OBJECTIVE

With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them.

METHODS

In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks.

RESULTS

The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone.

CONCLUSIONS

This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

springD2A: capturing uncertainty in disease-drug association prediction with model integration

MOTIVATION

Drug repositioning that aims to find new indications for existing drugs has been an efficient strategy for drug discovery. In the scenario where we only have confirmed disease-drug associations as positive pairs, a negative set of disease-drug pairs is usually constructed from the unknown disease-drug pairs in previous studies, where we do not know whether drugs and diseases can be associated, to train a model for disease-drug association prediction (drug repositioning). Drugs and diseases in these negative pairs can potentially be associated, but most studies have ignored them.

RESULTS

We present a method, springD2A, to capture the uncertainty in the negative pairs, and to discriminate between positive and unknown pairs because the former are more reliable. In springD2A, we introduce a spring-like penalty for the loss of negative pairs, which is strong if they are too close in a unit sphere, but mild if they are at a moderate distance. We also design a sequential sampling in which the probability of an unknown disease-drug pair sampled as negative is proportional to its score predicted as positive. Multiple models are learned during sequential sampling, and we adopt parameter- and feature-based ensemble schemes to boost performance. Experiments show springD2A is an effective tool for drug-repositioning.

AVAILABILITY AND IMPLEMENTATION

A python implementation of springD2A and datasets used in this study are available at https://github.com/wangyuanhao/springD2A.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational identification of human ubiquitination sites using convolutional and recurrent neural networks

Ubiquitination is a very important protein post-translational modification in humans, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict human ubiquitination sites, the accuracy of these methods is generally not very satisfactory. In order to improve the prediction accuracy of human ubiquitination sites, we propose a new ensemble method HUbipPred, which takes the binary encoding and physicochemical properties of amino acids as training features, and integrates two intensively trained convolutional neural networks and two recurrent neural networks to build the model. Finally, HUbiPred achieves AUC values of 0.852 and 0.844 in five-fold cross-validation and independent tests, respectively, which greatly improves the prediction accuracy compared to previous predictors. We also analyze the physicochemical properties of amino acids around ubiquitination sites, study the important roles of architectures (i.e., convolution, long short-term memory (LSTM) and fully connected hidden layers) in the networks for prediction performance, and also predict potential ubiquitination sites in humans using HUbiPred. The training and test datasets, predicted human ubiquitination sites, and source codes of HUbiPred are publicly available at https://github.com/amituofo-xf/HUbiPred.