Identifying drug-target and drug-disease associations using computational intelligence

Background: Traditional drug development is an expensive process that typically requires the investment of a large number of resources in terms of finances, equipment, and time. However, sometimes these efforts do not result in a pharmaceutical product in the market. To overcome the limitations of this process, complementary—or in some cases, alternative—methods with high-throughput results are necessary. Computational drug discovery is a shortcut that can reduce the difficulties of traditional methods because of its flexible nature. Drug repositioning, which aims to find new applications for existing drugs, is one of the promising approaches in computational drug discovery. Considering the availability of different types of data in various public databases, drug-disease association identification and drug repositioning can be performed based on the interaction of drugs and biomolecules. Moreover, drug repositioning mainly focuses on the similarity of drugs and the similarity of agents interacting with drugs. It is assumed that if drug D is associated or interacts with target T, then drugs similar to drug D can be associated or interact with target T or targets similar to target T. Therefore, similarity-based approaches are widely used for drug repositioning. Research Objectives: Develop novel computational methods for drug-target and drug-disease association prediction to be used for drug repositioning. Results: In this thesis, the problem of drug-disease association identification and drug repositioning is divided into sub-problems. These sub-problems include drug-target interaction prediction and using targets as intermediaries for drug-disease association identification. Addressing these subproblems results in the development of three new computational models for drug-target interaction and drug-disease association prediction: MDIPA, NMTF-DTI, and NTD-DR. MDIPA is a nonnegative matrix factorization-based method to predict interaction scores of drug-microRNA pairs, where the interaction scores can effectively be used for drug repositioning. This method uses the functional similarity of microRNAs and structural similarity of drugs to make predictions. To include more biomolecules (e.g., proteins) in the study as well as achieve a more flexible model, we develop NMTF-DTI. This nonnegative matrix tri- factorization method uses multiple types of similarities for drugs and proteins to predict the associations between drugs and targets and their interaction score. To take another step towards drug repositioning, we identify the associations between drugs and disease. In this step, we develop NTD-DR, a nonnegative tensor decomposition approach where multiple similarities for drugs, targets, and diseases are used to identify the associations between drugs and diseases to be used for drug repositioning. The detail of each method is discussed in Chapters 3, 4, 5, respectively. Future work will focus on considering additional biomolecules as the drug target to identify drug-disease associations for drug repositioning. In summary, using nonnegative matrix factorization, nonnegative matrix tri-factorization, and nonnegative tensor decomposition, as well as applying different types of association information and multiple types of similarities, improve the performance of proposed methods over those methods that use single association or similarity information

Integrative methods for analysing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

ITRPCA: a new model for computational drug repositioning based on improved tensor robust principal component analysis

Background: Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications.Methods: In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug–disease associations. First, we use a weighted k-nearest neighbor (WKNN) approach to increase the overall density of the drug–disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug–disease association pairs derived from the low-rank drug and low-rank disease tensors.Results: We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates

Biological Applications of Knowledge Graph Embedding Models

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.The work presented in this paper was supported by the CLARIFY project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875160, and by Insight research centre supported by the Science Foundation Ireland (SFI) grant (12/RC/2289_2)peer-reviewed2021-02-1

A bioinformatics potpourri

© 2018 The Author(s). The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018