Disease gene recognition and editing optimization through knowledge learned from domain feature spaces

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis presents computational methods used for the recognition of disease genes and for the optimal design of disease gene CRISPR/Cas9 editing systems. The key innovation in these computational methods is the feature space and characteristics captured from the biology domain knowledge through machine learning algorithms. The disease-gene association prediction problems are studied in Chapters 3-5. Disease gene recognition is a hot topic in various fields, especially in biology, medicine and pharmacology. Non-coding genes, a type of genes without protein products, have been proved to play important roles in disease development. Particularly, the two kinds of non-coding gene products such as microRNA (miRNA) and long non-coding RNA (lncRNA) have caught much attention as they are abundantly expressed in various tissues and frequently interact with other biomolecules, e.g. DNA, RNA and protein. The disease-ncRNA relationships remain largely unknown. Computational methods can immensely help replenish this kind of knowledge. To overcome existing computational methods’ limitations such as significantly relying on network structures and similarity measurements, or lacking reliable negative samples, this thesis presents two novel methods. One is the precomputed kernel matrix support vector machine (SVM) method to predict disease related miRNAs in Chapter 3. The precomputed kernel matrix was built by integrating several kinds of similarities computed with effective characteristics for miRNAs and diseases. The reliable negative samples were collected through analyzing the published array and sequencing data. This binary classification method accurately predicts disease-miRNA associations, which outperforms those state-of-the-art methods. In Chapter 4, the predicted novel disease-miRNA associations were combined with known relationships of diseases, miRNAs and genes to reconstruct a disease-gene-miRNA (DGR) tripartite network. Reliable multi-disease associated co-functional miRNA pairs were extracted from this DGR for cross-disease analysis by defining the co-function score. This not only proves the proposed method’s effectiveness but also contributes to the study of multi-purpose miRNA therapeutics. Another is the bagging SVM-based positive-unlabeled learning method for disease-lncRNA prioritizing that is described in Chapter 5. It creatively characterized a disease with its related genes’ chromosome distribution and pathway enrichment properties. The disease-lncRNA pairs were represented as novel feature vectors to train the bagging SVM for predicting disease-lncRNA associations. This novel representation contributes to the superior performance of the proposed method in disease-lncRNA prediction even when a given disease has no currently recognized lncRNA genes. After confirming the relationships between genes and diseases, one of the most difficult tasks is to investigate the molecular mechanism and treatment of the diseases considering their related genes. The CRISPR/Cas9 system is a promising gene editing tool for operating the genes to achieve the goals of disease-gene function clarification and genetic disease curing. Designing an optimal CRISPR/Cas9 system can not only improve its editing efficiency but also reduce its side effect, i.e. off-target editing. Furthermore, the off-target site detection problem involves genome-wide sequence observing which makes it a more challenging job. The CRISPR/Cas9 system on-target cutting efficiency prediction and off-target site detection questions are discussed in Chapters 6 and 7 respectively. To accurately measure the CRISPR/Cas9 system’s cutting efficiency, the profiled Markov properties and some cutting position related features were merged into the feature space for representing the single-guide RNAs (sgRNAs). These features were learned by a two-step averaging method where an XGBoost’s predictions and an SVM’s predictions were averaged as the final results. Later performance evaluations and comparisons demonstrate that this method can predict a sgRNA’s cutting efficiency with consistently good performance no matter it is expressed from a U6 promoter in cells or from a T7 promoter in vitro. In the off-target site detection, a sample was defined as an on-target-off-target site sequence pair to turn this problem into a classification issue. Each sample was numerically depicted with the nucleotide composition change features and the mismatch distribution properties. An ensemble classifier was constructed to distinguish real off-target sites and no-editing sites of a given sgRNA. Its excellent performance was confirmed with different test scenarios and case studies

Tools for experimental and computational analyses of off-target editing by programmable nucleases

Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.Off-target effects of programmable nucleases remain a critical issue for therapeutic applications of genome editing. This review compares experimental and computational tools for off-target analysis and provides recommendations for better assessments of off-target effects

Safety quantification in gene editing experiments using machine learning on rationally designed feature spaces

With ongoing development of the CRISPR/Cas programmable nuclease system, applications in the area of \textit{in vivo} therapeutic gene editing are increasingly within reach. However, non-negligible off-target effects remain a major concern for clinical applications. Even though a multitude of off-target cleavage datasets have been published, a comprehensive, transparent overview tool has not yet been established. The first part of this thesis presents the creation of crisprSQL (http://www.crisprsql.com), a large, diverse, interactive and bioinformatically enhanced collection of CRISPR/Cas9 off-target cleavage studies aimed at enriching the fields of cleavage profiling, gene editing safety analysis and transcriptomics. Having established this data source, we use it to train novel deep learning algorithms and explore feature encodings for off-target prediction, systematically sampling the resulting model space in order to find optimal models and inform future modelling efforts. We lay emphasis on physically informed features which capture the biological environment of the cleavage site, hence terming our approach piCRISPR. We find that our best-performing model highlights the importance of sequence context and chromatin accessibility for cleavage prediction and compares favourably with state-of-the-art prediction performance. We further show that our novel, environmentally sensitive features are crucial to accurate prediction on sequence-identical locus pairs, making them highly relevant for clinical guide design. We then turn our attention to the cell-intrinsic repair mechanisms that follow CRISPR/Cas-induced cleavage and provide a prediction algorithm for the outcome genotype distribution based on thermodynamic features of the DNA repair process. In a pioneering approach, we utilise structural calculations for the generation of these features and show that this novel approach surpasses published outcome prediction algorithms within our testing regime. Through interpretation of the trained model, we elucidate the thermodynamic factors driving DNA repair and provide a computational tool that allows experts to assess the severity of the genotypic changes predicted for a given edit. Together, these efforts provide a comprehensive, one-stop computational source to assess and improve CRISPR/Cas9 gene editing safety

Generalisable Methods for Improving CRISPR Efficiency and Outcome Specificity using Machine Learning Algorithms

CRISPR (clustered regularly interspaced short palindromic repeats) based genome editing has become a popular tool for a range of disciplines, including microbiology, agricultural science, and health. Driving these applications is the ability of the "programmable" system to target a predefined location in the genome. A single guide RNA (sgRNA) defines the target through Watson-Crick base pairing, and a class 2 type II CRISPR associated protein 9 (Cas9) nuclease cleaves the target, resulting in a double-strand break (DSB). This activates DNA repair, and depending on the repair pathway initiated, can result in arbitrary insertions/deletions or a predefined variant. Despite the versatility and ease of design enabled by this RNA-guided nuclease, it lacks specificity, regarding off-target effects, and efficiency, regarding the rate of successful editing outcomes. The overarching hypothesis of my thesis is to solve the disadvantages of CRISPR systems by using machine learning to train generalisable models on existing and novel datasets. One pathway that demonstrates the need for prediction models is homology directed repair (HDR). HDR enables researchers to induce nearly any editing outcome, however, it is inefficient. And with an incomplete knowledge of its kinetics, no models existed for predicting its efficiency. I generated a novel dataset representing the efficiency of HDR. Using the Random Forests algorithm, I identified the sgRNA and the 3' region of the template to modulate HDR efficiency. This novel finding relates to the kinetics of template interaction during HDR repair. Even with efficient gene editing, a potential problem is unwanted side effects, such as embryonic lethality. This can be solved by using CRISPR to create conditional knockout alleles, to control when and where knockouts occur. To investigate the efficiency of this process, I used statistical analyses and the Random Forest algorithm to analyse a dataset generated by a consortium of 19 laboratories. I identified the inherent inefficiency of this method as defined by the efficiency of two simultaneous HDR events. Other experimental variables, like reagent concentrations or technician skill level, had no significant influence on efficiency. Because of the unrivalled versatility of this method, I created a statistical model for forecasting the efficiency of this technique from a low number of attempts, aiming to overcome its inherent inefficiency. While Cas9 is the most cited CRISPR system, alternative CRISPR systems can further expand the gene editing repertoire. To support the uptake of the more-recent Cas12a, I performed a comprehensive comparison between the two nucleases. I found support for Cas12a having a superior specificity. Despite this, editing outcome and efficiency prediction tools for Cas12a were scarce. Aiming to address this, I trained a Cas12a cleavage efficiency prediction model on representative data. This outperformed the current top model despite the dataset being 300x smaller, demonstrating the importance of clean data. Altogether, this thesis improves the knowledge of different CRISPR gene editing techniques. These findings can enable researchers to design efficient experiments as well as provide researchers guidance where certain techniques may be inherently inefficient. As well as resulting in CUNE (Computational Universal Nucleotide Editor) and Cas12aRF, it also identifies the generalisability of prediction models due to the high degree of influence on efficiency by the sgRNA and repair template design

Text and data mining for human drug understanding

University of Technology Sydney. Faculty of Engineering and Information Technology.This research employs text and data mining methods to gain valuable knowledge for human drugs. Specifically, computational methods are developed for three topics, namely drug-side-effect prediction, drug-target identification, and drug-drug-interaction detection. The key innovations of the proposed methods lie in the feature space construction using medical domain knowledge, generation of reliable negative samples, and successful application of machine learning algorithms. The drug-side-effect prediction problems are studied in Chapters 3-5. Side-effects are secondary phenotypic responses of human organisms to drug treatments. Side-effect prediction is an important topic for drugs especially in post-marketing surveillance because they cause significant fatality and severe morbidity. To overcome the limitations of existing computational methods such as lack of proper drug representation and reliable negative samples, this thesis presents three novel methods. The first method is to predict side-effects for single drug medication as described in Chapter 3. A comprehensive drug similarity framework is developed by integrating several types of similarities measured by representative features of drugs first. Then reliable negative samples are generated through analyzing the comprehensive drug similarities. Trained with generated reliable negatives, the prediction performance of four classical classifiers are improved significantly, outperforming those state-of-the-art methods. Chapter 4 describes the method proposed to predict side-effects for combined medication of multi-drugs. A scoring method on a drug-disease-gene tripartite network is developed to prioritize interacting drugs, paving a way to generate credible negative samples for side-effect prediction of combined medication. It creatively characterized a drug with its chemical structures, target proteins, substituents, and enriched pathways. The drug-drug pairs are represented as novel feature vectors to train binary classifiers for prediction. This novel representation and the inferred negative samples contribute to the superior performance of the proposed method in drug-drug-side-effect association prediction. Chapter 5 introduces the last method for detecting adverse drug reactions (ADRs, i.e., side-effects) from medical forums. It filters the cause-result relationship between drugs and ADRs using a self-built dictionary and detects drug-ADRs associations by information entropy. Compared with conventional co-occurrence based methods, the proposed method captures both high-frequency and low-frequency ADRs simultaneously. Besides, it returns drug-related ADRs only owing to the self-built relation dictionary. Drug-target identification plays a crucial role in drug discovery. Existing computational methods have achieved remarkable prediction accuracy, however, usually obtain poor prediction efficiency due to computational problems. Chapter 6 presents a method to improve the prediction efficiency using an advanced technique named anchor graph hashing (AGH). AGH embeds data into low-dimensional Hamming space while maintaining the neighbourship. It turns the drug-target identification problem into a binary classification task where inputs are AGH-embedded vectors of drug-target pairs, and labels are judgments of their associations. Ensemble learning with random forest and XGBoost is employed to learn a good decision boundary. The proposed method is demonstrated to be the most efficient method and achieves comparable prediction accuracy with the best literature method. Chapter 7 introduces a novel positive-unlabeled learning method named DDI-PULearn for large-scale detection of drug-drug interactions (DDIs). DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify the set of entire reliable negatives from the unlabeled samples. The identified negatives and validated positives are represented as vectors using the bit-wise similarity of corresponding drug pairs to train random forest for prediction. Its excellent performance is confirmed by comparing with two baseline methods and five state-of-the-art methods

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scien