Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Decoding function through comparative genomics: from animal evolution to human disease

Deciphering the functionality encoded in the genome constitutes an essential first step to understanding the context through which mutations can cause human disease. In this dissertation, I present multiple studies based on the use or development of comparative genomics techniques to elucidate function (or lack of function) from the genomes of humans and other animal species. Collectively, these studies focus on two biological entities encoded in the human genome: genes related to human disease susceptibility and those that encode microRNAs - small RNAs that have important gene-regulatory roles in normal biological function and in human disease. Extending this work, I investigated the evolution of these biological entities within animals to shed light on how their underlying functions arose and how they can be modeled in non-human species. Additionally, I present a new tool that uses large-scale clinical genomic data to identify human mutations that may affect microRNA regulatory functions, thereby providing a method by which state-of-the-art genomic technologies can be fully utilized in the search for new disease mechanisms and potential drug targets. The scientific contributions made in this dissertation utilize current data sets generated using high-throughput sequencing technologies. For example, recent whole-genome sequencing studies of the most distant animal lineages have effectively restructured the animal tree of life as we understand it. The first two chapters utilize data from this new high-confidence animal phylogeny - in addition to data generated in the course of my work - to demonstrate that (1) certain classes of human disease have uncommonly large proportions of genes that evolved with the earliest animals and/or vertebrates, and (2) that canonical microRNA functionality - absent in at least two of the early branching animal lineages - likely evolved after the first animals. In the third chapter, I expand upon recent research in predicting microRNA target sites, describing a novel tool for predicting clinically significant microRNA target site variants and demonstrating its applicability to the analysis of clinical genomic data. Thus, the studies detailed in this dissertation represent significant advances in our understanding of the functions of disease genes and microRNAs from both an evolutionary and a clinical perspective

Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks

Cellular senescence is a barrier to tumorigenesis in normal cells and tumour cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. 147 virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase (SA-β-gal) assays. Among the found hits a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced SA-β-gal activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1 and CDC25C. Additionally, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long term treatments. Preliminary structure-activity and structure clustering analyses are reported and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells

Inference and Analysis of Multilayered Mirna-Mediated Networks in Cancer

MicroRNAs (miRNAs) are small noncoding transcripts that can regulate gene expression, thereby controlling diverse biological processes. Aberrant disruptions of miRNA expression and their interactions with other biological agents (e.g., coding and noncoding transcripts) have been associated with several types of cancer. The goal of this dissertation is to use multidimensional genomic data to model two different gene regulation mechanisms by miRNAs in cancer. This dissertation results from two research projects. The first project investigates a miRNA-mediated gene regulation mechanism called competing endogenous RNA (ceRNA) interactions, which suggests that some transcripts can indirectly regulate one another\u27s activity through their interactions with a common set of miRNAs. Identification of context-specific ceRNA interactions is a challenging task. To address that, we proposed a computational method called Cancerin to identify genome-wide cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation (DM), copy number alteration (CNA), and gene and miRNA expression datasets to construct cancer-specific ceRNA networks. Cancerin was applied to three cancer datasets from the Cancer Genome Atlas (TCGA) project. We found that the RNAs involved in ceRNA interactions were enriched with cancer-related genes and have high prognostic power. Moreover, the ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. The second project investigates what biological functions are regulated by both miRNAs and transcription factors (TFs). While it has been known that miRNAs and TFs can coregulate common target genes having similar biological functions, it is challenging to associate specific biological functions to specific miRNAs and TFs. In this project, we proposed a computational method called CanMod to identify gene regulatory modules. Each module consists of miRNAs, TFs and their coregulated target genes. CanMod was applied on the breast cancer dataset from TCGA. Many hub regulators (i.e., miRNAs and TFs) found in the inferred modules were known cancer genes, and CanMod was able to find experimentally validated regulator-target interactions. In addition, the modules were associated with distinguishable and cancer-related biological processes. Given the biological findings obtained from Cancerin and CanMod, we believe that the two computational methods are valuable tools to explore novel miRNA involvement in cancer

생물학적 서열 데이터에 대한 표현 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 윤성로.As we are living in the era of big data, the biomedical domain is not an exception. With the advent of technologies such as next-generation sequencing, developing methods to capitalize on the explosion of biomedical data is one of the most major challenges in bioinformatics. Representation learning, in particular deep learning, has made significant advancements in diverse fields where the artificial intelligence community has struggled for many years. However, although representation learning has also shown great promises in bioinformatics, it is not a silver bullet. Off-the-shelf applications of representation learning cannot always provide successful results for biological sequence data. There remain full of challenges and opportunities to be explored. This dissertation presents a set of representation learning methods to address three issues in biological sequence data analysis. First, we propose a two-stage training strategy to address throughput and information trade-offs within wet-lab CRISPR-Cpf1 activity experiments. Second, we propose an encoding scheme to model interaction between two sequences for functional microRNA target prediction. Third, we propose a self-supervised pre-training method to bridge the exponentially growing gap between the numbers of unlabeled and labeled protein sequences. In summary, this dissertation proposes a set of representation learning methods that can derive invaluable information from the biological sequence data.우리는 빅데이터의 시대를 맞이하고 있으며, 의생명 분야 또한 예외가 아니다. 차세대 염기서열 분석과 같은 기술들이 도래함에 따라, 폭발적인 의생명 데이터의 증가를 활용하기 위한 방법론의 개발은 생물정보학 분야의 주요 과제 중의 하나이다. 심층 학습을 포함한 표현 학습 기법들은 인공지능 학계가 오랫동안 어려움을 겪어온 다양한 분야에서 상당한 발전을 이루었다. 표현 학습은 생물정보학 분야에서도 많은 가능성을 보여주었다. 하지만 단순한 적용으로는 생물학적 서열 데이터 분석의 성공적인 결과를 항상 얻을 수는 않으며, 여전히 연구가 필요한 많은 문제들이 남아있다. 본 학위논문은 생물학적 서열 데이터 분석과 관련된 세 가지 사안을 해결하기 위해, 표현 학습에 기반한 일련의 방법론들을 제안한다. 첫 번째로, 유전자가위 실험 데이터에 내재된 정보와 수율의 균형에 대처할 수 있는 2단계 학습 기법을 제안한다. 두 번째로, 두 염기 서열 간의 상호 작용을 학습하기 위한 부호화 방식을 제안한다. 세 번째로, 기하급수적으로 증가하는 특징되지 않은 단백질 서열을 활용하기 위한 자기 지도 사전 학습 기법을 제안한다. 요약하자면, 본 학위논문은 생물학적 서열 데이터를 분석하여 중요한 정보를 도출할 수 있는 표현 학습에 기반한 일련의 방법론들을 제안한다.1 Introduction 1 1.1 Motivation 1 1.2 Contents of Dissertation 4 2 Background 8 2.1 Representation Learning 8 2.2 Deep Neural Networks 12 2.2.1 Multi-layer Perceptrons 12 2.2.2 Convolutional Neural Networks 14 2.2.3 Recurrent Neural Networks 16 2.2.4 Transformers 19 2.3 Training of Deep Neural Networks 23 2.4 Representation Learning in Bioinformatics 26 2.5 Biological Sequence Data Analyses 29 2.6 Evaluation Metrics 32 3 CRISPR-Cpf1 Activity Prediction 36 3.1 Methods 39 3.1.1 Model Architecture 39 3.1.2 Training of Seq-deepCpf1 and DeepCpf1 41 3.2 Experiment Results 44 3.2.1 Datasets 44 3.2.2 Baselines 47 3.2.3 Evaluation of Seq-deepCpf1 49 3.2.4 Evaluation of DeepCpf1 51 3.3 Summary 55 4 Functional microRNA Target Prediction 56 4.1 Methods 62 4.1.1 Candidate Target Site Selection 63 4.1.2 Input Encoding 64 4.1.3 Residual Network 67 4.1.4 Post-processing 68 4.2 Experiment Results 70 4.2.1 Datasets 70 4.2.2 Classification of Functional and Non-functional Targets 71 4.2.3 Distinguishing High-functional Targets 73 4.2.4 Ablation Studies 76 4.3 Summary 77 5 Self-supervised Learning of Protein Representations 78 5.1 Methods 83 5.1.1 Pre-training Procedure 83 5.1.2 Fine-tuning Procedure 86 5.1.3 Model Architecturen 87 5.2 Experiment Results 90 5.2.1 Experiment Setup 90 5.2.2 Pre-training Results 92 5.2.3 Fine-tuning Results 93 5.2.4 Comparison with Larger Protein Language Models 97 5.2.5 Ablation Studies 100 5.2.6 Qualitative Interpreatation Analyses 103 5.3 Summary 106 6 Discussion 107 6.1 Challenges and Opportunities 107 7 Conclusion 111 Bibliography 113 Abstract in Korean 130박

One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases

The study of MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and gene interactions may be expected to provide new technologies to serve as valuable biomarkers for personalized treatments of diseases and to aid in the prognosis of certain conditions. These molecules act at the genome level by regulating or suppressing their protein expression functions. The primary challenge in the study of these non-coding molecules involves the necessity of finding labeled data indicating positive and negative interactions when predicting interactions using machine-learning or deep-learning techniques. However, usually we end up with a scenario of unbalanced data or unstable scenarios for using these models. An additional problem involves the extraction of features derived from the binding of these non-coding RNAs and genes. This binding process usually occurs fully or partially in animal genetics, which leads to considerable complexity in studying the process. Therefore, the main objective of the present work is to demonstrate that it is possible to use features extracted for miRNAs sequences in the development of diseases such as breast cancer, breast neoplasms, or if there is any influence with immune genes related to the SARS-COV-2. We performed experiments focusing on the erb-b2 receptor tyrosine kinase 2 (ERBB2) gene involved in breast cancer. For this purpose, we gathered miRNA-mRNA information from the binding between these two genetic molecules. In this part of our research, we applied a One-Class SVM and an Isolation Forest to discriminate between weak interactions, outliers given by the one-class model, and strong interactions that could occur between miRNA and mRNA (messenger RNA). Additionally, this study aimed to differentiate between breast cancer cases and breast neoplasm conditions. In this section we used the information encoded in lncRNAs. The additional feature used in this part was the frequency of k-mers, i.e., small portions of nucleotides, along with the data from the energy released in miRNA folding. The models used to discriminate between these diseases were One-Class SVM, SVM, and Random Forest. In the final part of the present work, we described a subset of probable miRNA binding with SARS-COV-2 RNA, focusing on those miRNAs with a relationship with genes involved in the immunological system of the human body. The models used as classifiers were One-Class SVM, SVM, and Random Forest. The results obtained in the present study are comparable to those found in the current literature and demonstrate the feasibility of using one-class models combined with features from the coupling of non-coding genes or mRNAs and their relationships with forms of breast cancer and viral infections. This work is expected to establish a basis for future avenues of research to apply one-class machine-learning models with feature extraction based on genomic sequences to the study of the relationship between non-coding RNAs and various diseases.School of ComputingPh. D. (Computing

Leveraging big data resources and data integration in biology: applying computational systems analyses and machine learning to gain insights into the biology of cancers

Recently, many "molecular profiling" projects have yielded vast amounts of genetic, epigenetic, transcription, protein expression, metabolic and drug response data for cancerous tumours, healthy tissues, and cell lines. We aim to facilitate a multi-scale understanding of these high-dimensional biological data and the complexity of the relationships between the different data types taken from human tumours. Further, we intend to identify molecular disease subtypes of various cancers, uncover the subtype-specific drug targets and identify sets of therapeutic molecules that could potentially be used to inhibit these targets. We collected data from over 20 publicly available resources. We then leverage integrative computational systems analyses, network analyses and machine learning, to gain insights into the pathophysiology of pancreatic cancer and 32 other human cancer types. Here, we uncover aberrations in multiple cell signalling and metabolic pathways that implicate regulatory kinases and the Warburg effect as the likely drivers of the distinct molecular signatures of three established pancreatic cancer subtypes. Then, we apply an integrative clustering method to four different types of molecular data to reveal that pancreatic tumours can be segregated into two distinct subtypes. We define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Then we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to infer the drug sensitivity of pancreatic cancer cell lines accurately. Further, we evaluate the alterations of metabolic pathway genes across 32 human cancers. We find that while alterations of metabolic genes are pervasive across all human cancers, the extent of these gene alterations varies between them. Based on these gene alterations, we define two distinct cancer supertypes that tend to be associated with different clinical outcomes and show that these supertypes are likely to respond differently to anticancer drugs. Overall, we show that the time has already arrived where we can leverage available data resources to potentially elicit more precise and personalised cancer therapies that would yield better clinical outcomes at a much lower cost than is currently being achieved