The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

Algorithms for pre-microrna classification and a GPU program for whole genome comparison

MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpin can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). The first part of this dissertation presents a new method, called MirID, for identifying and classifying microRNA precursors. MirID is comprised of three steps. Initially, a combinatorial feature mining algorithm is developed to identify suitable feature sets. Then, the feature sets are used to train support vector machines to obtain classification models, based on which classifier ensemble is constructed. Finally, an AdaBoost algorithm is adopted to further enhance the accuracy of the classifier ensemble. Experimental results on a variety of species demonstrate the good performance of the proposed approach, and its superiority over existing methods. In the second part of this dissertation, A GPU (Graphics Processing Unit) program is developed for whole genome comparison. The goal for the research is to identify the commonalities and differences of two genomes from closely related organisms, via multiple sequencing alignments by using a seed and extend technique to choose reliable subsets of exact or near exact matches, which are called anchors. A rigorous method named Smith-Waterman search is applied for the anchor seeking, but takes days and months to map millions of bases for mammalian genome sequences. With GPU programming, which is designed to run in parallel hundreds of short functions called threads, up to 100X speed up is achieved over similar CPU executions

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

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

An integrated network of Arabidopsis growth regulators and its use for gene prioritization

Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs