Encounter complexes and dimensionality reduction in protein-protein association

An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of {\tt MATS} are presented and the implemented measures are briefly described. The usefulness of {\tt MATS} is illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at http://eeganalysis.web.auth.gr/indexen.ht

Finding and Visualizing Relevant Subspaces for Clustering High-Dimensional Astronomical Data Using Connected Morphological Operators

Data sets in many scientific areas are growing to enormous sizes. For example, modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Gene expression ex-periments produce data about the complete genome of an organism under different conditions and at a sequence of time points. Ex-ploration of such very high-dimensional data spaces poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. How-ever, many clustering algorithms require the user to set a large num-ber of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clus-ters present or the significance of the clusters

A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

Worldwide cities are becoming more sustainable and are being monitored using data collection techniques at various geographical levels. Given the growing volume of data, there is a need to identify challenges associated with the processing, visualization, and analysis of the generated data from an urban scale. This study proposes a framework to investigate the capabilities of dimensionality reduction techniques (t-SNE, and UMAP) applied to city-scale data to identify key features of high consumption and generation areas based on building characteristics. The analysis is performed on measured data from 2735 postcodes consisting of 72000 households/buildings from a city in the Netherlands. The evaluation results showed that the UMAP's algorithm mean sigma quickly approaches a threshold of 0.6 at n_neighbor values of 50 and the low dimensional shape does not change with increasing values. Whereas the t-SNE's mean sigma value increases continuously with the increasing perplexity value, implying that t-SNE is significantly more sensitive to the perplexity parameter. The UMAP algorithm was used to extract information about the high photovoltaic generation and consumption regions. The proposed framework will assist grid operators and energy planners in extracting information from energy consumption data at the neighbourhood level by utilizing high dimensional reduction techniques

RNA 시퀀싱 데이터의 해독과 활용을 위한 기계학습 기법

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.진핵 세포 시스템에서는 mRNA 분자가 전사된 이후 완전히 처리되어 단백질로 번역될 때까지 여러 단계의 전사 후 조절 과정을 거치게 된다. 전사 후 조절 과정은 RNA 편집, 선택적 접합, 선택적 아데닐화 등을 포함한다. 즉 어느 한 시점에서 전사체를 들여다보면 그 내부는 다양한 중간체들의 혼합물로 구성되어 있는 것이다. 이러한 복잡한 조절 시스템 때문에 전사체를 전체적인 수준에서 이해하기가 쉽지 않다. 본 학위 연구는 RNA 시퀀싱 데이터를 해독하고 활용하기 위한 기계학습 기법들에 대한 연구이며 RNA 편집, 선택적 접합 및 유전자 발현의 관점에서 수행된 세 가지 연구로 구성된다. RNA 편집은 ADAR(A=>I) 과 APOBEC(C=>U) 두 가지 효소에 의해 촉매 되는 전사 후 RNA 서열 조절 기작이다. RNA 편집은 단백질 활성도, 선택적 접합 및 miRNA 표적 조절 등 다양한 세포 기작을 제어하는 것으로 알려진 중요한 새포 내 조절 시스템이다. RNA 시퀀싱을 이용해 RNA 편집 현상을 검출하는 것은 RNA 편집 현상의 생물학적 기능을 이해하는 데에 매우 중요하다. 문제는 이 과정에서 상당한 양의 위양성이 발생한다는 점이다. 샘플당 수만 개 이상 발생하는 RNA 편집 잔기들 모두를 실험적으로 검증할 수 없기 때문에 이를 걸러내기 위한 전산학적 모델이 요구된다. RDDpred는 RNA 시퀀싱 데이터로부터 RNA 편집 현상을 검출하는 과정에서 발생하는 위양성 잔기들을 기계학습 기술에 기반하여 구분하는 모델이다. RDDpred는 두 개의 기 발표된 RNA 편집 연구 데이터를 이용하여 검증되었다. RNA 시퀀싱 기술이 활용될 수 있는 또 하나의 복잡한 문제로 접합체 차원에서의 종양 이질성 (ITH) 측정 문제가 있다. ITH는 암 조직을 구성하는 세포 집단의 다양성의 지표이며, 최근 출판된 연구들의 결과는 유전자 발현량 데이터에 기반하여 측정된 전사체 수준에서의 ITH가 암 환자의 예후예측에 유용함을 시사한다. 접합체는 유전자 발현량과 함께 전사체를 구성하는 주요 요소 중 하나이며 따라서 접합체 수준에서 ITH를 측정하는 것은 보다 전체적인 수준에서 전사체 ITH를 연구하기 위한 자연스러운 흐름이다. RNA 시퀀싱 데이터를 이용하여 암 접합체 수준에서 ITH를 측정하는 과정에는 복잡한 접합 패턴과 광범위한 인트론 연장 변이 및 짧은 시퀀싱 판독 길이 등의 심각한 기술적 난관들이 있다. SpliceHetero는 이러한 문제들을 고려하여 접합체 수준에서의 ITH (즉, sITH)를 측정하기 위한 도구이며 내부적으로 정보이론을 활용한다. SpliceHetero는 시뮬레이션 데이터, 이종이식 종양 데이터 및 TCGA pan-cancer 데이터 등을 활용하여 광범위하게 검증되었으며 ITH를 잘 반영하는 것으로 확인되었다. 이뿐 아니라 sITH는 암의 진행과 암 환자의 예후 및 PAM50와 같은 잘 알려진 분자 아형들과도 높은 상관관계를 가지는 것으로 확인되었다. 마지막 연구 주제는 유전자 발현량 데이터에 기반하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의하는 기계학습 알고리즘을 개발하는 것이다. RNA 시퀀싱 데이터는 암 환자의 유전자 발현량 프로파일을 얻는 데에 유용한 도구이지만, 2만 개 이상의 차원을 가진 매우 고차원의 데이터이기 때문에 실질적인 용도로 사용되기 위해서는 그 차원의 크기를 축소할 필요가 있다. 이때 각 유전자들은 복잡하지만 고유한 방식으로 서로 상호작용한다는 점을 이용할 수 있다. 실험적으로 검증된 단백질 간의 상호작용 정보를 모아 네트워크 형태로 묶은 것을 단백질 상호작용 네트워크 (혹은 PIN)라 부른다. 이 PIN을 활용하여 RNA 시퀀싱 데이터의 차원을 줄이면서도 데이터로부터 생물학적으로 유의미한 특징들을 추출할 수 있다. Tumor2Vec은 이렇게 추출된 PIN 수준의 특징들을 활용하여 특정 암 표현형에 특이적인 환자 부분 공간을 정의한다. Tumor2Vec은 조기 구강 암에서 림프절 전이를 예측하기 위한 파일럿 연구에 적용되었으며 그 결과 RNA 시퀀싱 데이터의 차원을 줄여 림프절 전이 예측 모델을 생성했고 이 과정에서 암 표현형을 잘 설명하는 PIN 수준의 특징들을 보존하는 데에도 성공했다.In eukaryotic cells, there are several post-transcriptional modification steps such as RNA editing and alternative splicing, until mRNA molecules are fully matured and translated into proteins. Thus, the transcriptome is a complex mixture of various intermediates that are processed in multiple steps. This complex regulatory structure makes it difficult to fully understand the landscape of transcriptome. My doctoral study consists of three studies that enable RNA-seq to be decoded and utilized in terms of RNA editing, alternative splicing, and gene expression. RNA editing is a post-transcriptional RNA sequence modification performed by two catalytic enzymes ADAR (A-to-I) and APOBEC (C-to-U). RNA editing is considered an important regulatory system that controls a variety of cellular functions such as protein activation, alternative splicing, and miRNA targeting. Therefore, detecting RNA editing events in RNA-seq data is important for understanding its biological functions. However, it is known that a significant amount of false-positives occur when detecting RNA editing in RNA-seq. Since it is not possible to experimentally validate all RNA editing residues extracted from RNA-seq, a computational model is needed to filter potential false-positive RNA editing calls. RDDpred, an RNA editing predictor based on machine learning techniques, was developed to filter out false-positive RNA editing calls in RNA-seq. It uses prior knowledge bases to collect training instances directly from the input data, and then trains the random forest (RF) predictors that are specific to the input data. RDDpred was tested using two publicly available datasets of RNA editing studies and has shown good performance. Another complex problem in RNA-seq decoding is spliceomic intratumor heterogeneity (ie, sITH). Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up the cancer tissue. Recent studies have identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There is a serious technical challenge in measuring sITH from bulk tumor RNA-seq, such as complex splicing patterns, widespread intron retentions, and short sequencing read lengths. SpliceHetero, an information-theoretic method for measuring the sITH of a tumor, was developed to address the aforementioned technical problems. SpliceHetero was extensively tested in experiments using synthetic data, xenograft tumor data and TCGA pan-cancer data and measured sITH successfully. Also, sITH was shown to be closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome, and PAM50 subtype. The last research topic is to develop a machine learning algorithm that defines patient subspaces specific to particular cancer phenotypes based on gene expression data. Since RNA-seq data is high-dimensional data composed of 20,000 or more genes in general, it is not easy to apply a machine learning algorithm. A network that collects information of experimentally verified interaction of proteins is called a Protein Interaction Network (PIN). Tumor2Vec defines the patient subspace by defining the subnetwork communities that interact with each other by applying the Graph Embedding technique to PIN. Tumor2Vec proposed a clinical model by defining a subspace for patients with different lymph node metastases in early oral cancer and found biologically significant features in the PIN subnetwork unit in the process.Chapter 1 Introduction 1 1.1 Biological background . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Challenges in decoding and utilizing RNA-seq data . . . . . . . . 5 1.2.1 false-positives in RNA editing calls . . . . . . . . . . . . . 6 1.2.2 Absence of a model for measuring spliceomic intratumor heterogeneity considering complex cancer spliceome . . . 6 1.2.3 Lack of biological interpretation of dimension reduction techniques using gene expression . . . . . . . . . . . . . . 8 1.3 Machine learning techniques to solve difficulties in using RNA-seq 9 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 RDDpred: A condition specific machine learning model for filtering false-positive RNA editing calls in RNAseq data 11 2.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5.1 Design of experiments for evaluation . . . . . . . . . . . . 18 2.5.2 Evaluation using data from Bahn et al. . . . . . . . . . . 19 2.5.3 Evaluation using data from Peng et al. . . . . . . . . . . . 19 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 3 SpliceHetero: An information-theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq data 24 3.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.2 Xenograft tumor data . . . . . . . . . . . . . . . . . . . . 36 3.5.3 TCGA pan-cancer data . . . . . . . . . . . . . . . . . . . 38 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 4 Tumor2Vec: A supervised learning algorithm for extracting subnetwork representations of cancer RNAseq data using protein interaction networks 48 4.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Lymph node metastasis in early oral cancer . . . . . . . . 57 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 5 Conclusion 62 초록 78Docto