100 research outputs found

    Packing Cars into Narrow Roads: PTASs for Limited Supply Highway

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    In the Highway problem, we are given a path with n edges (the highway), and a set of m drivers, each one characterized by a subpath and a budget. For a given assignment of edge prices (the tolls), the highway owner collects from each driver the total price of the associated path when it does not exceed drivers\u27s budget, and zero otherwise. The goal is to choose the prices to maximize the total profit. A PTAS is known for this (strongly NP-hard) problem [Grandoni,Rothvoss-SODA\u2711, SICOMP\u2716]. In this paper we study the limited supply generalization of Highway, that incorporates capacity constraints. Here the input also includes a capacity u_e >= 0 for each edge e; we need to select, among drivers that can afford the required price, a subset such that the number of drivers that use each edge e is at most u_e (and we get profit only from selected drivers). To the best of our knowledge, the only approximation algorithm known for this problem is a folklore O(log m) approximation based on a reduction to the related Unsplittable Flow on a Path problem (UFP). The main result of this paper is a PTAS for limited supply highway. As a second contribution, we study a natural generalization of the problem where each driver i demands a different amount d_i of capacity. Using known techniques, it is not hard to derive a QPTAS for this problem. Here we present a PTAS for the case that drivers have uniform budgets. Finding a PTAS for non-uniform-demand limited supply highway is left as a challenging open problem

    Prizing on Paths: A PTAS for the Highway Problem

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    In the highway problem, we are given an n-edge line graph (the highway), and a set of paths (the drivers), each one with its own budget. For a given assignment of edge weights (the tolls), the highway owner collects from each driver the weight of the associated path, when it does not exceed the budget of the driver, and zero otherwise. The goal is choosing weights so as to maximize the profit. A lot of research has been devoted to this apparently simple problem. The highway problem was shown to be strongly NP-hard only recently [Elbassioni,Raman,Ray-'09]. The best-known approximation is O(\log n/\log\log n) [Gamzu,Segev-'10], which improves on the previous-best O(\log n) approximation [Balcan,Blum-'06]. In this paper we present a PTAS for the highway problem, hence closing the complexity status of the problem. Our result is based on a novel randomized dissection approach, which has some points in common with Arora's quadtree dissection for Euclidean network design [Arora-'98]. The basic idea is enclosing the highway in a bounding path, such that both the size of the bounding path and the position of the highway in it are random variables. Then we consider a recursive O(1)-ary dissection of the bounding path, in subpaths of uniform optimal weight. Since the optimal weights are unknown, we construct the dissection in a bottom-up fashion via dynamic programming, while computing the approximate solution at the same time. Our algorithm can be easily derandomized. We demonstrate the versatility of our technique by presenting PTASs for two variants of the highway problem: the tollbooth problem with a constant number of leaves and the maximum-feasibility subsystem problem on interval matrices. In both cases the previous best approximation factors are polylogarithmic [Gamzu,Segev-'10,Elbassioni,Raman,Ray,Sitters-'09]

    Developing graph-based co-scheduling algorithms on multicore computers

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    It is common that multiple cores reside on the same chip and share the on-chip cache. As a result, resource sharing can cause performance degradation of co-running jobs.Job co-scheduling is a technique that can effectively alleviate this contention and many co-schedulers have been reported in related literature. Most solutions however do not aim to find the optimal co-scheduling solution. Being able to determine the optimal solution is critical for evaluating co-scheduling systems. Moreover, most co-schedulers only consider serial jobs, and there often exist both parallel and serial jobs in real-world systems. In this paper a graph-based method is developed to find the optimal co-scheduling solution for serial jobs; the method is then extended to incorporate parallel jobs, including multi-process, and multithreaded parallel jobs. A number of optimization measures are also developed to accelerate the solving process. Moreover, a flexible approximation technique is proposed to strike a balance between the solving speed and the solution quality. Extensive experiments are conducted to evaluate the effectiveness of the proposed co-scheduling algorithms. The results show that the proposed algorithms can find the optimal co-scheduling solution for both serial and parallel jobs. The proposed approximation technique is also shown to be flexible in the sense that we can control the solving speed by setting the requirement for the solution quality

    Assembly of long error-prone reads using de Bruijn graphs

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    The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions

    RNA μƒν˜Έμž‘μš© 및 DNA μ„œμ—΄μ˜ 정보해독을 μœ„ν•œ κΈ°κ³„ν•™μŠ΅ 기법

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    ν•™μœ„λ…Όλ¬Έ(박사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :κ³΅κ³ΌλŒ€ν•™ 컴퓨터곡학뢀,2020. 2. κΉ€μ„ .생물체 κ°„ ν‘œν˜„ν˜•μ˜ μ°¨μ΄λŠ” 각 개체의 μœ μ „μ  정보 μ°¨μ΄λ‘œλΆ€ν„° κΈ°μΈν•œλ‹€. μœ μ „μ  μ •λ³΄μ˜ 변화에 λ”°λΌμ„œ, 각 μƒλ¬Όμ²΄λŠ” μ„œλ‘œ λ‹€λ₯Έ μ’…μœΌλ‘œ μ§„ν™”ν•˜κΈ°λ„ ν•˜κ³ , 같은 병에 κ±Έλ¦° ν™˜μžλΌλ„ μ„œλ‘œ λ‹€λ₯Έ μ˜ˆν›„λ₯Ό 보이기도 ν•œλ‹€. 이처럼 μ€‘μš”ν•œ 생물학적 μ •λ³΄λŠ” λŒ€μš©λŸ‰ μ‹œν€€μ‹± 뢄석 기법 등을 톡해 λ‹€μ–‘ν•œ 였믹슀 λ°μ΄ν„°λ‘œ μΈ‘μ •λœλ‹€. κ·ΈλŸ¬λ‚˜, 였믹슀 λ°μ΄ν„°λŠ” 고차원 νŠΉμ§• 및 μ†Œκ·œλͺ¨ ν‘œλ³Έ 데이터이기 λ•Œλ¬Έμ—, 였믹슀 λ°μ΄ν„°λ‘œλΆ€ν„° 생물학적 정보λ₯Ό ν•΄μ„ν•˜λŠ” 것은 맀우 μ–΄λ €μš΄ λ¬Έμ œμ΄λ‹€. 일반적으둜, 데이터 νŠΉμ§•μ˜ κ°œμˆ˜κ°€ μƒ˜ν”Œμ˜ κ°œμˆ˜λ³΄λ‹€ λ§Žμ„ λ•Œ, 였믹슀 λ°μ΄ν„°μ˜ 해석을 κ°€μž₯ λ‚œν•΄ν•œ κΈ°κ³„ν•™μŠ΅ λ¬Έμ œλ“€ 쀑 ν•˜λ‚˜λ‘œ λ§Œλ“­λ‹ˆλ‹€. λ³Έ λ°•μ‚¬ν•™μœ„ 논문은 κΈ°κ³„ν•™μŠ΅ 기법을 ν™œμš©ν•˜μ—¬ 고차원적인 생물학적 λ°μ΄ν„°λ‘œλΆ€ν„° 생물학적 정보λ₯Ό μΆ”μΆœν•˜κΈ° μœ„ν•œ μƒˆλ‘œμš΄ 생물정보학 방법듀을 κ³ μ•ˆν•˜λŠ” 것을 λͺ©ν‘œλ‘œ ν•œλ‹€. 첫 번째 μ—°κ΅¬λŠ” DNA μ„œμ—΄μ„ ν™œμš©ν•˜μ—¬ μ’… κ°„ 비ꡐ와 λ™μ‹œμ— DNA μ„œμ—΄μƒμ— μžˆλŠ” λ‹€μ–‘ν•œ 지역에 λ‹΄κΈ΄ 생물학적 정보λ₯Ό μœ μ „μ  κ΄€μ μ—μ„œ ν•΄μ„ν•΄λ³΄κ³ μž ν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄, μˆœμœ„ 기반 k 단어 λ¬Έμžμ—΄ 비ꡐ방법, RKSS 컀널을 κ°œλ°œν•˜μ—¬ λ‹€μ–‘ν•œ κ²Œλ†ˆ μƒμ˜ μ§€μ—­μ—μ„œ μ—¬λŸ¬ μ’… κ°„ 비ꡐ μ‹€ν—˜μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. RKSS 컀널은 기쑴의 k 단어 λ¬Έμžμ—΄ 컀널을 ν™•μž₯ν•œ κ²ƒμœΌλ‘œ, k 길이 λ‹¨μ–΄μ˜ μˆœμœ„ 정보와 μ’… κ°„ 곡톡점을 ν‘œν˜„ν•˜λŠ” 비ꡐ기쀀점 κ°œλ…μ„ ν™œμš©ν•˜μ˜€λ‹€. k 단어 λ¬Έμžμ—΄ 컀널은 k의 길이에 따라 단어 μˆ˜κ°€ κΈ‰μ¦ν•˜μ§€λ§Œ, 비ꡐ기쀀점은 κ·Ήμ†Œμˆ˜μ˜ λ‹¨μ–΄λ‘œ 이루어져 μžˆμœΌλ―€λ‘œ μ„œμ—΄ κ°„ μœ μ‚¬λ„λ₯Ό κ³„μ‚°ν•˜λŠ” 데 ν•„μš”ν•œ κ³„μ‚°λŸ‰μ„ 효율적으둜 쀄일 수 μžˆλ‹€. κ²Œλ†ˆ μƒμ˜ μ„Έ 지역에 λŒ€ν•΄μ„œ μ‹€ν—˜μ„ μ§„ν–‰ν•œ κ²°κ³Ό, RKSS 컀널은 기쑴의 컀널에 λΉ„ν•΄ μ’… κ°„ μœ μ‚¬λ„ 및 차이λ₯Ό 효율적으둜 계산할 수 μžˆμ—ˆλ‹€. λ˜ν•œ, RKSS 컀널은 μ‹€ν—˜μ— μ‚¬μš©λœ 생물학적 지역에 ν¬ν•¨λœ 생물학적 μ •λ³΄λŸ‰ 차이λ₯Ό 생물학적 지식과 λΆ€ν•©λ˜λŠ” μˆœμ„œλ‘œ 비ꡐ할 수 μžˆμ—ˆλ‹€. 두 번째 μ—°κ΅¬λŠ” 생물학적 λ„€νŠΈμ›Œν¬λ₯Ό 톡해 λ³΅μž‘ν•˜κ²Œ μ–½νžŒ μœ μ „μž μƒν˜Έμž‘μš© κ°„ 정보λ₯Ό ν•΄μ„ν•˜μ—¬, 더 λ‚˜μ•„κ°€ 생물학적 κΈ°λŠ₯ 해석을 톡해 μ•”μ˜ μ•„ν˜•μ„ λΆ„λ₯˜ν•˜κ³ μž ν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄, κ·Έλž˜ν”„ μ»¨λ³Όλ£¨μ…˜ λ„€νŠΈμ›Œν¬μ™€ μ–΄ν…μ…˜ λ©”μ»€λ‹ˆμ¦˜μ„ ν™œμš©ν•˜μ—¬ νŒ¨μŠ€μ›¨μ΄ 기반 해석 κ°€λŠ₯ν•œ μ•” μ•„ν˜• λΆ„λ₯˜ λͺ¨λΈ(GCN+MAE)을 κ³ μ•ˆν•˜μ˜€λ‹€. κ·Έλž˜ν”„ μ»¨λ³Όλ£¨μ…˜ λ„€νŠΈμ›Œν¬λ₯Ό ν†΅ν•΄μ„œ 생물학적 사전 지식인 νŒ¨μŠ€μ›¨μ΄ 정보λ₯Ό ν•™μŠ΅ν•˜μ—¬ λ³΅μž‘ν•œ μœ μ „μž μƒν˜Έμž‘μš© 정보λ₯Ό 효율적으둜 λ‹€λ£¨μ—ˆλ‹€. λ˜ν•œ, μ—¬λŸ¬ νŒ¨μŠ€μ›¨μ΄ 정보λ₯Ό μ–΄ν…μ…˜ λ©”μ»€λ‹ˆμ¦˜μ„ 톡해 해석 κ°€λŠ₯ν•œ μˆ˜μ€€μœΌλ‘œ λ³‘ν•©ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, ν•™μŠ΅ν•œ νŒ¨μŠ€μ›¨μ΄ 레벨 정보λ₯Ό 보닀 λ³΅μž‘ν•˜κ³  λ‹€μ–‘ν•œ μœ μ „μž 레벨둜 효율적으둜 μ „λ‹¬ν•˜κΈ° μœ„ν•΄μ„œ λ„€νŠΈμ›Œν¬ μ „νŒŒ μ•Œκ³ λ¦¬μ¦˜μ„ ν™œμš©ν•˜μ˜€λ‹€. λ‹€μ„― 개의 μ•” 데이터에 λŒ€ν•΄ GCN+MAE λͺ¨λΈμ„ μ μš©ν•œ κ²°κ³Ό, 기쑴의 μ•” μ•„ν˜• λΆ„λ₯˜ λͺ¨λΈλ“€λ³΄λ‹€ λ‚˜μ€ μ„±λŠ₯을 λ³΄μ˜€μœΌλ©° μ•” μ•„ν˜• 특이적인 νŒ¨μŠ€μ›¨μ΄ 및 생물학적 κΈ°λŠ₯을 λ°œκ΅΄ν•  수 μžˆμ—ˆλ‹€. μ„Έ 번째 μ—°κ΅¬λŠ” νŒ¨μŠ€μ›¨μ΄λ‘œλΆ€ν„° μ„œλΈŒ νŒ¨μŠ€μ›¨μ΄/λ„€νŠΈμ›Œν¬λ₯Ό μ°ΎκΈ° μœ„ν•œ 연ꡬ닀. νŒ¨μŠ€μ›¨μ΄λ‚˜ 생물학적 λ„€νŠΈμ›Œν¬μ— 단일 생물학적 κΈ°λŠ₯이 μ•„λ‹ˆλΌ λ‹€μ–‘ν•œ 생물학적 κΈ°λŠ₯이 ν¬ν•¨λ˜μ–΄ μžˆμŒμ— μ£Όλͺ©ν•˜μ˜€λ‹€. 단일 κΈ°λŠ₯을 μ§€λ‹Œ μœ μ „μž 쑰합을 μ°ΎκΈ° μœ„ν•΄μ„œ 생물학적 λ„€νŠΈμ›Œν¬μƒμ—μ„œ 쑰건 특이적인 μœ μ „μž λͺ¨λ“ˆμ„ 찾고자 ν•˜μ˜€μœΌλ©° MIDASλΌλŠ” 도ꡬλ₯Ό κ°œλ°œν•˜μ˜€λ‹€. νŒ¨μŠ€μ›¨μ΄λ‘œλΆ€ν„° μœ μ „μž μƒν˜Έμž‘μš© κ°„ ν™œμ„±λ„λ₯Ό μœ μ „μž λ°œν˜„λŸ‰κ³Ό λ„€νŠΈμ›Œν¬ ꡬ쑰λ₯Ό 톡해 κ³„μ‚°ν•˜μ˜€λ‹€. κ³„μ‚°λœ ν™œμ„±λ„λ“€μ„ ν™œμš©ν•˜μ—¬ 닀쀑 ν΄λž˜μŠ€μ—μ„œ μ„œλ‘œ λ‹€λ₯΄κ²Œ ν™œμ„±ν™”λœ μ„œλΈŒ νŒ¨μŠ€λ“€μ„ 톡계적 기법에 κΈ°λ°˜ν•˜μ—¬ λ°œκ΅΄ν•˜μ˜€λ‹€. λ˜ν•œ, μ–΄ν…μ…˜ λ©”μ»€λ‹ˆμ¦˜κ³Ό κ·Έλž˜ν”„ μ»¨λ³Όλ£¨μ…˜ λ„€νŠΈμ›Œν¬λ₯Ό ν†΅ν•΄μ„œ ν•΄λ‹Ή 연ꡬλ₯Ό νŒ¨μŠ€μ›¨μ΄λ³΄λ‹€ 더 큰 생물학적 λ„€νŠΈμ›Œν¬μ— ν™•μž₯ν•˜λ €κ³  μ‹œλ„ν•˜μ˜€λ‹€. μœ λ°©μ•” 데이터에 λŒ€ν•΄ μ‹€ν—˜μ„ μ§„ν–‰ν•œ κ²°κ³Ό, MIDAS와 λ”₯λŸ¬λ‹ λͺ¨λΈμ„ 닀쀑 ν΄λž˜μŠ€μ—μ„œ 차이가 λ‚˜λŠ” μœ μ „μž λͺ¨λ“ˆμ„ 효과적으둜 μΆ”μΆœν•  수 μžˆμ—ˆλ‹€. 결둠적으둜, λ³Έ λ°•μ‚¬ν•™μœ„ 논문은 DNA μ„œμ—΄μ— λ‹΄κΈ΄ 진화적 μ •λ³΄λŸ‰ 비ꡐ, νŒ¨μŠ€μ›¨μ΄ 기반 μ•” μ•„ν˜• λΆ„λ₯˜, 쑰건 특이적인 μœ μ „μž λͺ¨λ“ˆ λ°œκ΅΄μ„ μœ„ν•œ μƒˆλ‘œμš΄ κΈ°κ³„ν•™μŠ΅ 기법을 μ œμ•ˆν•˜μ˜€λ‹€.Phenotypic differences among organisms are mainly due to the difference in genetic information. As a result of genetic information modification, an organism may evolve into a different species and patients with the same disease may have different prognosis. This important biological information can be observed in the form of various omics data using high throughput instrument technologies such as sequencing instruments. However, interpretation of such omics data is challenging since omics data is with very high dimensions but with relatively small number of samples. Typically, the number of dimensions is higher than the number of samples, which makes the interpretation of omics data one of the most challenging machine learning problems. My doctoral study aims to develop new bioinformatics methods for decoding information in these high dimensional data by utilizing machine learning algorithms. The first study is to analyze the difference in the amount of information between different regions of the DNA sequence. To achieve the goal, a ranked-based k-spectrum string kernel, RKSS kernel, is developed for comparative and evolutionary comparison of various genomic region sequences among multiple species. RKSS kernel extends the existing k-spectrum string kernel by utilizing rank information of k-mers and landmarks of k-mers that represents a species. By using a landmark as a reference point for comparison, the number of k-mers needed to calculating sequence similarities is dramatically reduced. In the experiments on three different genomic regions, RKSS kernel captured more reliable distances between species according to genetic information contents of the target region. Also, RKSS kernel was able to rearrange each region to match a biological common insight. The second study aims to efficiently decode complex genetic interactions using biological networks and, then, to classify cancer subtypes by interpreting biological functions. To achieve the goal, a pathway-based deep learning model using graph convolutional network and multi-attention based ensemble (GCN+MAE) for cancer subtype classification is developed. In order to efficiently reduce the relationships between genes using pathway information, GCN+MAE is designed as an explainable deep learning structure using graph convolutional network and attention mechanism. Extracted pathway-level information of cancer subtypes is transported into gene-level again by network propagation. In the experiments of five cancer data sets, GCN+MAE showed better cancer subtype classification performances and captured subtype-specific pathways and their biological functions. The third study is to identify sub-networks of a biological pathway. The goal is to dissect a biological pathway into multiple sub-networks, each of which is to be of a single functional unit. To achieve the goal, a condition-specific sub-module detection method in a biological network, MIDAS (MIning Differentially Activated Subpaths) is developed. From the pathway, edge activities are measured by explicit gene expression and network topology. Using the activities, differentially activated subpaths are explored by a statistical approach. Also, by extending this idea on graph convolutional network, different sub-networks are highlighted by attention mechanisms. In the experiment with breast cancer data, MIDAS and the deep learning model successfully decomposed gene-level features into sub-modules of single functions. In summary, my doctoral study proposes new computational methods to compare genomic DNA sequences as information contents, to model pathway-based cancer subtype classifications and regulations, and to identify condition-specific sub-modules among multiple cancer subtypes.Chapter 1 Introduction 1 1.1 Biological questions with genetic information 2 1.1.1 Biological Sequences 2 1.1.2 Gene expression 2 1.2 Formulating computational problems for the biological questions 3 1.2.1 Decoding biological sequences by k-mer vectors 3 1.2.2 Interpretation of complex relationships between genes 7 1.3 Three computational problems for the biological questions 9 1.4 Outline of the thesis 14 Chapter 2 Ranked k-spectrum kernel for comparative and evolutionary comparison of DNA sequences 15 2.1 Motivation 16 2.1.1 String kernel for sequence comparison 17 2.1.2 Approach: RKSS kernel 19 2.2 Methods 21 2.2.1 Mapping biological sequences to k-mer space: the k-spectrum string kernel 23 2.2.2 The ranked k-spectrum string kernel with a landmark 24 2.2.3 Single landmark-based reconstruction of phylogenetic tree 27 2.2.4 Multiple landmark-based distance comparison of exons, introns, CpG islands 29 2.2.5 Sequence Data for analysis 30 2.3 Results 31 2.3.1 Reconstruction of phylogenetic tree on the exons, introns, and CpG islands 31 2.3.2 Landmark space captures the characteristics of three genomic regions 38 2.3.3 Cross-evaluation of the landmark-based feature space 45 Chapter 3 Pathway-based cancer subtype classification and interpretation by attention mechanism and network propagation 46 3.1 Motivation 47 3.2 Methods 52 3.2.1 Encoding biological prior knowledge using Graph Convolutional Network 52 3.2.2 Re-producing comprehensive biological process by Multi-Attention based Ensemble 53 3.2.3 Linking pathways and transcription factors by network propagation with permutation-based normalization 55 3.3 Results 58 3.3.1 Pathway database and cancer data set 58 3.3.2 Evaluation of individual GCN pathway models 60 3.3.3 Performance of ensemble of GCN pathway models with multi-attention 60 3.3.4 Identification of TFs as regulator of pathways and GO term analysis of TF target genes 67 Chapter 4 Detecting sub-modules in biological networks with gene expression by statistical approach and graph convolutional network 70 4.1 Motivation 70 4.1.1 Pathway based analysis of transcriptome data 71 4.1.2 Challenges and Summary of Approach 74 4.2 Methods 78 4.2.1 Convert single KEGG pathway to directed graph 79 4.2.2 Calculate edge activity for each sample 79 4.2.3 Mining differentially activated subpath among classes 80 4.2.4 Prioritizing subpaths by the permutation test 82 4.2.5 Extension: graph convolutional network and class activation map 83 4.3 Results 84 4.3.1 Identifying 36 subtype specific subpaths in breast cancer 86 4.3.2 Subpath activities have a good discrimination power for cancer subtype classification 88 4.3.3 Subpath activities have a good prognostic power for survival outcomes 90 4.3.4 Comparison with an existing tool, PATHOME 91 4.3.5 Extension: detection of subnetwork on PPI network 98 Chapter 5 Conclusions 101 ꡭ문초둝 127Docto
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