729 research outputs found
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Computational Strategies for Scalable Genomics Analysis.
The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications
An Investigation in Efficient Spatial Patterns Mining
The technical progress in computerized spatial data acquisition and storage results
in the growth of vast spatial databases. Faced with large amounts of increasing spatial
data, a terminal user has more difficulty in understanding them without the helpful
knowledge from spatial databases. Thus, spatial data mining has been brought under
the umbrella of data mining and is attracting more attention.
Spatial data mining presents challenges. Differing from usual data, spatial data includes
not only positional data and attribute data, but also spatial relationships among
spatial events. Further, the instances of spatial events are embedded in a continuous
space and share a variety of spatial relationships, so the mining of spatial patterns demands
new techniques.
In this thesis, several contributions were made. Some new techniques were proposed,
i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree),
maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’
Generalization Sequences), and fuzzy association prediction. Three algorithms
were put forward on co-location patterns mining: the fuzzy co-location mining algorithm,
the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique-
based maximal prevalence co-location mining algorithm (order-clique-based algorithm).
An attribute-oriented induction algorithm based on attributes’ generalization sequences
(AOI-ags algorithm) is further given, which unified the attribute thresholds and
the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association
prediction algorithm is designed. Also a cell-based spatial object fusion algorithm
is proposed. Two fuzzy clustering methods using domain knowledge were proposed:
Natural Method and Graph-Based Method, both of which were controlled by a
threshold. The threshold was confirmed by polynomial regression. Finally, a prototype
system on spatial co-location patterns’ mining was developed, and shows the relative
efficiencies of the co-location techniques proposed
The techniques presented in the thesis focus on improving the feasibility, usefulness,
effectiveness, and scalability of related algorithm. In the design of fuzzy co-location
Abstract
mining algorithm, a new data structure, the binary partition tree, used to improve the
process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to
partition the prevalent event set search space into subsets, where each sub-problem can
be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is
guaranteed since it does not require expensive spatial joins or instance joins for identifying
co-location table instances. In the order-clique-based algorithm, the co-location table
instances do not need be stored after computing the Pi value of corresponding colocation,
which dramatically reduces the executive time and space of mining maximal colocations.
Some technologies, for example, partitions, equivalence partition trees, prune
optimization strategies and interestingness, were used to improve the efficiency of the
AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing
window” and the proximity computation pruning were introduced to reduce both I/O and
CPU costs in computing the fuzzy semantic proximity between time-series.
For new techniques and algorithms, theoretical analysis and experimental results
on synthetic data sets and real-world datasets were presented and discussed in the thesis
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Deconvolute individual genomes from metagenome sequences through short read clustering.
Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality
<Notes>On the Kinds and Idols of Ewenki People\u27s Deities in North-east China
エヴァンキ族は、主として中国の内モンゴル自治区呼倫貝爾盟に分布している。1992年に行われた中国の人口調査によると、全国のエヴァンキ族の”エヴァンキ”という言葉は、この民族の自称であり、”山から降りた人”、或いは”山から降りてきた人達”という意味である。・・
On Power Law Scaling Dynamics for Time-fractional Phase Field Models during Coarsening
In this paper, we study the phase field models with fractional-order in time.
The phase field models have been widely used to study coarsening dynamics of
material systems with microstructures. It is known that phase field models are
usually derived from energy variation so that they obey some energy dissipation
laws intrinsically. Recently, many works have been published on investigating
fractional-order phase field models, but little is known of the corresponding
energy dissipation laws. We focus on the time-fractional phase field models and
report that the effective free energy and roughness obey a universal power-law
scaling dynamics during coarsening. Mainly, the effective free energy and
roughness in the time-fractional phase field models scale by following a
similar power law as the integer phase field models, where the power is
linearly proportional to the fractional order. This universal scaling law is
verified numerically against several phase field models, including the
Cahn-Hilliard equations with different variable mobilities and molecular beam
epitaxy models. This new finding sheds light on potential applications of time
fractional phase field models in studying coarsening dynamics and crystal
growths
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