1,637 research outputs found
Relationship between promoter sequence and its strength in gene expression
In this study, through various tests one theoretical model is presented to
describe the relationship between promoter strength and its nucleotide
sequence. Our analysis shows that, promoter strength is greatly influenced by
nucleotide groups with three adjacent nucleotides in its sequence. Meanwhile,
nucleotides in different regions of promoter sequence have different effects on
promoter strength. Based on experimental data for {\it E. coli} promoters, our
calculations indicate, nucleotides in -10 region, -35 region, and the
discriminator region of promoter sequence are more important than those in
spacing region for determining promoter strength. With model parameter values
obtained by fitting to experimental data, four promoter libraries are
theoretically built for the corresponding experimental environments under which
data for promoter strength in gene expression has been measured previously
Existence and uniqueness of solution of the differential equation describing the TASEP-LK coupled transport process
In this paper, the existence and uniqueness of solution of a specific
differential equation is studied. This equation originates from the description
of a coupled process by totally asymmetric simple exclusion process (TASEP) and
Langmuir kinetics (LK). In the fields of physics and biology, the properties of
the TASEP-LK coupled process have been extensively studied by Monte Carlo
simulations and numerical calculations, as well as detailed experiments.
However, so far, no rigorous mathematical analysis has been given to the
corresponding differential equations, especially their existence and uniqueness
of solution. In this paper, using the upper and lower solution method, the
existence of solution of the steady state equation is obtained. Then using a
generalized maximum principle, we show that the solution constructed from the
upper and lower solution method is actually the unique solution in C∞ space.
Moreover, the existence and uniqueness of solution of the time dependent
differential equation are also obtained in one specific space X\b{eta}. Our
results imply that the previous results obtained by numerical calculations and
Monte Carlo simulations are theoretically correct, especially the most
important phase diagram of particle density along the travel track under
different model parameters. The study in this paper provides theoretical
foundations for the analysis of TASEP-LK coupled process. At the same time, the
methods used in this paper may be instructive for studies about the more
general cases of the TASEP-LK process, such as the one with multiple travel
tracks or the one with multiple particle species.Comment: This paper has been thoroughly modified and submited again to arXiv
by my coauther Jingwei Li. So I think it is betetr for me to withdraw from my
account. see arXiv:1905.12235v
Multi-Way Factorization Machine For Sentiment Analysis
Sentiment analysis is a process of learning the relationship between sentiment label
and text. The research value of sentiment analysis is two-fold: first, it has a wide range
of applications in many sectors and industries, e.g., the industry has flourished due to the
proliferation of commercial applications such as using sentiment analysis as an integrated
part of customer experience strategy. Second, it offers an array of new challenging problems
for research community such as word feature embedding and machine learning. Albeit earlier
methods such as Naïve Bayes (NB), Random Forest (RF), k-Nearest-Neighbours (kNN),
Support Vector Machine (SVM) and more recent methods such as Deep Learning (DL)
methods are effective, they are primarily designed for shorter or longer textual data thus
are not able to maintain a robust performance across a variety of text with diverse lengths.
In reality, some text is as abbreviated as one single word while others are so pleonastic
that are over thousands of words. Moreover, ad hoc combination of feature embedding and
learning methods makes it more difficult to choose the right approach for different types of
textual data. Undoubtedly an integrated feature embedding and sentiment analysis method
is desirable. In this thesis, we introduce multi-way FM as a new method for sentiment analysis
accounting for higher-order feature interaction. We demonstrate the performance and
flexibility of the FM method to other competing methods by tuning a single parameter to
accommodate both shorter Twitter and longer movie review documents
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