181 research outputs found
Research of Choosing and Developing the Leading Regional Service Industry
This paper initially constructed a theoretical analysis framework of choosing the leading service industry on the basis of results of the relevant domestic and foreign theories. The paper takes Chongqing as a case to determine its pillar service industry by the factor analysis method. From the factors of system safeguard, development orientation and platform building etc, we put forward countermeasures and proposals for cultivating the leading industry based on qualitative analysis on choosing pillar industry in Chongqing
Structural Deep Embedding for Hyper-Networks
Network embedding has recently attracted lots of attentions in data mining.
Existing network embedding methods mainly focus on networks with pairwise
relationships. In real world, however, the relationships among data points
could go beyond pairwise, i.e., three or more objects are involved in each
relationship represented by a hyperedge, thus forming hyper-networks. These
hyper-networks pose great challenges to existing network embedding methods when
the hyperedges are indecomposable, that is to say, any subset of nodes in a
hyperedge cannot form another hyperedge. These indecomposable hyperedges are
especially common in heterogeneous networks. In this paper, we propose a novel
Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with
indecomposable hyperedges. More specifically, we theoretically prove that any
linear similarity metric in embedding space commonly used in existing methods
cannot maintain the indecomposibility property in hyper-networks, and thus
propose a new deep model to realize a non-linear tuplewise similarity function
while preserving both local and global proximities in the formed embedding
space. We conduct extensive experiments on four different types of
hyper-networks, including a GPS network, an online social network, a drug
network and a semantic network. The empirical results demonstrate that our
method can significantly and consistently outperform the state-of-the-art
algorithms.Comment: Accepted by AAAI 1
ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
Protein-nucleic acid interactions play a very important role in a variety of
biological activities. Accurate identification of nucleic acid-binding residues
is a critical step in understanding the interaction mechanisms. Although many
computationally based methods have been developed to predict nucleic
acid-binding residues, challenges remain. In this study, a fast and accurate
sequence-based method, called ESM-NBR, is proposed. In ESM-NBR, we first use
the large protein language model ESM2 to extract discriminative biological
properties feature representation from protein primary sequences; then, a
multi-task deep learning model composed of stacked bidirectional long
short-term memory (BiLSTM) and multi-layer perceptron (MLP) networks is
employed to explore common and private information of DNA- and RNA-binding
residues with ESM2 feature as input. Experimental results on benchmark data
sets demonstrate that the prediction performance of ESM2 feature representation
comprehensively outperforms evolutionary information-based hidden Markov model
(HMM) features. Meanwhile, the ESM-NBR obtains the MCC values for DNA-binding
residues prediction of 0.427 and 0.391 on two independent test sets, which are
18.61 and 10.45% higher than those of the second-best methods, respectively.
Moreover, by completely discarding the time-cost multiple sequence alignment
process, the prediction speed of ESM-NBR far exceeds that of existing methods
(5.52s for a protein sequence of length 500, which is about 16 times faster
than the second-fastest method). A user-friendly standalone package and the
data of ESM-NBR are freely available for academic use at:
https://github.com/wwzll123/ESM-NBR
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