Extracting structured knowledge from amounts of experimental information is a major challenge to bioinformatics. In this work we propose a novel approach to analyze protein interactome data. The main goal of our research is to provide a biologically meaningful explanation for the phenomena captured by...
A fundamental problem in science involves explaining natural phenomena in a manner consistent with noisy experimental data and a body of potentially inexact and incomplete background knowledge about the universe’s laws1. In the past few centuries, The Scientific Method2has led to significant progress...
In the case of big data, there are too many attributes to identify the weakest parts to be protected. To address these issues, we propose a new model of attackers that gain background knowledge from an attribute of the transaction data and estimate the risk of re-identification from the ...
Zhou Y, Liu L (2013) Social influence based clustering of heterogeneous information networks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 338–346 Google Scholar Zhou Y, Cheng H, Yu JX (2009) Graph clustering based on structural/at...
We applied two state-of-the-art, knowledge independent data-mining methods – Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) – to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples fr...
For example, as machine learning and AI can process the complex analysis of parameters and models as image analysis, even researchers without knowledge of remote sensing learn to use Earth observation data. In addition to the increase of the researchers, the analysis scope of another observation pe...
aggregation strategy;background knowledge;biomedical ontologies;indirect matching;mapping composition;ontology alignment;ontology matching 1. Introduction The evolution of semantic web technologies and the growth of big data volumes maintained by various database models have resulted in many disparate and indep...
The more data you have to train with, the better, but data alone isn’t enough. It’s just as important to make sure that the datasets are relevant to the task at hand and of high quality. For those delving into the complex world of machine learning in finance, ensuring data relevance...
- Acm Sigkdd International Conference on Knowledge Discovery & Data Mining 被引量: 77发表: 2016年 Cross-Modal Deep Variational Hashing In this paper, we propose a cost-sensitive local binary feature learning (CS-LBFL) method for facial age estimation. Unlike conventional facial age estimat... ...
Therefore, the first optimization objective in the model is set to minimize carbon dioxide emissions during the delivery process. Zhou et al. summarized most of the estimation methods on carbon emissions from automobiles29, considering the difficulty of obtaining data, this paper decided to use the...