Considering nowadays propagation of Public Opinion possessing different regular pattern and effect, it is the respond of reality demand to research on network ropagation mechanism of public opinion. Simulation is conducted based on network structured by improved scale-free network. Instead of study propagation by single method, the paper analyzes the view exchange process in detail by introducing different degree of individual’s stubbornness and belief degree. Deffaunt Model is used and improved. By studying two kinds of initialization, the paper ﬁnds out people will have distinct opinion after propagation in the context of non-intervention, learning the nature of network’s inﬂuence in propagation. Furthermore, when intervention is taken into
OCC or PU Learning Definition: One Class Classification or Positive and Unlabeled Learning corresponds to a special case in machine learning when only a small proportion of positive samples and unlabeled ones are employed for learning task. One frequent task is generally to retrieve positive ones from the unlabeled in the training dataset, namely transductive learning. Detecting positive or identifying negative ones from the unlabeled in both training dataset and unknown is another popular task, also referred to as inductive learning.
The identification and validation of drug targets is crucial in biomedical research. Most recently, lots of studies have been conducted on analyzing drug target features aiming at getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we examined three main ways to understand the functional biomolecules based on the topological features of drug targets. By comparison between two types of proteins (targets and common proteins) of the protein-protein interactions (PPI) network, the results show there are no significant differences on intermediary and source functions.
The past decades of years has witnessed the booming of pharmacology as well as the dilemma of drug development. Playing in a crucial role in drug design, retrieval of potential proteins of drug targets from open access database is an overwhelming task of challenge but significance, receiving much attention from areas of academia and medical engineering. Human proteins with several measured physical-chemical properties can be well mined for screening reliable candidates, which would accelerate the process of drug development and bring the reduction in expense. In this paper, retrieval of potential drug target proteins (DTPs) from a fine collected dataset was researched from the perspective of data mining. Puzzled with the uncertainty of non-drug target proteins (NDTPs),
One class classification (OCC) problem corresponds to a special case in machine learning area when only a proportion of positive samples and unlabeled ones are employed for learning tasks. Due to the similar characteristics of the learning dataset, we also recognize the positive and learning problems (PU) as equivalent to the OCC problems in this paper. At the same time, some other concepts such as anomaly detection, novelty detection, concept learning show great connection with the OCC when it comes to some specific task. Obviously, the OCC behaves significantly different from the traditional supervised learning paradigm in which the former neg-lects the prior distribution of classes and gets rid of the dependence on the negative
The Command and Control (C2) organization plays an indispensable role on planning of military operation. Nowadays the plan oriented more uncertaintybattlefield environment is the hot area. Many studies highlight how to rapidlymake or change the plans with detecting advance information. One of the solutions is based the time-domain metric model to evaluate C2 organizational decision-making capability. We develop an improved simulated annealing algorithm to facilitate this model implementation. In order todiscretize the time-domain, we propose a horizon partition that is based on the task dynamic state.Finally, the optimization
Currently as the emergence of high throughput technologies for omics data, such as yeast 2 hybrid protein interactions, researches of protein-protein interaction (PPI) had been explosively conducted on. One of the consensuses is that topographical analysis of the intercellular protein interactions lead to new avenues for drug target prediction. In this paper, a panorama view of drug targets is constructed. We found that the connectivity is often not a sufficient criterion to analyze the function of the drug target. On the other hand, the core of the PPI network poses a systematic way to consider the local and global significance of a protein, which indicates the inherent layer structure of the targets interactions.
Motivation: Identifying new drug protein plays an important role in pharmaceutical and biomedical research. Before new drug target get manual tested and put into experimental study, a globe filter method is more efficient, practical and economical. New methods are developed to predict new drug targets, but most of them are hypothesis specific or data specific, thus, a more general computational method is required. Result: In this study, we first category the target protein as carrier, transporter, and enzyme, gained 153 overlap proteins among these three categories which should be further analyzed. Using their sequence information we gained their
Motivation: The identification of drug target proteins can certainly benefit new drug development. With the advances in high throughput sequencing technologies, proteins are studied systematically as a network namely protein-protein interaction (PPI) network. PPI plays a fundamental role in many biological processes. Therefore, it provides us a better way to understand drug and protein interaction. In this paper, we analyzed the drug target protein’s interaction network and extracted the physical and chemical properties from protein sequence information. Based both of the network and individual information, the potential drug target proteins are predicted to help identify new drug targets.