Effect of Seeding Strategy on the Efficiency of Brand Spreading in Complex Social Networks.

In social networks, consumers gather to form brand communities, and the community structure significantly impacts the dissemination of brand information. Which communication strategy is more conducive to information dissemination in different structured brand communities? Considering the above factors, we propose the word-of-mouth (WOM) agent model based on the traditional rumor model and bass model, in which the brand WOM spreading is affected by the user's psychological mechanisms, the network structure, and other factors. Through simulation experiments, the results showed the following: (1) the conclusion of the traditional bass model is no longer applicable to social marketing in brand information diffusion, that is, the effect of external marketing stimulation on information dissemination is limited.

Do Product Characteristics Affect Customers' Participation in Virtual Brand Communities? An Empirical Study.

The virtual brand community has become an important marketing tool for companies. A successful brand community marketing strategy should attract a large number of consumers. Although past studies have revealed consumer motivations for participating in virtual brand communities, they fail to answer an important question: Why is it so easy for some virtual brand communities to attract users while others have such difficulty? In this study, product characteristics are hypothesized to be important factors that determine consumer motivation to participate in brand communities.

PIFA-Mediated Dearomatizative Spirocyclization of Phenolic Biarylic Ketones via Oxidation and C-C Bond Cleavage.

The dearomatizing spirocyclization of phenolic biarylic ketones using PhI(OCOCF) as oxidant is presented. The reaction affords various cyclohexadienones through C-C bond cleavage under mild conditions. Mechanistic investigations reveal that an exocyclic enol ether acts as the key intermediate in the transformation.

A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.

Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues.

Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis.

Post-translational modifications (PTMs) are crucial steps in protein synthesis and are important factors contributing to protein diversity. PTMs play important roles in the regulation of gene expression, protein stability and metabolism. Lysine residues in protein sequences have been found to be targeted for both types of PTMs: sumoylations and acetylations; however, each PTM has a different cellular role.

Prediction of S-nitrosylation modification sites based on kernel sparse representation classification and mRMR algorithm.

Protein S-nitrosylation plays a very important role in a wide variety of cellular biological activities. Hitherto, accurate prediction of S-nitrosylation sites is still of great challenge. In this paper, we presented a framework to computationally predict S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm.

Classification of non-small cell lung cancer based on copy number alterations.

Lung cancer is one of the leading causes of cancer mortality worldwide and non-small cell lung cancer (NSCLC) accounts for the most part. NSCLC can be further divided into adenocarcinoma (ACA) and squamous cell carcinoma (SCC). It is of great value to distinguish these two subgroups clinically.

Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.

Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets.

Predicting DNA-binding sites of proteins based on sequential and 3D structural information.

Protein-DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein-DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein-DNA-binding sites based solely on protein sequences.

Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way.

Prediction of carbamylated lysine sites based on the one-class k-nearest neighbor method.

Protein carbamylation is one of the important post-translational modifications, which plays a pivotal role in a number of biological conditions, such as diseases, chronic renal failure and atherosclerosis. Therefore, recognition and identification of protein carbamylated sites are essential for disease treatment and prevention. Yet the mechanism of action of carbamylated lysine sites is still not realized.

Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG.

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival.

Prediction and Analysis of Post-Translational Pyruvoyl Residue Modification Sites from Internal Serines in Proteins.

Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming.

Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network.

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network.

Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network.

Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention.

A novel method of predicting protein disordered regions based on sequence features.

With a large number of disordered proteins and their important functions discovered, it is highly desired to develop effective methods to computationally predict protein disordered regions. In this study, based on Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Feature Selection (IFS), we developed a new method to predict disordered regions in proteins. The mRMR criterion was used to rank the importance of all candidate features.

An ensemble prognostic model for colorectal cancer.

Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer.

Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

Identification of catalytic residues plays a key role in understanding how enzymes work. Although numerous computational methods have been developed to predict catalytic residues and active sites, the prediction accuracy remains relatively low with high false positives. In this work, we developed a novel predictor based on the Random Forest algorithm (RF) aided by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS).

Prediction of protein cleavage site with feature selection by random forest.

Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging.

Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS).

Computational prediction and analysis of protein γ-carboxylation sites based on a random forest method.

The glutamate γ-carboxylation plays a pivotal part in a number of important human diseases. However, traditional protein γ-carboxylation site detection by experimental approaches are often laborious and time-consuming. In this study, we initiated an attempt for the computational prediction of protein γ-carboxylation sites.

Prediction of protein domain with mRMR feature selection and analysis.

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem.

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network.

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma.

MicroRNA mediated network and DNA methylation in colorectal cancer.

Colorectal cancer (CRC) is one of the most malignant cancers. A growing number of studies have shown that both genetic and epigenetic play important roles in the etiology of CRC. Both microRNA (miRNA) and DNA methylation belong to the scope of epigenetic and there are complex regulatory mechanisms within miRNA and DNA methylation.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases.