Identification of MHC Class II Binders/ Non-binders using Negative Selection Algorithm

The identification of major histocompatibility complex (MHC) class-II restricted peptides is an important goal in human immunological research leading to peptide based vaccine design. These MHC class–II peptides are predominantly recognized by CD4+ T-helper cells, which when turned on, have profound immune regulatory effects. Thus, prediction of such MHC class-II binding peptides is very helpful towards epitope-based vaccine design. HLA-DR proteins were found to be associated with autoimmune diseases e.g. HLA-DRB1*0401 with rheumatoid arthritis. It is important for the treatment of autoimmune diseases to determine which peptides bind to MHC class II molecules. The experimental methods for identification of these peptides are both time consuming and cost intensive. Therefore, computational methods have been found helpful in classifying these peptides as binders or non-binders. We have applied negative selection algorithm, an artificial immune system approach to predict MHC class–II binders and non-binders. For the evaluation of the NSA algorithm, five fold cross validation has been used and six MHC class–II alleles have been taken. The average area under ROC curve for HLA-DRB1*0301, DRB1*0401, DRB1*0701, DRB1*1101, DRB1*1501, DRB1*1301 have been found to be 0.75, 0.77, 0.71, 0.72, and 0.69, and 0.84 respectively indicating good predictive performance for the small training set

Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related meta-information, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/3

Cluster Algebra: A Query Language for Heterogeneous Databases

This report describes a query language based on algebra for heterogeneous databases. The database logic is used as a uniform framework for studying the heterogeneous databases. The data model based on the database logic is referred to as cluster data model in this report. Generalized Structured Query Language (GSQL) is used for expressing ad-hoc queries over the relational, hierarchical and network database uniformly. For the purpose of query optimization, a query language that can express the primitive heterogeneous database operations is required. This report describes such a query language for the clusters (i.e., heterogeneous databases). The cluster algebra consists of (a) generalized relational operations such as selection, union, intersection, difference, semi-join, rename and cross-product; (b) modified relational operations such as normal projection and normal join; and (c) new operations such as normalize, embed, and unembed

Srikumar and Bhasker: Personalized Product Selection In Internet Business PERSONALIZED PRODUCT SELECTION IN INTERNET BUSINESS

Traditional product selection methods – especially for high involvement products like refrigerators, cars and diamonds – use customer specified multi-attributes of the product to select products of interest to the customer. However, such methods tend to generate lot of false positives and false negatives due to conflicting, imprecise and non-commensurable nature of product attributes. In this paper, we present a novel methodology for product selection in Internet business to effectively handle the nebulous nature of product attributes. The system enhances the customer desired product attributes by utilizing his/her past profile, which is built by using his/her past purchases in the related product category. The suggested system offers the product variants as recommendation in a ranked order with customization to individual user’s needs. We experimentally evaluate the system on a real-life dataset in order to assess its potential usefulness. The methodology discussed here can be useful for consumers in making a better choice of the final product. In addition, it can also be useful for e-commerce managers in providing personalized services to their customers

PERSONALIZED ITEM RANKING FROM IMPLICIT USER FEEDBACK USING HETEROGENEOUS INFORMATION NETWORK

In this age of information overload, providing useful recommendations to the users of E-commerce websites is an important and challenging problem. Personalized item ranking to recommend top-N items of interest to a user is more challenging in the case of implicit feedback data. Due to the lack of explicit user preferences, a model-based approach like matrix factorization is not effective for implicit user feedback. However, a neighborhood-based technique like Item-based collaborative filtering (IBCF) has shown better performance for implicit user feedback data and is a popular technique due to its scalability property. Although better than matrix factorization based technique for implicit feedback data, IBCF generates low-quality recommendations when data is sparse. In this work, we propose a method to address the problem of IBCF due to the sparseness of data by incorporating the metainformation related to items, and hybridization is done by forming a heterogeneous item information network. The proposed method uses a meta-path based framework for generating personalized item ranking and generates good quality recommendations in real time. The interest of users and popularity of items are leveraged simultaneously to improve the quality of recommendations. To find the intrinsic interest of users from the implicit feedback data, we perform the personalized weight learning to integrate the semantics of various meta-paths in the network. Experimental evaluation of the proposed method using the real-world data shows that the proposed method performs better than IBCF