Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction

We present a model for predicting HLA class I restricted CTL epitopes. In contrast to almost all other work in this area, we train a single model on epitopes from all HLA alleles and supertypes, yet retain the ability to make epitope predictions for specific HLA alleles. We are therefore able to leverage data across all HLA alleles and/or their supertypes, automatically learning what informatio...

متن کامل

Leveraging Information Across Categories

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers’ behavior in a variety of situations and product categories. For example, information about a customer’s purchase behavior in one category can be helpful in predicting his potentia...

متن کامل

T-Epitope Designer: A HLA-peptide binding prediction server

UNLABELLED The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA...

متن کامل

Leveraging Common Structure to Improve Prediction across Related Datasets

In many applications, training data is provided in the form of related datasets obtained from several sources, which typically affects the sample distribution. The learned classification models, which are expected to perform well on similar data coming from new sources, often suffer due to bias introduced by what we call ‘spurious’ samples – those due to source characteristics and not represent...

متن کامل

Network information improves cancer outcome prediction

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures o...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Computational Biology

سال: 2007

ISSN: 1066-5277,1557-8666

DOI: 10.1089/cmb.2007.r013