Non-homologous end joining (NHEJ) is a major DNA double strand breaks (DSBs) repair pathway that maintains genome integrity. However, this pathway may reduce radiotherapy efficacy by repairing DSBs on cancer cells. This research reported a computer-aided drug design (CADD) method to identify novel inhibitors from traditional Chinese medicine (TCM) that… (More)
This paper describes our ensemble of three classifiers for the KDD Cup 2009 challenge. First, we transform the three binary classification tasks into a joint multi-class classification problem, and solve an l1-regularized maximum entropy model under the LIBLINEAR framework. Second, we propose a heterogeneous base learner, which is capable of handling… (More)
We present a latent variable structured prediction model, called the Latent Left-linking Model (L3M), for discriminative supervised clustering of items that follow a streaming order. L 3 M admits efficient inference and we present a learning framework for L 3 M that smoothly interpolates between latent structural SVMs and hidden variable CRFs. We present a… (More)
We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of… (More)
• Tractable machine learning methods for complex and big data. • Statistical approaches to natural language processing. 2013 Given in support of a student showing exceptional research promise relatively early in their graduate studies.
Many machine learning applications involve jointly predicting multiple mutually dependent output variables. Learning to search is a family of methods where the complex decision problem is cast into a sequence of decisions via a search space. Although these methods have shown promise both in theory and in practice, implementing them has been burdensomely… (More)
Machine learning techniques have been widely applied in many areas. In many cases, high accuracy requires training on large amount of data, adding more expressive features and/or exploring complex input and output interactions, often resulting in scalability problems. My research goal is to design practical algorithms to efficiently learn expressive models… (More)
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very… (More)