Joint Prediction Model Research Articles

Integrative clustering by nonnegative matrix factorization can reveal coherent functional groups from gene profile data.

Recent developments in molecular biology and techniques for genome-wide data acquisition have resulted in abundance of data to profile genes and predict their function. These datasets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper, we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of nonnegative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous datasets and yield high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering.

Using Parameter Joint Model to Predict the Grain Yield of Guangxi Province

The principle of the Markov chain model is described in this paper, based on the combination forecasting thought and Markov chain thinking, we combine joint forecasting model with the state of the Markov chain transfer model , which is better reflected the law of development of the system of things. We get with parameters in the joint model, and use the joint model to predict the Guangxi food production. Based on historical grain yields data from 1964 to 2011 and the nature of the growth curve regression equation, we establish parameter joint prediction model of Markov and the quadratic function growth curve, which can predict the future grain production of Guangxi province, and we could proof its feasibility. The accuracy of parameter joint model prediction is high, which can be validated by historical grain yields data.

The Application of Joint Model for Sugarcane Production Forecast of Guangxi Province

Based on Markov process and the nature of the growth curve regression equation, we establish joint prediction model of Markov and the Logistic growth curve, which can predict the future sugarcane production of Guangxi province, and we could proof its feasibility.

Discriminative graphical models for faculty homepage discovery

Faculty homepage discovery is an important step toward building an academic portal. Although the general homepage finding tasks have been well studied (e.g., TREC-2001 Web Track), faculty homepage discovery has its own special characteristics and not much focused research has been conducted for this task. In this paper, we view faculty homepage discovery as text categorization problems by utilizing Yahoo BOSS API to generate a small list of high-quality candidate homepages. Because the labels of these pages are not independent, standard text categorization methods such as logistic regression, which classify each page separately, are not well suited for this task. By defining homepage dependence graph, we propose a conditional undirected graphical model to make joint predictions by capturing the dependence of the decisions on all the candidate pages. Three cases of dependencies among faculty candidate homepages are considered for constructing the graphical model. Our model utilizes a discriminative approach so that any informative features can be used conveniently. Learning and inference can be done relatively efficiently for the joint prediction model because the homepage dependence graphs resulting from the three cases of dependencies are not densely connected. An extensive set of experiments have been conducted on two testbeds to show the effectiveness of the proposed discriminative graphical model.