This study focuses on automatic searching and verifying methods for the teachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic sear...This study focuses on automatic searching and verifying methods for the teachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a considerable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for verifying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed in a temporal logic, such as computational tree logic （CTL）. This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.展开更多
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （200...The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.展开更多
文摘This study focuses on automatic searching and verifying methods for the teachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a considerable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for verifying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed in a temporal logic, such as computational tree logic （CTL）. This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.
基金Hong Kong Grants Council Grants #622105 and #622307the National Basic Research Program of China (aka the 973 Program) under project No.2003CB517106.
文摘The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.
