In this paper,we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data.The availability of various types of omics data provides great opportunity and also un-precedented challenge to infer the interactome in cells.Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes.The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations.In this paper,we describe the main steps of predicting protein-protein interaction networks and categorize the available ap-proaches to couple the physical and functional linkages.The future topics and the analyses beyond prediction are also discussed and concluded.
The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.
In general,a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network,which can be considered as a set of interactions or edges among molecules.Thus,instead of individual molecules,networks or edges are stable forms to reliably characterize complex diseases.This paper reviews both traditional node biomarkers and edge biomarkers,which have been newly proposed.These biomarkers are classified in terms of their contained information.In particular,we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample.First,we categorize the biomarkers based on the information used in the learning and prediction steps.We then briefly introduce conventional node biomarkers,or molecular biomarkers without network information,and their computational approaches.The main focus of this paper is edge and network biomarkers,which exploit network information to improve the accuracy of diagnosis and prognosis.Moreover,by extracting both network and dynamic information from the data,we can develop dynamical network and edge biomarkers.These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state.The identified critical molecules can be used as drug targets,and the critical state indicates the critical point of disease control.The paper also discusses representative biomarker-based methods.
In ＂Omics＂ era of the life sciences, data is presented in many forms, which represent the information at various levels of bio- logical systems, including data about genome, transcriptome, epigenome, proteome, metabolome, molecular imaging, molec- ular pathways, different population of people and clinical/med- ical records. The biological data is big, and its scale has already been well beyond petabyte （PB） even exabyte （EB）. Nobody doubts that the biological data will create huge amount of val- ues, if scientists can overcome many challenges, e.g., how to handle the complexity of information, how to integrate the data from very heterogeneous resources, what kind of principles or standards to be adopted when facing with the big data. Tools and techniques for analyzing big biological data enable us to translate massive amount of information into a better under- standing of the basic biomedical mechanisms, which can be fur- ther applied to translational or personalized medicine.
