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CEA: Combination-based gene set functional enrichment analysis

Science, Chinese Academy of Sciences, Beijing, 100190, ChinaDuanchen Sun, Yinliang Liu, Xiang-Sun Zhang & Ling-Yun WuSchool of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing ... • Google Scholar Search for Xiang-Sun Zhang in:Nature Research journals • PubMed • Google Scholar Search for Ling-Yun Wu in:Nature Research journals • PubMed • Google Scholar Contributions L.Y.W. and

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data

Motivation: In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. Results...

Discovery of co-occurring driver pathways in cancer

Background It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer...

Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis

Chen Chen 0 Shihua Zhang 0 Xiang-Sun Zhang 0 0 National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190

Computational systems biology in the big data era

A report of the 6th IEEE International Conference on Systems Biology (IEEE ISB2012), 18-20 August, Xi'an, China.

Corbi: a new R package for biological network alignment and querying

In the last decade, plenty of biological networks are built from the large scale experimental data produced by the rapidly developing high-throughput techniques as well as literature and other sources. But the huge amount of network data have not been fully utilized due to the limited biological network analysis tools. As a basic and essential bioinformatics method, biological...

Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data

Wang 0 Xiang-Sun Zhang 0 0 National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190 , China Motivation

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction...

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some...

Modelling biological systems from molecules to dynamical networks

interdisciplinary field, including mathematical methods and its applications in biosciences and researches on a variety of aspects of Systems Biology. Therefore, Prof. Xiang-Sun Zhang, the honorary president of

Efficient methods for identifying mutated driver pathways in cancer

Motivation: The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations...

ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions

Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach...

A unified computational model for revealing and predicting subtle subtypes of cancers

BackgroundGene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual...

An efficient network querying method based on conditional random fields

Motivation: A large amount of biomolecular network data for multiple species have been generated by high-throughput experimental techniques, including undirected and directed networks such as protein–protein interaction networks, gene regulatory networks and metabolic networks. There are many conserved functionally similar modules and pathways among multiple biomolecular networks...

Computational systems biology: integration of sequence, structure, network, and dynamics

A report of the 4nd International Conference on Computational Systems Biology, 9-11 September 2010, Suzhou, China.

Prediction of hot spots in protein interfaces using a random forest model with hybrid features

Prediction of hot spots in protein interfaces provides crucial information for the research on protein–protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed...

Two-stage flux balance analysis of metabolic networks for drug target identification

Background Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective...

Optimization meets systems biology

A report of the 3nd International Symposium on Optimization and Systems Biology, 20-22 September 2009, Zhangjiajie, China.

Inferring domain-domain interactions from protein-protein interactions in the complex network conformation

Background As protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can...