Liver Cancer

Cancer is a complex disease involving multiple genetic and epigenetic events occurring, and influencing each other, over a long period of time. Liver cancer is one of the most common types of tumors and, because of the homogeneity of the hepatic tissue, the most experimentally tractable one.

CD ComputaBio brings together elite experts in the fields of genetics, chromatin regulation, genomics, liver cancer, computational and systems biology. This combination of skills will allow us to investigate and model at the unprecedented resolution, for the chain of events leading from environmental perturbations and the occurrence of driver mutations to preneoplastic disease and cancer.

Understanding cancer, and ultimately developing effective targeted therapies, will therefore require that mutations and epigenetic alterations be systematically investigated during the multiple stages of disease development, from identifiable pre-neoplastic phases to overt cancer. Use advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms.

BIOBASE ExPlain System

ExPlain integrates genomic information with biological knowledge bases and computational analysis methods.

TRANSFAC Database

Transcription factor binding sites (TFBSs) can be predicted by positional weight matrices (PWMs) from the TRANSFAC database.

TRANSPATH Database

Topological analyses of signal transduction networks can be performed using molecular reactions collected in the TRANSPATH database.

GO Enrichment Analysis

The Gene Ontology (GO) provides an extensive ontological description of cellular components, molecular functions and biological processes. It is routinely applied in studies to test for enrichment of categories in sets of genes or proteins. Statistical significance of enrichment is typically quantified by the one-tailed Fisher test.

Why Choose Us?

Our mission is to uncover the mechanisms of liver cancer behavior, study genome-wide DNA, RNA, microRNA, and methylation profiles, and determine their relationship with biological outcomes. We utilize microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

In addition, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.