Sarcoma

Sarcomas are heterogeneous and clinically challenging soft tissue and bone cancers. Owing to their most aggressive biological behavior, relative rarity, and occurrence at virtually every anatomical site, many sarcoma subtypes are in particular difficult-to-treat categories.

Sarcoma Subtypes Profiling

Based on morphology it is often challenging to distinguish between the many different sarcoma subtypes. Many more sarcomas subtypes are being discovered due to molecular profiling. Recent scientific advancements have enabled a more precise molecular characterization of sarcoma subtypes and revealed novel therapeutic targets and prognostic/predictive biomarkers.

We are exploring novel molecular classification approaches to sarcomas based on genomic profiling. Attributing to our computational biology and bioinformatics backgrounds, we are interested in assisting in the development and implementation of novel bioinformatics approaches to analyze high-dimensional genomic data in a biologically relevant manner.

AI-Driven Sarcoma Research

Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities, to identify novel diagnostic and prognostic markers and therapeutic targets for sarcomas.

• Cancer Genome Atlas
• Normalization of Expression Data
• Unsupervised t-Distributed Stochastic Neighbor Embedding Clustering
• Deep Neural Network (TensorFlow)
• Random Forest Algorithm
• Enrichment Analysis (EnrichR R Package)
• Kaplan-Meier Analysis

Why Choose Us?

Our mission is to uncover the mechanisms of sarcoma 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.