Sylvester Data Portal (SDP)
A novel multi-omics research platform developed at the Sylvester Comprehensive Cancer Center. SDP facilitates the storage, management, analysis, and sharing of biomedical datasets. By leveraging state-of-the-art software and hybrid-cloud computing technologies, SDP provides researchers with well-annotated, FAIR (findable, accessible, interoperable, and reusable) and analysis-ready datasets, along with well-established bioinformatics tools and automated processing pipelines.
SCAN 360
Visualizes cancer incidence, mortality rates, late stage diagnosis, and years of potential life lost, and maps a variety of indicators, including sociodemographics, cancer histology and staging, risk behaviors, screening behavior, environmental factors, hazardous sites, health insurance access, prevalence of potential co-morbidities, housing characteristics, and levels of residential segregation.
Cancer Gene Prognosis Atlas (CGPA)
Innovative online tool designed to enhance gene-centric biomarker discovery and validation in cancer genomics. CGPA offers comprehensive analysis capabilities, addressing the limitations of existing databases by offering multivariable and multi-gene survival models, crucial for accurate prognostic assessments. It provides an intuitive platform for researchers and clinicians, facilitating the exploration of gene expression’s impact on clinical outcomes, a task often hindered by the complexity and volume of data in current cancer transcriptome databases. CGPA’s unique functionalities also enable the effective mining of gene correlations and the creation of customizable gene panels, significantly advancing the field of oncological research.
PluMA
The PluMA Initiative: Plugin-Based Microbiome Analysis, an effort to transform microbiome analysis from individual to community-driven.
A flexible and lightweight analysis pipelines through which a developer can implement a new algorithm in their programming language of choice, and easily test and debug within a larger pipeline alongside stages in different languages that potentially use different file formats.
MOGAT
A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction
CyFinder
A Cytoscape plugin, helps find subgraph biomarkers from biological networks.
KNET
Compound-kinase activity prediction platform to predict the probability of inhibition for small molecules, represented by SMILES strings, against a broad range of protein kinases. The predictions are powered by two multitask deep neural network (MTDNN) models trained using large-scale bioactivity data with over 1 million human kinase bioactivity annotations and more than 400,000 unique small molecules from databases ChEMBL and the Kinase Knowledge Base (KKB): Kinase-Cutoff6: This model covers 406 kinases and classifies molecules as active or inactive based on a pActivity cutoff value of 6. It provides wide kinase coverage, offering broad insights across many targets. Kinase-Cutoff7: This model covers 328 kinases and classifies molecules using a stricter pActivity cutoff value of 7. While the kinase coverage is smaller, it provides higher accuracy and more reliable predictions. It is ideal for users seeking precise inhibition probabilities for a refined set of kinases.