- HPN-DREAM Breast Cancer Network Inference Challenge - HPN-DREAM Breast Cancer Network Inference Challenge
- NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge - NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge
- National Brain Tumor Society – DREAM Cancer Prediction Challenge - National Brain Tumor Society – DREAM Cancer Prediction Challenge
- The Whole-Cell Parameter Estimation DREAM Challenge - The Whole-Cell Parameter Estimation DREAM Challenge
- Network Topology and Parameter Inference Challenge - Participants are asked to develop and/or apply optimization methods, including the selection of the most informative experiments, to accurately estimate parameters and predict outcomes of perturbations in Systems Biology models, given the complete and incomplete structure of the model for a gene regulatory network.
- Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge - The goal of the breast cancer prognosis Challenge is to assess the accuracy of computational models designed to predict breast cancer survival, based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles.
- NCI-DREAM Drug Sensitivity Prediction Challenge - The challenge is to use genomic information to build models capable of ranking the sensitivity of cancer cell lines to a set of small molecule compounds or their combinations.
- The DREAM Phil Bowen ALS Prediction Prize4Life - The goal of this challenge is to predict the future progression of disease in ALS patients based on the patient’s current disease status. The data available to make this prediction includes demographics, medical and family history data, functional measures, vital signs, and lab data (blood chemistry/hematology/urinalysis). These data have been obtained from industry, academic, and government-funded clinical trials. The prize award is $50,000.
- DREAM6 Estimation of Model Parameters Challenge - Inference of the kinetic parameters of three gene regulatory networks by iterative optimization and experimental design
- DREAM6 Alternative Splicing Challenge - Reconstruct the alternatively spliced mRNA transcripts from short-read mRNA-seq data
- DREAM6 Gene Expression Prediction Challenge - Predict gene expression levels from promoter sequences in eukaryotes
- DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge - The goal of this challenge is to diagnose Acute Myeloid Leukaemia from patient samples using flow cytometry data.
- Epitope-Antibody Recognition (EAR) Challenge - Predict the binding specificity of peptide-antibody interactions.
- TF-DNA Motif Recognition Challenge - Predict the specificity of a Transcription Factor binding to a 35-mer probe.
- Systems Genetics Challenge - Predict disease phenotypes and infer Gene Networks from Systems Genetics data
- Network Inference Challenge - Infer simulated and in-vivo gene regulation networks
- Peptide Recognition Domain (PRD) Specificity Prediction - Predict protein-protein interactions at the level of binding domains and peptides
- In Silico Network Challenge - Infer simulated gene regulation networks and predict gene expression measurements
- Predictive Signaling Network Modeling - Predict phosphoprotein measurements using an interpretable, predictive network
- Signaling Cascade Identification - Infer a signaling network from flow cytometery data
- Signaling Response Prediction - Predict missing protein concentrations from a large corpus of measurements
- Gene Expression Prediction - Predict missing gene expression measurements
- In Silico Network Challenge - Infer simulated gene regulation networks
- BCL6 Transcriptional Target Prediction - Predict the genes that a transcription factor binds to
- Protein-Protein Interaction Network Inference - Predict a PPI network of 47 proteins
- Synthetic Five-Gene Network Inference - Infer a gene regulation network from qPCR and microarray measurements
- In Silico Network Challenge - Infer various network topologies from simulated "measurements"
- Genome-Scale Network Inference - Reconstruct a genome scale regulatory network from a large collection of microarrays