- HPN-DREAM Breast Cancer Network Inference Challenge - Participants in this Challenge will be provided with an extensive proteomics time-course dataset on four breast cancer cell lines and tasked with analyzing these data to solve the following 3 sub-challenges: 1) build network models that represent the active cell signaling pathways in breast cancer, 2) predict the dynamic response of various phospho-proteins to drug perturbations, and 3) propose novel strategies to visualize these high dimensional data.
- NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge - We will provide genetics and transcriptomics information of the 1000 Genomes Project (www.1000genomes.org), as well as cytotoxicity measures derived from compound exposure to over a hundred toxic agents using the 1000 genomes lymphoblastoid cell lines. Participants are tasked with solving two related subchallenges: (1) develop predictive models of cytotoxicity using genetic and genomic data to predict individual responses to compound exposure and (2) use chemical attributes to predict population-based cytotoxicity characteristics (median, variance) for a set of compounds.
- The Whole-Cell Parameter Estimation DREAM Challenge - Participants will be provided with a whole cell model of Mycoplasma genitalium and tasked with estimating the model parameters for specific biological processes from simulated data. The simulated data to be provided represents possible measurements in actual experiments: participants will will have a credit budget and will be able to purchase simulated data on demand with the aim to refine the parameters under estimation.
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. Results were published in Meyer et al BMC Sys Bio 2014.
- 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.
- 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.
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.
- DREAM6 Alternative Splicing Challenge - Reconstruct the alternatively spliced mRNA transcripts from short-read mRNA-seq data
- DREAM6 Estimation of Model Parameters Challenge - Inference of the kinetic parameters of three gene regulatory networks by iterative optimization and experimental design
- 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