Dose response curves

• per compound
• per cell line
• for combinations (provide training set for learning algorithm)

Molecules

• molecular weight
• solubility
• chemical fragments
• shape
• QSAR

Genomic

• CNVs (copy number variations)
  • parts of genome are {duplicated|lost}
  • changes in expression levels -> gene dosage effects
  • most important for gene loss
• SNPs - structural, splice sites, regulation

Methylation

• histones -> expression

Epistatic interactions

• A or B by itself not effective, need to stop both
• Pathway C might regulate/interact with A/B

Network data

• pathways (priority to pathways involved in cancer?)
  • cell growth
  • genome stability
  • loss of contact inhibition
  • metastasis
• PPi
• co-expression data (quantitative relationships b/w proteins and genes)

Feature -> ML -> Combination scores

Round 1 DeadLine has been changed to Dec 3.(See DeadLine File for more details)

1. Infer Drug Synergy without experimental synergy score.
   
2. Making drug predictions based on prior knowledge.
   
3. We are allowed to use ** molecular data for the cell lines, cell response data for all respective mono-therapies, chemical information and putative targets of the compounds**
   
4. Require 740 drug combinations without overlapping with Subchallenge 1.
   -- 4a. A leaderboard set (370 combinations) and a final validation set (370 combinations) are required.
   -- 4b. A full synergy-prediction-matrix (Score either 1 or 0)
   -- 4c. A full synergy-confidence-matrix (Score ranging from 0 to 1)
   
5. The above data will be used to score accuracy of predictions.
   
6. Justify our stratifications and synergy predictions, by giving an algorithm and rationally translatable as biomarkers.
   

• Boris (Hyginn)
• Emma (ehsueh)
• Julian (thejmazz)
• Nathan (njia95)
• Pruthvi (Pruthv1)
• Ashley (AshleyWWW)
• Jack (c5chenpe)
• Bhawan (B-1P)

• Targets: Nathan, Jack
• Data Preprocessing: Ashley, Fred
• Data Reduction: Boris
• Cross Validation: Chris, Julian
• Drug Similarity: Bhawan, Zach, Ricardo, Emma
• Cell Line Similarity: Pruthvi, Jenny

• Scoring:
• Submission: Jack, Nathan
• Documentation: Julian
• QA: