I have improved network inference part of the script slightly by changing the way of comparing intervention (presence of inhibitor and stimulus) and no intervention (presence of stimulus) data from in silico part.
Yesterday, I managed to infer a network for some part of in silico data from the challenge. Since the challenge also asks for scoring the edges in networks, I developed the script further and add a function for that.
Lately, I have been writing an R script to infer network using in silico data. Last version of the script was reading MIDAS file and plotting expression profiles. I have modified it and now it reads MIDAS file, does some analyses and prints causal relations to a file. This file is a SIF file as required.
For in silico data network inference I decided to develop a script because the existing tools have bugs and they are not compatible with the data. At the same time, I will try to report bugs and the compatibility issues to developers.
DREAM8 organizers plan a webinar about HPN-DREAM Breast Cancer Network Inference Challenge on July 19, at 10:30 - 11:30 (PDT / UTC -7). General setup of the challenge, demo submissions to the leaderboard will be discussed and also questions about the challenge will be accepted during webinar. The number of the participants to the challenge is also announced: 138.
I had a meeting with BiGCaT this week and we discussed DREAM Breast Cancer Challenge. I presented the challenge and also some ways that I have found to solve the first sub-challenge network inference. Tina, from BiGCaT, suggested starting with in silico data which is much simpler than breast cancer data. Later, I can use the methods I develop for in silico data in experimental data.
Actually, I signed up Rosalind.info 8 months ago, I didn’t really play around with it. But last week, in a BiGCaT science cafe, after I learnt it, I was more interested than before and I just started solving problems.
With CellNOptR, we will try to construct network models for the challenge. For this, the tool needs two inputs. First one is a special data object called CNOlist that stores vectors and matrices of data. Second one is a .SIF file that contains prior knowledge network which can be obtained from pathway database and analysis tools.
This sub-challenge has several requirements:
Last semester, I took a course from Informatics Institute at METU called “Biological Databases and Data Analysis Tools” where first we learned what is a database and how to do queries on it. Also, the technology behind databases are taught. Then, we learned many biological databases and data analysis tools available. These include gene, protein and pathway databases, tools for creating databases.