I am trying to solve the following question: Given a text file containing a bunch of biological information, find out the one gene which is {up/down}regulated. Now, for this I have many such (60K) files and have annotated some (1000) of them as to which gene is {up/down}regulated.ConditionsMany sentences in the file have some gene name mention and some of them also have neighboring text that can help one decide if this is indeed the gene being modulated.Some files also have NO gene modulated. But these still have gene mentions.Given this, I wanted to ask (having absolutely no background in ML), what sequence learning algorithm/tool do I use that can take in my annotated (training) data (after probably converting the text to vectors somehow!) and can build a good model on which I can then test more files?Example dataTitle:    Assessment of Thermotolerance in preshocked hsp70(-/-) and  (+/+) cells Organism:  Mus musculus Experiment type:   Expression profiling by array Summary:   From preliminary experiments, HSP70  deficient MEF cells display moderate thermotolerance to a severe heatshock of 45.5 degrees after a mild preshock at 43 degrees, even in the absence of hsp70 protein. We would like to determine which genes in these cells are being activated to account for this thermotolerance. AQP has also been reported to be important.Keywords: thermal stress, heat shock response, knockout, cell culture, hsp70       Overall design:    Two cell lines are analyzed - hsp70 knockout and hsp70 rescue cells. 6 microarrays from the (-/-)knockout cells are analyzed (3 Pretreated vs 3 unheated controls). For the (+/+) rescue cells, 4 microarrays are used (2 pretreated and 2 unheated controls). Cells were plated at 3k/well in a 96 well plate, covered with a gas permeable sealer and heat shocked at  43degrees for 30 minutes at the 20 hr time point. The RNA was harvested at 3hrs after heat treatmentHere my main gene is hsp70 and it is down-regulated (deducible from hsp(-/-) or HSP70 deficient). Many other gene names are also there like AQP. There could be another file with no gene modified at all. In fact, more files have no actual gene modulation than those who do, and all contain gene name mentions.Any idea would be great!!
Applying Machine learning in biological data
machine learning;natural language processing