CODEWITHSUNDEEP
machine-learningnlpsequence
user1995
user1995

Reputation: 538

Applying MACHINE learning in biological text data

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.

Conditions -

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 data -

Title: 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 treatment

Here 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!!

Upvotes: 1

Views: 87

Answers (1)

Vlad
Vlad

Reputation: 9481

If you have no background in ML I suggest buying a product like this one, this one or this one. These products where in development for decades with team budgets in millions.

What you are trying to do is not that simple. For example a lot of papers contain negative statements by first citing the original statement from another paper and then negating it. In your example how are you going to handle this:

AQP has also been reported to be important by Doe et al. However, this study suggest that this might not be the case.

Also, if you are looking into large corpus of biomedical research papers, or for this matter any corpus of research papers. You will find tons of papers that suggest something for example gene being up-regulated or not, and then there is one paper published in Cell magazine that all previous research has been mistaken.

To make matters worse, gene/protein names are not that stable. Besides few famous ones like P53. There is a bunch of run of the mill ones that are initially thought that they are one gene, but later it turns out that these are two different things. When this happen there are two ways community handles it. Either both of the genes get new names (usually with some designator at the end) or if the split is uneven the larger class retains original name and the second one gets the new name. To compound this problem, after this split happens not all researchers get the memo at instantly, so there is still stream of publications using old publication.

These are just two simple problems, there are 100s of these.

If you are doing this for personal enrichment. Here are some suggestions:

  1. Build a language model on biomedical papers. Existing language models are usually built from news-wire sources or from social media data. All three of the corpora claim to be written in English language. But in reality these are three different languages with their own grammar and vocabulary

  2. Look into things like embeddings and word2vec.

  3. Look into Kaggle competitions, this is somewhat popular topic there.

  4. Subscribe to KDD and BIBM magazines or find them in nearby library. There are 100s of papers on this subject.

Upvotes: 2

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