feat: add interval logic for l2g features

[xyg123]

✨ Context

Adding interval based features to the l2g model, based on the feature list (opentargets/issues#3521).
opentargets/issues#3512

🛠 What does this PR implement

• Implementation of PCHIC-based interval features for the L2G gene prediction model.
• Added back interval processing steps into the L2G feature generation step.

🙈 Missing

More features from anderson + thurman.

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[addramir]

Why is it maximum? According to the table and what we discussed it should be mean?
https://docs.google.com/spreadsheets/d/1wUs1AprRCCGItZmgDhc1fF5BtwCSosdzFv4NQ8V6Dtg/edit?gid=452826388#gid=452826388

[ireneisdoomed]

Thank you for the changes Jack!!!

The logic to build the features looks good! Please see my comments, but they are more along the lines of how we process the interval data in the L2G step.
I suggested processing all interval sources to make the process simpler, but since the code is accommodated to take source names and paths individually and changing it is a mess, it's also fine to leave it like that as long as the interval_paths parameter is correctly configured.

The implemented changes wouldn't run, because of the creation of a Interval dataset with a mismatching schema. I would encourage you to:

• add any features you add to the test_l2g_feature_matrix.py suite, to make sure that the code doesnt crash
• In the same file, add a semantic test for the common logic
• Update the documentation pages
• Pull dev branch to bring the changes to the feature matrix step

[xyg123]

We have investigated the Intervals-only V2G dataset, the problem is that one variant can contain interval information from multiple genes (up to 200) from one interval source, in addition, the Interval-only V2G dataset is too big, and can potentially be 4x the size of the variant index.

Therefore, it is not feasible to include interval data inside the variant index, instead, the "processed interval" dataset (requires gene index), will be generated each release, and the feature generation step will intersect variants found within studyLocus to the "processed interval" using an overlap approach to generate the scores needed for the interval features.