Feature engineering is still (or is no longer) important

**Have you checked the [list of proposed rules](https://github.com/Benjamin-Lee/deep-rules/issues?q=is%3Aissue+is%3Aopen+label%3Arule) to see if the rule has already been proposed?**

- [x] Yes

**Did you add yourself as a [contributor](https://github.com/Benjamin-Lee/deep-rules/blob/master/contributors.md) by making a pull request if this is your first contribution?**

- [x] Yes, I added myself or am already a contributor

**Feel free to elaborate, rant, and/or ramble.**

**Any citations for the rule?** (peer-reviewed literature preferred but not required)
- Seeking relevant citations from other contributors

I have seen assertions that deep learning reduces or eliminates the need for feature engineering because the network itself constructs features from "raw" inputs. Most examples supporting this assertion come from imaging tasks.

I propose having a rule discussing whether or not this is true in biology, or perhaps the types of biological tasks for which feature engineering is still required. Unlike some of our other rules that are applicable to all DL or all ML in biology, this one would be specific to DL in biology.

I have some initial thoughts about feature engineering in DL but am hoping to gather a broad set of references before proposing the final rule.

Start with the proposed-rules issue list and the contributor guidance in contributors.md, then gather the biological deep-learning literature requested in the issue. Define the rule's scope for biological tasks and support the final wording with relevant citations; done means an agreed, citable rule proposal.

Written by the indexing model from the issue text.