{"action":"created","issue":{"url":"https://api.github.com/repos/greenelab/deep-review/issues/6","repository_url":"https://api.github.com/repos/greenelab/deep-review","labels_url":"https://api.github.com/repos/greenelab/deep-review/issues/6/labels{/name}","comments_url":"https://api.github.com/repos/greenelab/deep-review/issues/6/comments","events_url":"https://api.github.com/repos/greenelab/deep-review/issues/6/events","html_url":"https://github.com/greenelab/deep-review/issues/6","id":169624618,"number":6,"title":"Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders.","user":{"login":"cgreene","id":542643,"avatar_url":"https://avatars.githubusercontent.com/u/542643?v=3","gravatar_id":"","url":"https://api.github.com/users/cgreene","html_url":"https://github.com/cgreene","followers_url":"https://api.github.com/users/cgreene/followers","following_url":"https://api.github.com/users/cgreene/following{/other_user}","gists_url":"https://api.github.com/users/cgreene/gists{/gist_id}","starred_url":"https://api.github.com/users/cgreene/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cgreene/subscriptions","organizations_url":"https://api.github.com/users/cgreene/orgs","repos_url":"https://api.github.com/users/cgreene/repos","events_url":"https://api.github.com/users/cgreene/events{/privacy}","received_events_url":"https://api.github.com/users/cgreene/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/greenelab/deep-review/labels/paper","name":"paper","color":"cc317c"},{"url":"https://api.github.com/repos/greenelab/deep-review/labels/unsupervised","name":"unsupervised","color":"d4c5f9"}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":{"url":"https://api.github.com/repos/greenelab/deep-review/milestones/1","html_url":"https://github.com/greenelab/deep-review/milestones/Initial%20review%20of%20primary%20literature","labels_url":"https://api.github.com/repos/greenelab/deep-review/milestones/1/labels","id":1933292,"number":1,"title":"Initial review of primary literature","description":"","creator":{"login":"cgreene","id":542643,"avatar_url":"https://avatars.githubusercontent.com/u/542643?v=3","gravatar_id":"","url":"https://api.github.com/users/cgreene","html_url":"https://github.com/cgreene","followers_url":"https://api.github.com/users/cgreene/followers","following_url":"https://api.github.com/users/cgreene/following{/other_user}","gists_url":"https://api.github.com/users/cgreene/gists{/gist_id}","starred_url":"https://api.github.com/users/cgreene/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cgreene/subscriptions","organizations_url":"https://api.github.com/users/cgreene/orgs","repos_url":"https://api.github.com/users/cgreene/repos","events_url":"https://api.github.com/users/cgreene/events{/privacy}","received_events_url":"https://api.github.com/users/cgreene/received_events","type":"User","site_admin":false},"open_issues":15,"closed_issues":0,"state":"open","created_at":"2016-08-09T18:55:41Z","updated_at":"2016-08-09T19:00:03Z","due_on":"2016-08-31T00:00:00Z","closed_at":null},"comments":5,"created_at":"2016-08-05T14:50:44Z","updated_at":"2016-08-09T19:21:45Z","closed_at":null,"body":"Paper needs to be read carefully for relevance.\r\nhttp://dx.doi.org/10.1142/9789814644730_0014"},"comment":{"url":"https://api.github.com/repos/greenelab/deep-review/issues/comments/238661945","html_url":"https://github.com/greenelab/deep-review/issues/6#issuecomment-238661945","issue_url":"https://api.github.com/repos/greenelab/deep-review/issues/6","id":238661945,"user":{"login":"akundaje","id":146193,"avatar_url":"https://avatars.githubusercontent.com/u/146193?v=3","gravatar_id":"","url":"https://api.github.com/users/akundaje","html_url":"https://github.com/akundaje","followers_url":"https://api.github.com/users/akundaje/followers","following_url":"https://api.github.com/users/akundaje/following{/other_user}","gists_url":"https://api.github.com/users/akundaje/gists{/gist_id}","starred_url":"https://api.github.com/users/akundaje/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/akundaje/subscriptions","organizations_url":"https://api.github.com/users/akundaje/orgs","repos_url":"https://api.github.com/users/akundaje/repos","events_url":"https://api.github.com/users/akundaje/events{/privacy}","received_events_url":"https://api.github.com/users/akundaje/received_events","type":"User","site_admin":false},"created_at":"2016-08-09T19:21:44Z","updated_at":"2016-08-09T19:21:44Z","body":"@cgreene Fully agree with you. Only caution that I think is again not stressed enough in current reviews is the interpretability of even a single layer model should be done very cautiously. Neural nets learn distributed representations and even though individual neurons/filters may appear interpretable, they should not be overinterpretted as \"this filter is a CTCF motif\" like some papers do. There are often many filters that collectively capture a single predictive pattern like a motif. There are ways to re-derive these. Looking at filters for an intuitive feel of what the network is great. Using individual filters outside of the network is dangerous and wrong IMHO.\r\n\r\nOn a side note, sorry if I'm being negatively critical of too many things :). Just feel like the use of deep nets in compbio is still in its infancy and if we can avoid propagating suboptimal practices we should do that through this review and papers."}}
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