{"action":"created","comment":{"url":"https://api.github.com/repos/greenelab/deep-review/pulls/comments/112924214","pull_request_review_id":34269693,"id":112924214,"diff_hunk":"@@ -78,6 +78,53 @@ able to significantly outperform a random forest classifier trained on gene\n expression changes. These approaches suggest promising directions to improve\n the efficiency of clinical trials and accelerate drug development.\n \n+##### Drug repositioning\n+\n+Drug repositioning (or repurposing) is an attractive option for delivering new\n+drugs to the market because of the high costs and failure rates associated with\n+more traditional drug discovery approaches [@doi:10.1016/j.jhealeco.2016.01.012\n+@doi:10.1038/nrd4609]. Ever since the concept of the Connectivity Map was\n+proposed [@doi:10.1126/science.1132939], several advanced computational methods\n+have been applied to formulate and validate drug repositioning hypotheses\n+[@doi:10.1093/bib/bbv020 @doi:10.1093/bib/bbw112 @doi:10.1093/bib/bbw110]. Using\n+supervised learning and recommender systems to tackle this type of problems is\n+proving successful in different scenarios, especially when coupled with\n+topological information from protein - protein or protein - compound interaction\n+networks [@doi:10.1186/1758-2946-5-30 @doi:10.1021/ci500340n\n+@doi:10.1186/s12859-015-0845-0]. Menden et al\n+[@doi:10.1371/journal.pone.0061318] used a shallow neural network to predict\n+sensitivity of cancer cell lines to drug treatment using both cell line and drug\n+features, opening the door to precision medicine and drug repositioning\n+opportunities in cancer. More recently, Aliper et al\n+[@doi:10.1021/acs.molpharmaceut.6b00248] used gene- and pathway-level drug\n+perturbation transcriptional profiles from the Library of Network-Based Cellular\n+Signatures (LINCS) [@doi:10.3389/fgene.2014.00342] to train a fully connected\n+deep neural network able to predict drug therapeutic uses and indications. By\n+using confusion matrices and leveraging misclassification, the authors formulate\n+a number of interesting hypotheses, including repurposing cardiovascular drugs\n+such as otenzepad and pinacidil for neurological disorders.\n+\n+Drug repositioning can also be approached by attempting to predict novel drug -\n+target interactions and then repurposing the drug for the associated\n+indication [doi:10.1371/journal.pcbi.1005219 @doi:10.1371/journal.pcbi.1005135].\n+Wang et al [@doi:10.1109/BIBM.2014.6999129] devised a pairwise input neural\n+network with two hidden layers that takes two inputs, a drug and a target\n+binding site, and predicts whether they interact. Wang et al\n+[@doi:10.1093/bioinformatics/btt234] trained individual RBMs for each target in\n+a drug - target interaction network and used these models to predict novel\n+interactions pointing to new indications for existing drugs. Wen et al.\n+[@doi:10.1021/acs.jproteome.6b00618] extended this concept to\n+deep learning by creating a deep belief network of stacked RBMs, DeepDTIs,\n+predicting interaction on the basis of chemical structure and protein sequence\n+features.","path":"sections/05_treat.md","position":42,"original_position":42,"commit_id":"db09baa69f9ce4fa426b0bc089c0060221d2f492","original_commit_id":"db09baa69f9ce4fa426b0bc089c0060221d2f492","user":{"login":"agitter","id":3532898,"avatar_url":"https://avatars3.githubusercontent.com/u/3532898?v=3","gravatar_id":"","url":"https://api.github.com/users/agitter","html_url":"https://github.com/agitter","followers_url":"https://api.github.com/users/agitter/followers","following_url":"https://api.github.com/users/agitter/following{/other_user}","gists_url":"https://api.github.com/users/agitter/gists{/gist_id}","starred_url":"https://api.github.com/users/agitter/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agitter/subscriptions","organizations_url":"https://api.github.com/users/agitter/orgs","repos_url":"https://api.github.com/users/agitter/repos","events_url":"https://api.github.com/users/agitter/events{/privacy}","received_events_url":"https://api.github.com/users/agitter/received_events","type":"User","site_admin":false},"body":"Are there connections to be made here between these drug-target interaction approaches and the ligand-based screening approaches? Any similarities in the featurization and representation of chemicals?  Should we contrast the scale of the datasets, e.g. the number of compounds considered?\r\n\r\nI plan to add a paragraph on virtual screening methods that use protein structure (e.g #56) and am thinking about how to contrast them with drug-target interaction prediction.","created_at":"2017-04-24T11:41:01Z","updated_at":"2017-04-24T11:53:58Z","html_url":"https://github.com/greenelab/deep-review/pull/341#discussion_r112924214","pull_request_url":"https://api.github.com/repos/greenelab/deep-review/pulls/341","_links":{"self":{"href":"https://api.github.com/repos/greenelab/deep-review/pulls/comments/112924214"},"html":{"href":"https://github.com/greenelab/deep-review/pull/341#discussion_r112924214"},"pull_request":{"href":"https://api.github.com/repos/greenelab/deep-review/pulls/341"}}},"pull_request":{"url":"https://api.github.com/repos/greenelab/deep-review/pulls/341","id":117155232,"html_url":"https://github.com/greenelab/deep-review/pull/341","diff_url":"https://github.com/greenelab/deep-review/pull/341.diff","patch_url":"https://github.com/greenelab/deep-review/pull/341.patch","issue_url":"https://api.github.com/repos/greenelab/deep-review/issues/341","number":341,"state":"open","locked":false,"title":"Drug repositioning [Treat + Intro]","user":{"login":"enricoferrero","id":4481814,"avatar_url":"https://avatars0.githubusercontent.com/u/4481814?v=3","gravatar_id":"","url":"https://api.github.com/users/enricoferrero","html_url":"https://github.com/enricoferrero","followers_url":"https://api.github.com/users/enricoferrero/followers","following_url":"https://api.github.com/users/enricoferrero/following{/other_user}","gists_url":"https://api.github.com/users/enricoferrero/gists{/gist_id}","starred_url":"https://api.github.com/users/enricoferrero/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/enricoferrero/subscriptions","organizations_url":"https://api.github.com/users/enricoferrero/orgs","repos_url":"https://api.github.com/users/enricoferrero/repos","events_url":"https://api.github.com/users/enricoferrero/events{/privacy}","received_events_url":"https://api.github.com/users/enricoferrero/received_events","type":"User","site_admin":false},"body":"OK, here's my attempt at a drug repositioning subsection for the Treat section. I've also slightly modified the relevant paragraph in the Introduction for consistency.\r\n\r\nFeedback very welcome!\r\nCheers","created_at":"2017-04-23T16:06:56Z","updated_at":"2017-04-24T11:53:58Z","closed_at":null,"merged_at":null,"merge_commit_sha":"38c1185ff351d260880a15484370a855ece5acfd","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/greenelab/deep-review/pulls/341/commits","review_comments_url":"https://api.github.com/repos/greenelab/deep-review/pulls/341/comments","review_comment_url":"https://api.github.com/repos/greenelab/deep-review/pulls/comments{/number}","comments_url":"https://api.github.com/repos/greenelab/deep-review/issues/341/comments","statuses_url":"https://api.github.com/repos/greenelab/deep-review/statuses/db09baa69f9ce4fa426b0bc089c0060221d2f492","head":{"label":"enricoferrero:drug-repositioning","ref":"drug-repositioning","sha":"db09baa69f9ce4fa426b0bc089c0060221d2f492","user":{"login":"enricoferrero","id":4481814,"avatar_url":"https://avatars0.githubusercontent.com/u/4481814?v=3","gravatar_id":"","url":"https://api.github.com/users/enricoferrero","html_url":"https://github.com/enricoferrero","followers_url":"https://api.github.com/users/enricoferrero/followers","following_url":"https://api.github.com/users/enricoferrero/following{/other_user}","gists_url":"https://api.github.com/users/enricoferrero/gists{/gist_id}","starred_url":"https://api.github.com/users/enricoferrero/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/enricoferrero/subscriptions","organizations_url":"https://api.github.com/users/enricoferrero/orgs","repos_url":"https://api.github.com/users/enricoferrero/repos","events_url":"https://api.github.com/users/enricoferrero/events{/privacy}","received_events_url":"https://api.github.com/users/enricoferrero/received_events","type":"User","site_admin":false},"repo":{"id":88962515,"name":"deep-review","full_name":"enricoferrero/deep-review","owner":{"login":"enricoferrero","id":4481814,"avatar_url":"https://avatars0.githubusercontent.com/u/4481814?v=3","gravatar_id":"","url":"https://api.github.com/users/enricoferrero","html_url":"https://github.com/enricoferrero","followers_url":"https://api.github.com/users/enricoferrero/followers","following_url":"https://api.github.com/users/enricoferrero/following{/other_user}","gists_url":"https://api.github.com/users/enricoferrero/gists{/gist_id}","starred_url":"https://api.github.com/users/enricoferrero/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/enricoferrero/subscriptions","organizations_url":"https://api.github.com/users/enricoferrero/orgs","repos_url":"https://api.github.com/users/enricoferrero/repos","events_url":"https://api.github.com/users/enricoferrero/events{/privacy}","received_events_url":"https://api.github.com/users/enricoferrero/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/enricoferrero/deep-review","description":"A 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