{"id":16730837,"url":"https://github.com/dillondaudert/pssp_lstm","last_synced_at":"2025-06-16T14:06:17.331Z","repository":{"id":89307552,"uuid":"123181920","full_name":"dillondaudert/pssp_lstm","owner":"dillondaudert","description":"Recurrent neural network implementations for protein secondary structure prediction and language models","archived":false,"fork":false,"pushed_at":"2018-09-18T02:19:45.000Z","size":539,"stargazers_count":7,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-24T10:05:22.955Z","etag":null,"topics":["amino-acid-sequence","deep-learning","deep-neural-networks","jupyter-notebook","language-models","lstm","paper","prediction","pretrained-models","protein","python3","recurrent-neural-networks","rnn","secondary","structure","structure-prediction","tensorflow","unsupervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dillondaudert.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-02-27T20:07:57.000Z","updated_at":"2024-01-12T14:25:14.000Z","dependencies_parsed_at":"2023-07-19T01:01:25.315Z","dependency_job_id":null,"html_url":"https://github.com/dillondaudert/pssp_lstm","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dillondaudert%2Fpssp_lstm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dillondaudert%2Fpssp_lstm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dillondaudert%2Fpssp_lstm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dillondaudert%2Fpssp_lstm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dillondaudert","download_url":"https://codeload.github.com/dillondaudert/pssp_lstm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248208564,"owners_count":21065202,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["amino-acid-sequence","deep-learning","deep-neural-networks","jupyter-notebook","language-models","lstm","paper","prediction","pretrained-models","protein","python3","recurrent-neural-networks","rnn","secondary","structure","structure-prediction","tensorflow","unsupervised-learning"],"created_at":"2024-10-12T23:34:57.859Z","updated_at":"2025-04-10T11:12:51.395Z","avatar_url":"https://github.com/dillondaudert.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Recurrent Neural Networks for Protein Secondary Structure Prediction\nThis repo contains ongoing work exploring recurrent neural network models as applied to protein secondary structure prediction.\n\nThe [pssp_lstm](./pssp_lstm/) module contains an implementation of the LSTM RNN specified in [Sonderby \u0026 Winther, 2015](https://arxiv.org/pdf/1412.7828.pdf). See the README in that folder for more details and a user guide.\n\nThe [lm_pretrain](./lm_pretrain/) module allows users to train bidirectional language models that can be combined with bidirectional RNNs for protein secondary structure prediction. See the README in that folder for more details and a user guide.\n\n## Further Work\nThis repo is under development. Current work is focusing on expanding the functionality of `lm_pretrain` to allow for more flexible models, and for exploring different ways of integrating pretrained LMs into BDRNNs.\n\nWork on implementing models from other papers is currently on pause.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdillondaudert%2Fpssp_lstm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdillondaudert%2Fpssp_lstm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdillondaudert%2Fpssp_lstm/lists"}