{"id":16130979,"url":"https://github.com/rvinas/predicting-subcellular-location","last_synced_at":"2025-08-05T08:14:15.300Z","repository":{"id":129460798,"uuid":"123732618","full_name":"rvinas/predicting-subcellular-location","owner":"rvinas","description":"Predicting the subcellular location of eukaryotic proteins with support vector machines","archived":false,"fork":false,"pushed_at":"2018-03-23T13:24:00.000Z","size":5394,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-17T13:43:36.295Z","etag":null,"topics":["amino-acid-sequence","amino-acids","bioinformatics","biopython","proteomics","random-forest","svm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rvinas.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2018-03-03T21:23:50.000Z","updated_at":"2018-08-22T18:45:38.000Z","dependencies_parsed_at":"2023-07-29T23:01:12.284Z","dependency_job_id":null,"html_url":"https://github.com/rvinas/predicting-subcellular-location","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rvinas/predicting-subcellular-location","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rvinas%2Fpredicting-subcellular-location","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rvinas%2Fpredicting-subcellular-location/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rvinas%2Fpredicting-subcellular-location/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rvinas%2Fpredicting-subcellular-location/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rvinas","download_url":"https://codeload.github.com/rvinas/predicting-subcellular-location/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rvinas%2Fpredicting-subcellular-location/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268859123,"owners_count":24318874,"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","status":"online","status_checked_at":"2025-08-05T02:00:12.334Z","response_time":2576,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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","amino-acids","bioinformatics","biopython","proteomics","random-forest","svm"],"created_at":"2024-10-09T22:18:54.675Z","updated_at":"2025-08-05T08:14:15.251Z","avatar_url":"https://github.com/rvinas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Predicting Subcellular Location\n\n## Overview\n\nWithin the last few years the complete sequence has been determined for over 3000 genomes. Predicting the function of a protein has proved to be a difficult task where no clear homology to proteins of known function exists. Knowing the subcellular location of such proteins might be a crucial feature to determine their function. This work presents an approach for predicting the subcellular location (nuclear, mitochondrial, cytosolic or secreted) of non-homologous proteins. This method extracts several N-terminal and global features, and performs classification using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel.\n\nThis work corresponds to the coursework of the module COMPGI10 Bioinformatics, University College London.\nPlease find further information at: http://www0.cs.ucl.ac.uk/staff/D.Jones/coursework/\n\n## Prerequisites\n- Python 3.5\n- Numpy 1.14.0\n- Pandas 0.22.0\n- Scikit-learn 0.19.1\n- Biopython 1.70\n- Matplotlib 2.1.2\n- Seaborn 0.8.1\n\n## Installation\nRun `sudo pip install -r requirements.txt`\n\n## Structure\n- data/\n  - cyto.fasta: Amino acid sequences from cytosol in FASTA format\n  - mito.fasta: Amino acid sequences from mitochondria in FASTA format\n  - nucleus.fasta: Amino acid sequences from nucleus in FASTA format\n  - secreted.fasta: Secreted amino acid sequences in FASTA format\n  - blind.fasta: 20 unlabeled amino acid sequences in FASTA format\n- src/\n  - data_pipeline.py: Parses FASTA data and creates features from the amino acid sequences\n  - utils.py: Subcellular location prediction utilities\n  - eda.ipynb: Exploratory Data Analysis\n  - svm.py: Trains a Support Vector Machine to perform subcellular location prediction\n  - rf.py: Trains a Random Forest to perform subcellular location prediction\n  - nn.py: Trains a feed-forward network to perform subcellular location prediction\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frvinas%2Fpredicting-subcellular-location","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frvinas%2Fpredicting-subcellular-location","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frvinas%2Fpredicting-subcellular-location/lists"}