Pinned Repositories
bio_embeddings
Repository containing bio_embeddings resources
ConSurf
Evolutionary conservation estimation of residues or nucleotides
EAT
Embedding-based annotation transfer (EAT) uses Euclidean distance between vector representations (embeddings) of proteins to transfer annotations from a set of labeled lookup protein embeddings to query protein embedding.
goPredSim
nalaf
NLP framework in python for entity recognition and relationship extraction
predictprotein-docker
Based off of the official Rostlab & PredictProtein website installation, as of 2020-09-07, the produced Docker image from this repository will result in a fully functioning predictprotein suite, including all of its required methods. Databases are not included.
ProtTrans
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
SeqVec
Modelling the Language of Life - Deep Learning Protein Sequences
TMbed
Transmembrane proteins predicted through Language Model embeddings
VESPA
VESPA is a simple, yet powerful Single Amino Acid Variant (SAV) effect predictor based on embeddings of the Protein Language Model ProtT5.
Rostlab's Repositories
Rostlab/JS16_ProjectA
In this project we will lay the foundations for our system by integrating data from multiple sources into a central database. The database will serve the apps and the visualization tool that will be developed in other projects.
Rostlab/DM_CS_WS_2016-17
Repo for general info of the course and communication
Rostlab/relna
Biomedical Relation Extraction for Transcription Factor and Gene / Gene Products (part of a Master Thesis at Rostlab, TUM)
Rostlab/JS16_ProjectF
In this project we will build a web portal for our GoT data analysis and visualization system. The website will integrate all the apps created in projects B-D with the help of the integration team assigned to Project E.
Rostlab/JS16_ProjectC_Group10
The known GoT world is vast and stretches over the three continents of Westeros, Essos and Sothorys. Readers of the Ice and Fire books will get acquainted and transported from King's Landing to the borders of the Seven Kingdoms, and further on across the Narrow Sea. Over two thousand characters mentioned in the books have been associated with multiple landmarks in the GoT world. Your mission is to find character-place associations and put those associations on an interactive GoT map. Such a tool will help us figure out where did Gregor “the hound” Clegane went on his travels and how are these travels coincide with the travels of Breanne of Tarth (hint: they never crossed paths in the books, however they had a deadly duel during the show).
Rostlab/SNAP2
SNP effect predictor
Rostlab/JS18_ProjectA_Group2
In this project we created the framework that translates natural language to data visualization creation. This project encompasses loading and querying data and creating simple graphs.
Rostlab/JS16_ProjectB_Group6
Game of Thrones characters are always in danger of being eliminated. The challenge in this assignment is to see at what risk are the characters that are still alive of being eliminated. The goal of this project is to rank characters by their Percentage Likelihood of Death (PLOD). You will assign a PLOD using machine learning approaches.
Rostlab/pssh-parser
A simple JS pssh parser
Rostlab/JS16_ProjectD_Group5
Joffrey Baratheon is one of the most loathed characters in TV history. As a matter of fact people were celebrating his TV death on Twitter. We are interested to learn more on how people feel about different characters by analyzing tweets mentioning GoT characters. In this project you will be analyzing Twitter feeds across a timeline, you will look for the name of GoT characters in that feed and try to identify whether the tweet is positive or negative. You can then generate a metric that evaluates what is the accumulated sentiment expressed on Twitter for that given character at a given point in time, and what is the trend (positive, negative). It will be interesting to intersect the sentiments for characters following the airing of a certain episode (you can easily get the airing date for an episode from the database constructed in Project A).
Rostlab/PP2_CS_WS_2015-16
Communication and documentation for the class
Rostlab/JS16_ProjectB_Group7
Game of Thrones characters are always in danger of being eliminated. The challenge in this assignment is to see at what risk are the characters that are still alive of being eliminated. The goal of this project is to rank characters by their Percentage Likelihood of Death (PLOD). You will assign a PLOD using machine learning approaches.
Rostlab/JS18_ProjectB_Group3
Rostlab/MetaDisorder
Protein sequenced-based Disorder Predictor
Rostlab/MetaStudent
Sequence-based Protein GO / Functional Predictor
Rostlab/some-scripts
General-utility scripts that hopefully are useful for somebody
Rostlab/someNA
Protein DNA/RNA binding predictor
Rostlab/FunFamsConsensus
Provides functionality to compute the binding residue similarity of sequences in the FunFam dataset.
Rostlab/JS18_ProjectA_Group1
Rostlab/JS18_ProjectB_Group4
Rostlab/biojs-vis-UniprotProteinAlignment
Uniprot website's new alignment visualization.
Rostlab/biojs.github.io
Rostlab/JS16_ProjectD_Group4
Joffrey Baratheon is one of the most loathed characters in TV history. As a matter of fact people were celebrating his TV death on Twitter. We are interested to learn more on how people feel about different characters by analyzing tweets mentioning GoT characters. In this project you will be analyzing Twitter feeds across a timeline, you will look for the name of GoT characters in that feed and try to identify whether the tweet is positive or negative. You can then generate a metric that evaluates what is the accumulated sentiment expressed on Twitter for that given character at a given point in time, and what is the trend (positive, negative). It will be interesting to intersect the sentiments for characters following the airing of a certain episode (you can easily get the airing date for an episode from the database constructed in Project A).
Rostlab/JS16_ProjectE
In this project we will put all the apps developed in Projects B, C and D into the website that is developed in Project F. In this project you will pull the code from each project repository, compile it with the set of dependencies and package the apps, so that they can be easily called from the web site developed in project F.
Rostlab/reprof
Protein Secondary Structure Predictor