Rostlab/JS18_ProjectA_Group2

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Rostlab/JS18_ProjectA_Group2

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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.

nalaf

NLP framework in python for entity recognition and relationship extraction

goPredSim

ConSurf

Evolutionary conservation estimation of residues or nucleotides

bindPredict

Prediction of binding residues for metal ions, nucleic acids, and small molecules.

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.

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.

DM_CS_WS_2016-17

Repo for general info of the course and communication

VESPA

VESPA is a simple, yet powerful Single Amino Acid Variant (SAV) effect predictor based on embeddings of the Protein Language Model ProtT5.

ProNA2020

ProNA2020: System predicting protein-DNA, protein-RNA and protein-protein binding sites from sequence

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.

relna

Biomedical Relation Extraction for Transcription Factor and Gene / Gene Products (part of a Master Thesis at Rostlab, TUM)

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.

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).

FunFamsClustering

SNAP2

SNP effect predictor

nala

Text mining of natural language mutations mentions

LocText

Relation Extraction (RE) of: Proteins <--> Cell Compartments

LambdaPP

LocNuclei

Prediction of subnuclear locations

PredictProtein

PredictProtein is an automatic service for protein database searches and the prediction of aspects of protein structure and function.

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.

some-scripts

General-utility scripts that hopefully are useful for somebody

PP2_CS_WS_2015-16

Communication and documentation for the class

LocTree3

Protein Subcelullar Localization Sequenced-Based Predictor

pssh-parser

A simple JS pssh parser

RostSpace

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).

someNA

Protein DNA/RNA binding predictor

MetaStudent

Sequence-based Protein GO / Functional Predictor

MetaDisorder

Protein sequenced-based Disorder Predictor

bindadjust

smiles-cl

TMvis

Combining AlphaFold 2 structures with predicted transmembrane proteins into interactive 3D visualizations of protein structures embedded into membranes.

JS18_ProjectB_Group3

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.