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bindadjust

bindpredict

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

bio_embeddings

Repository containing bio_embeddings resources

biojs-vis-uniprotproteinalignment

Uniprot website's new alignment visualization.

biojs.github.io

blog

consurf

Evolutionary conservation estimation of residues or nucleotides

dm_cs_ws_2016-17

Repo for general info of the course and communication

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.

funfamsclustering

funfamsconsensus

Provides functionality to compute the binding residue similarity of sequences in the FunFam dataset.

gopredsim

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.

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.

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.

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).

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).

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).

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.

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.

js18_projecta_group1

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.

js18_projectb_group3

js18_projectb_group4

lambdapp

locnuclei

Prediction of subnuclear locations

loctext

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

loctree3

Protein Subcelullar Localization Sequenced-Based Predictor

metadisorder

Protein sequenced-based Disorder Predictor

metastudent

Sequence-based Protein GO / Functional Predictor