Josh (hsingjun)

hsingjun

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Location:San Jose

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Josh's repositories

DeepSeqPan

A sequence-based pan model for peptide-MHC I binding affinity prediction.

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email-sentiment-classification

Natural Language Processing - Text Classification in Python

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ActionTubes

source code for Finding Action Tubes, CVPR 2015

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avspeech-downloader

AVSpeech downloader

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convmhc

ConvMHC - a web server for peptide-MHC class I binding predictions using deep convolutional neural networks

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decoder

decode your genome

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DeepMind-alphafold-repl

https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/

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Email_sentiment

Measuring sentiments in a mail

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EnronEmails

Enron Email Sentiment Analysis Project

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ForestMHC

Predictor of MHC-I binding

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ForestMHC-data

Amalgamated peptides of length nine from publicly available sources, after deconvolution of polyallelic data and filtering

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Kaggle_TGS2018_4th_solution

Kaggle TGS Salt Identification Challenge 2018 4th place code

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keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

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mhctools

Python interface to running command-line and web-based MHC binding predictors

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MolGAN

Tensorflow implementation of MolGAN: An implicit generative model for small molecular graphs (https://arxiv.org/abs/1805.11973)

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nlp_naivebayes_textclassifier

Language: Python Created a naive Bayes text classifier (nblearn.py, nbclassify.py) and compared accuracy with two popular machine learning toolkits. Worked with two datasets (emails and IMDB reviews) and performed binary classification: SPAM or HAM (not spam), and POSITIVE or NEGATIVE (sentiment analysis). Compared three machine learning techniques for making these classifications: naive Bayes classification, maximum entropy modeling, and support vector machines (MegaM and SVM-Light). report.txt - contains detailed instructions about the sequence of scripts to run and what cmd arguments to pass each python script.

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scnn

Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

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sentimail

Visualization tool for sentiments in email

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Sentiment-And-Email-Analysis

Naive Bayes, MEGAM and SVM Machine Learning ALgorithms were used.

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Twitter_Map

Web Application that allows users to search keywords from an AWS Elasticsearch Service powered by AWS Elastic Beanstalk. The Twitter Streaming API is used to send tweets to AWS SQS which is then handled by a worker that judges a tweet's sentiment or whether tweets are positive, negative, or neutral - this leverages IBM's Natural Language Understanding API. Once a sentiment is obtained, tweets are pushed to AWS SNS service which then are pushed to services that are interested in this topic. User may ask an HTTP/HTTPS, email, or SMS endpoint to subscribe to this AWS SNS topic. In this case, AWS Elasticsearch is the endpoint that is subscribed to the SNS topic. A web interface is provided to view tweets or filter them. Viewers may read the tweet and its corresponding sentiment by hovering over them on Google Map.

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vaxrank

Ranked vaccine peptides for personalized cancer immunotherapy

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Weibo-Analyst

This is a Weibo comments processing toolbox; 这是一个微博评论分析工具,实现功能主要有:1.微博评论数据爬取;2.分词与关键词提取;3.词云与词频统计;4.情感分析;5.主题聚类

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YOLO_tensorflow

tensorflow implementation of 'YOLO : Real-Time Object Detection'

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