Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana

Related tags

Deep Learningdeep_gene_annotator
Overview

DeepGeneAnnotator: A tool to annotate the gene in the genome

The master thesis of the "Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana" is shown in here.

Reference

If you want to cite the DeepGeneAnnotator , please consider citing as the following:

Wang, Ching-Tien. (2020). Using deep learning to predict gene structures of the coding genes in DNA sequences of Arabidopsis thaliana. 
(Master thesis), National Taiwan University, Retrieved from https://dx.doi.org/10.6342/NTU202002143
You might also like...
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 3, 2023
Predict the latency time of the deep learning models

Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num

QAQ 1 Nov 12, 2021
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

Rishikesh S 15 Aug 20, 2022
A library built upon PyTorch for building embeddings on discrete event sequences using self-supervision

pytorch-lifestream a library built upon PyTorch for building embeddings on discrete event sequences using self-supervision. It can process terabyte-si

Dmitri Babaev 103 Dec 17, 2022
Official PyTorch implementation of
Official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition" in AAAI2022.

AimCLR This is an official PyTorch implementation of "Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Reco

Gty 44 Dec 17, 2022
Implementation of the GVP-Transformer, which was used in the paper
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure

Phil Wang 19 May 6, 2022
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
A foreign language learning aid using a neural network to predict probability of translating foreign words
A foreign language learning aid using a neural network to predict probability of translating foreign words

Langy Langy is a reading-focused foreign language learning aid orientated towards young children. Reading is an activity that every child knows. It is

Shona Lowden 6 Nov 17, 2021
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain

Unity Technologies 187 Dec 24, 2022
Owner
Ching-Tien Wang
A researcher study in bioinformatics and deep learning. To see other repositories: https://bitbucket.org/grassking100/?sort=-updated_on&privacy=public.
Ching-Tien Wang
GitHub Repository https://dx.doi.org/10.6342/NTU202002143
Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

NeRF++ Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields" Work with 360 capture of large-scale unbounded scenes. Sup

Kai Zhang 722 Dec 28, 2022
Code for Max-Margin Contrastive Learning - AAAI 2022

Max-Margin Contrastive Learning This is a pytorch implementation for the paper Max-Margin Contrastive Learning accepted to AAAI 2022. This repository

Anshul Shah 12 Oct 22, 2022
Rainbow: Combining Improvements in Deep Reinforcement Learning

Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning [1]. Results and pretrained models can be found in the releases. DQN [2] Double

Kai Arulkumaran 1.4k Dec 29, 2022
Semi-supervised semantic segmentation needs strong, varied perturbations

Semi-supervised semantic segmentation using CutMix and Colour Augmentation Implementations of our papers: Semi-supervised semantic segmentation needs

146 Dec 20, 2022
Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL

A method to solve the Higgs boson challenge using Least Squares - Novae This project is the Project 1 of EPFL CS-433 Machine Learning. The project is

Giacomo Orsi 1 Nov 09, 2021
Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

DD3D: "Is Pseudo-Lidar needed for Monocular 3D Object detection?" Install // Datasets // Experiments // Models // License // Reference Full video Offi

Toyota Research Institute - Machine Learning 364 Dec 27, 2022
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"

DAGAN This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruct

TensorLayer Community 159 Nov 22, 2022
OoD Minimum Anomaly Score GAN - Code for the Paper 'OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary'

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C

- 8 Sep 27, 2022
Efficient Deep Learning Systems course

Efficient Deep Learning Systems This repository contains materials for the Efficient Deep Learning Systems course taught at the Faculty of Computer Sc

Max Ryabinin 173 Dec 29, 2022
PyTorch implementation of spectral graph ConvNets, NIPS’16

Graph ConvNets in PyTorch October 15, 2017 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbresson

Xavier Bresson 287 Jan 04, 2023
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022
A PyTorch implementation of QANet.

QANet-pytorch NOTICE I'm very busy these months. I'll return to this repo in about 10 days. Introduction An implementation of QANet with PyTorch. Any

H. Z. 343 Nov 03, 2022
A PyTorch implementation of the architecture of Mask RCNN

EDIT (AS OF 4th NOVEMBER 2019): This implementation has multiple errors and as of the date 4th, November 2019 is insufficient to be utilized as a reso

Sai Himal Allu 975 Dec 30, 2022
내가 보려고 정리한 <프로그래밍 기초 Ⅰ> / organized for me

Programming-Basics 프로그래밍 기초 Ⅰ 아카이브 Do it! 점프 투 파이썬 주차 강의주제 비고 1주차 Syllabus 2주차 자료형 - 숫자형 3주차 자료형 - 문자열형 4주차 입력과 출력 5주차 제어문 - 조건문 if 6주차 제어문 - 반복문 whil

KIMMINSEO 1 Mar 07, 2022
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

1 Nov 03, 2021
A big endian Gentoo port developed on a Pine64.org RockPro64

Gentoo-aarch64_be A big endian Gentoo port developed on a Pine64.org RockPro64 The endian wars are over... little endian won. As a result, it is incre

Rory Bolt 6 Dec 07, 2022
Repository of our paper 'Refer-it-in-RGBD' in CVPR 2021

Refer-it-in-RGBD This is the repository of our paper 'Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images' in CVPR 2021 Pape

Haolin Liu 34 Nov 07, 2022
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Jan 04, 2023
Learning cell communication from spatial graphs of cells

ncem Features Repository for the manuscript Fischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial graphs of cells. 2021

Theis Lab 77 Dec 30, 2022