scholarly journals ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network

Molecules ◽  
2017 ◽  
Vol 22 (10) ◽  
pp. 1732 ◽  
Author(s):  
Renzhi Cao ◽  
Colton Freitas ◽  
Leong Chan ◽  
Miao Sun ◽  
Haiqing Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Neural Machine Translation

Neural Machine Translation Using Sequence Modeling

2021 ◽  
Vol 9 (8) ◽  
pp. 2153-2169
Author(s):  
N Revathi
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Term Memory ◽  
Sequence Modeling

Abstract: Language is a main mode of communication, and translation is a critical tool for understanding information in a foreign language. Without the help of human translators, machine translation allows users to absorb unfamiliar linguistic material. The main goal of this project is to create a practical language translation from English to Hindi. Given its relevance and potential in the English-Hindi translation, machine translation is an efficient way to turn content into a new language without employing people. Among all available translation machines, Neural Machine Translation (NMT) is one of the most efficient ways. So, in this case, we're employing Sequence to Sequence Modeling, which includes the Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM), and Encoder-Decoder methods. Deep Neural Network (DNN) comprehension and principles of deep learning, i.e. machine translation, are disclosed in the field of Natural Language Processing (NLP). In machine reclining techniques, DNN plays a crucial role. Keywords: Sequence to Sequence, Encoder-Decoder, Recurrent Neural Network, Long & Short term Memory, Deep Neural Network.

scholarly journals Neural Machine Translation using Recurrent Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 1395-1400
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Neural Machine Translation ◽  
To Come ◽  
Artificial Neural Network Ann ◽  
Translation Systems

In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.

scholarly journals Analisis Perbandingan Nilai Akurasi Mekanisme Attention Bahdanau dan Luong pada Neural Machine Translation Bahasa Indonesia ke Bahasa Melayu Ketapang dengan Arsitektur Recurrent Neural Network

2021 ◽  
Vol 7 (3) ◽  
pp. 488
Author(s):  
Wahyu Gunawan ◽  
Herry Sujaini ◽  
Tursina Tursina
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Cross Validation ◽  
Neural Machine Translation ◽  
Fold Cross Validation ◽  
Bahasa Indonesia

Di Indonesia, penerapan mesin penerjemah masih banyak dilakukan dengan berbasis statistik khususnya dalam eksperimen penerjemahan bahasa daerah. Dalam beberapa tahun terakhir, mesin penerjemah jaringan saraf tiruan telah mencapai kesuksesan yang luar biasa dan menjadi metode pilihan baru dalam praktik mesin penerjemah. pada penelitian ini menggunakan mekanisme attention dari Bahdanau dan Luong dalam bahasa Indonesia ke bahasa Melayu Ketapang dengan data korpus paralel sejumlah 5000 baris kalimat. Hasil pengujian berdasarkan metode penambahan secara konsisten dengan jumlah epoch didapatkan nilai skor BLEU yaitu pada attention Bahdanau menghasilkan akurasi 35,96% tanpa out-of-vocabulary (OOV) dengan menggunakan jumlah epoch 40, sedangkan pada attention Luong menghasilkan akurasi 26,19% tanpa OOV menggunakan jumlah 30 epoch. Hasil pengujian berdasarkan k-fold cross validation didapatkan nilai rata-rata akurasi tertinggi sebesar 40,25% tanpa OOV untuk attention Bahdanau dan 30,38% tanpa OOV untuk attention Luong, sedangkan pengujian manual oleh dua orang ahli bahasa memperoleh nilai akurasi sebesar 78,17% dan 72,53%. 

scholarly journals Convolutional over Recurrent Encoder for Neural Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Praveen Dakwale ◽  
Christof Monz
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Machine Translation ◽  
Recurrent Neural Network ◽  
Neural Machine Translation ◽  
German Translation ◽  
Source Sentence ◽  
End To End ◽  
Target Words

AbstractNeural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN). In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

scholarly journals Prediction of protein function using a deep convolutional neural network ensemble

2017 ◽  
Vol 3 ◽  
pp. e124 ◽  
Author(s):  
Evangelia I. Zacharaki
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Deep Convolutional Neural Network ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Homologous Proteins

Background The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through support vector machines or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results Cross validation experiments on single-functional enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification, demonstrating an improvement over previous results on the same dataset when sequence similarity was not considered. Discussion The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification. The proposed method shows promise for structure-based protein function prediction, but sufficient data may not yet be available to properly assess the method’s performance on non-homologous proteins and thus reduce the confounding factor of evolutionary relationships.

Sign in / Sign up

Export Citation Format

Share Document