Extraction of protein-protein interactions using natural language processing based pattern matching

2017 ◽  
Author(s):  
Kaixian Yu ◽  
Tingting Zhao ◽  
Peixiang Zhao ◽  
Jinfeng Zhang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals Survey of Natural Language Processing Techniques in Bioinformatics

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiqiang Zeng ◽  
Hua Shi ◽  
Yun Wu ◽  
Zhiling Hong
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Protein Interactions ◽  
Noncoding Rna ◽  
Structure And Function ◽  
Protein Protein Interactions ◽  
And Function ◽  
Processing Techniques

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Developing Chatbots for Supporting Health Self-Management

2021 ◽  
pp. 135-156
Author(s):  
Jesús Fernández-Avelino ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Laura Nely Sánchez-Morales
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing ◽  
Self Management ◽  
Customer Base ◽  
Design And Implementation ◽  
Main Components ◽  
Answering Questions

A chatbot is a software agent that mimics human conversation using artificial intelligence technologies. Chatbots help to accomplish tasks ranging from answering questions, playing music, to managing smart home devices. The adoption of this kind of agent is increasing since people are discovering the benefits of them, such as saving time and money, higher customer satisfaction, customer base growing, among others. However, developing a chatbot is a challenging task that requires addressing several issues such as pattern matching, natural language understanding, and natural language processing, as well as to design a knowledge base that encapsulates the intelligence of the system. This chapter describes the design and implementation of a text/speech chatbot for supporting health self-management. This chatbot is currently based on Spanish. The main goal of this chapter is to clearly describe the main components and phases of the chatbot development process, the methods, and tools used for this purpose, as well as to describe and discuss our findings from the practice side of things.

PENERAPAN NATURAL LANGUAGE PROCESSING BERBASIS VIRTUAL ASSISTANT PADA BAGIAN ADMINISTRASI AKADEMIK STMIK DHARMA WACANA

2021 ◽  
Vol 5 (1) ◽  
pp. 33-47
Author(s):  
Arief Adjie Wicaksono ◽  
Ridwan Yusuf ◽  
Tri Aristi Saputri
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Pattern Matching ◽  
Language Processing ◽  
Regular Expression

Sekolah Tinggi Ilmu Manajemen Informatika dan Komputer (STMIK) Dharma Wacana memiliki beberapa bagian seperti Bagian Administrasi Akademik yang memiliki tugas melaksanakan pelayanan dibidang akademik. Bagian Administrasi Akademik menjadi sumber informasi terkait kegiatan perkuliahan. Kebutuhan informasi perkuliahan belum efektif dikarenakan terbatasnya jam kerja dari pegawai dan masih banyak pertanyaan berulang yang berdatangan ke Bagian Administrasi Akademik, seperti pertanyaan yang telah ditanyakan oleh seorang mahasiswa kemudian ditanyakan lagi oleh mahasiswa lainnya. Tujuan dari penelitian ini adalah melakukan observasi dan wawancara terhadap mahasiswa dan pegawai Bagian Administrasi Akademik serta menganalisis kelemahannya sehingga dapat menjadi acuan untuk merancang aplikasi dengan penerapan Natural Language Processing (NLP). Pada penelitian telah dibangun Virtual Assistant berupa Chatbot yang tersedia pada platform messenger yaitu LINE, Facebook dan Telegram yang hanya bertindak layaknya bagian informasi perkuliahan. NLP dengan pendekatan pattern matching menggunakan regular expression diterapkan dalam proses mengenali pertanyaan mahasiswa sehingga Virtual Assistant dapat memberikan jawaban yang sesuai.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence
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