Use of semantics in bio-informatics

2017 ◽  
Vol 01 (01) ◽  
pp. 1630017
Author(s):  
Charles C. N. Wang ◽  
Jeffrey J. P. Tsai
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Bioinformatics Analysis ◽  
Learning Technologies ◽  
Biological Data ◽  
Mining Machine ◽  
Biological Processes ◽  
Primary Methods ◽  
Modeling Data

Bioinformatics conceptualizes biological processes in terms of genomics and applies computer science (derived from disciplines such as applied modeling, data mining, machine learning and statistics) to extract knowledge from biological data. This paper introduces the working definitions of bioinformatics and its applications and challenges. We also identify the bioinformatics resources that are popular among bioinformatics analysis, review some primary methods used to analyze bioinformatics problems, and review the data mining, semantic computing and deep learning technologies that may be applied in bioinformatics analysis.

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

scholarly journals Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 121
Author(s):  
Marco Sánchez-Aguayo ◽  
Luis Urquiza-Aguiar ◽  
José Estrada-Jiménez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Learning Strategies ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
General Interest ◽  
Fraud Triangle ◽  
Learning Techniques

Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.

scholarly journals Regression plane concept: analysing continuous cellular processes with machine learning

2020 ◽  
Author(s):  
Abel Szkalisity ◽  
Filippo Piccinini ◽  
Attila Beleon ◽  
Tamas Balassa ◽  
Istvan Gergely Varga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Biological Processes ◽  
Cellular Processes ◽  
Novel Concept ◽  
User Friendly

ABSTRACTBiological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implemented Regression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT

2018 ◽  
Vol 39 (8) ◽  
pp. 1871-1877 ◽  
Author(s):  
Tomoaki Sonobe ◽  
Hitoshi Tabuchi ◽  
Hideharu Ohsugi ◽  
Hiroki Masumoto ◽  
Naohumi Ishitobi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Epiretinal Membrane ◽  
Learning Technologies ◽  
Support Vector ◽  
Learning Machine

scholarly journals LitCovid: an open database of COVID-19 literature

2020 ◽  
Vol 49 (D1) ◽  
pp. D1534-D1540 ◽  
Author(s):  
Qingyu Chen ◽  
Alexis Allot ◽  
Zhiyong Lu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Information Needs ◽  
Healthcare Professionals ◽  
Information Overload ◽  
Evidence Synthesis ◽  
Scientific Information ◽  
Manual Curation ◽  
Text And Data Mining

Abstract Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

Data mining/machine learning methods in foodomics

2021 ◽  
Vol 37 ◽  
pp. 76-82
Author(s):  
Ana M Jimenez-Carvelo ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mining Machine ◽  
Learning Methods ◽  
Machine Learning Methods
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