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Artificial Intelligent and Machine Learning Methods in Bioinformatics and Medical Informatics

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Emerging Technologies in Biomedical Engineering and Sustainable TeleMedicine

Part of the book series: Advances in Science, Technology & Innovation ((ASTI))

Abstract

Recently, the machine learning techniques have been widely adopted in the field of bioinformatics and medical informatics. Generally, the main purpose of machine learning is to develop algorithms that can learn and improve over time and can be utilized for predictions in hindcast and forecast applications. Computational intelligence has been significantly employed to develop optimization and prediction solutions for several bioinformatics and medical informatics techniques in which it utilized various computational methodologies to address complex real-world problems and promises to enable computers to help humans in analyzing large complex data sets. Its approaches have been widely applied in biomedical fields, and there are many applications that use the machine learning, such as genomics, proteomics, systems biology, evolution and text mining, which are also discussed. In this chapter, we provide a comprehensive study of the use of artificial intelligent and machine learning methods in bioinformatics and medical informatics, including AI and its learning processes, machine learning and its applications for health informatics, text mining methods, and many other related topics.

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Abbreviations

AI:

Artificial intelligence.

IT:

Information technology.

ML:

Machine learning.

NN:

Neural networks.

EHR:

Electronic health records.

ANI:

Artificial narrow intelligence.

AGI:

Artificial general intelligence.

NLP:

Natural language processing.

SR:

Speech recognition.

ES:

Expert systems.

AI-R:

Artificial intelligence for robotics.

PML:

Probabilistic machine learning.

MLD:

Machine learning data.

aML:

automated Machine Learning.

iML:

interactive Machine learning.

HCI:

Human–computer interaction.

KDD:

Knowledge discovery/data.

RNA:

Ribonucleic acid.

DNA:

Microarray deoxyribonucleic acid.

cDNA:

complementary Microarray deoxyribonucleic acid.

mRNA:

messenger ribonucleic acid.

MIAME:

Minimum information about a microarray experiment.

FGED:

Functional Genomics Data Society.

FRG/BKG:

Foreground/background.

MS:

Mass spectrometry.

SVM:

Support vector machine.

RBF:

Radial basis function.

CART:

Classification and regression tree.

OOB:

Out of the bag.

TM:

Text mining.

IR:

Information retrieval.

DC:

Document classification.

NER/NEN:

Named entity recognition/normalization.

ART:

Adaptive resonance theory.

DNN:

Deep neural network.

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Author information

Authors and Affiliations

  1. Department of Computer Sciences, King Faisal University, Hufof, Al-Ahsa, Saudi Arabia

    Noor A. Jebril,

  2. Department of Data Science & Artificial Intelligence, University of Petra (UoP),, Amman, Jordan

    Qasem Abu Al-Haija,

Authors
  1. Noor A. Jebril,
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  2. Qasem Abu Al-Haija,
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Corresponding author

Correspondence to Qasem Abu Al-Haija, .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Jihad Alja’am

  2. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Somaya Al-Maadeed

  3. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Osama Halabi

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Jebril,, N.A., Abu Al-Haija,, Q. (2021). Artificial Intelligent and Machine Learning Methods in Bioinformatics and Medical Informatics. In: Alja’am, J., Al-Maadeed, S., Halabi, O. (eds) Emerging Technologies in Biomedical Engineering and Sustainable TeleMedicine. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-14647-4_2

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