Role of Data Mining Techniques in Bioinformatics

Singh, Pushpa; Singh, Narendra

doi:10.4018/ijarb.2021010106

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Classification is a process that is an inherent aspect of daily life and it is perceived to be the decision-making function that is most frequently undertaken by human beings (Singh and Singh 2020 ). Essentially, when we allocate an object to a predetermined class or category, we are classifying that object according to several different predetermined characteristics that may have some relation to the allocated object (Khanbabaei et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-022-06917-z.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Alweshah

2022

Soft Comput

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“…Predicting the activity of biosimilar candidates against specific targets for lead compound selection computer tools to glean insightful information from large biological datasets [91]. These methods are crucial for identifying pertinent biomarkers [92], therapeutic targets [92], and biosimilar candidate optimization [93].…”

Section: Qsar Modelingmentioning

confidence: 99%

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents

Askari,

Ghofrani,

Taherdoost

2023

BioMedInformatics

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In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects.

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“…Ribonucleic acid (RNA) is a chain of nitrogen bases, compared to DNA, RNA nitrogen bases include rings of ribose rather than deoxyribose and uracil (U) rather than thymine (T). RNA is transcribed from DNA by RNA polymerase (enzyme) and then processed by other proteins [2].…”

Section: Dna Rna and Protein Compositionsmentioning

confidence: 99%

“…Sequence motifs, often known as regulatory elements, may be found in eukaryotic genes' regulatory regions (RR). These patterns are crucial for identifying transcription factor binding sites (TF-BSs), which aids in understanding how genes are regulated [2]. While intergenic areas are very extensive and very varied, sequence motifs are frequently repeated and preserved, making it difficult to identify them.…”

Section: Introductionmentioning

confidence: 99%

A survey of exact motif finding algorithms

Yousif

Khafaji

Abbas

2022

IJEECS

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<span lang="EN-US"><span lang="EN-US">Deoxyribonucleic </span>acid (DNA) motif finding (discovery/mining) in biological chains is the most recent challenging and interesting trend in bioinformatics. It represents a crucial phase in most bioinformatics systems related to unravelling the secrets of gene functions. Despite the efforts made to date to produce robust algorithms, DNA motif finding remains a difficult task for researchers in this field. In general, biological pattern locating algorithms are categorized into two categories: probabilistic and numerical methods. In this paper, we provide a survey of exact DNA motif finding algorithms and their working principles with a suitable comparison among these algorithms to provide an essential step for researchers in this field.</span>

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Role of Data Mining Techniques in Bioinformatics

Cited by 8 publications

References 14 publications

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents

A survey of exact motif finding algorithms

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