Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods

Zulfiqar, Hasan; Grace-Mercure, Bakanina Kissanga; Zhang, Zhao-Yue; Gao, Hui; Lin, Hao; Wu, Yun

doi:10.1016/j.csbj.2023.03.024

Cited by 9 publications

(1 citation statement)

References 95 publications

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“…The main idea of AB is to set the classifiers' weights and trained the data in each and every iteration. The support vector machine (SVM) is also very famous and has been used in many bioinformatics and computational biology-related tools (Tao et al, 2020 ; Ahmed et al, 2022 ; Manavalan and Patra, 2022 ; Zou et al, 2022 ; Bupi et al, 2023 ; Zulfiqar et al, 2023 ). It is mostly used to perform binary classification.…”

Section: Methodsmentioning

confidence: 99%

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

et al. 2023

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Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

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Section: Methodsmentioning

confidence: 99%

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

et al. 2023

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Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies

Akhtar,

Belal Bin Heyat,

Sultana

et al. 2024

WIREs Data Min & Knowl

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This comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential of medical intelligence [medical + artificial intelligence (AI)]. By addressing fundamental research questions, this study investigated the molecular and hormonal foundations underlying anxiety disorders, shedding light on the intricate interplay of genetic and hormonal factors contributing to the etiology and progression of anxiety. Furthermore, this review delves into the emerging implications of biomaterials, defibrillators, and state‐of‐the‐art devices for anxiety research, elucidating their potential roles in diagnosis, treatment, and patient management. A pivotal contribution of this review is the development and exploration of an AI‐driven model for real‐time cardiac signal analysis. This innovative approach offers a promising avenue for enhancing the precision and timeliness of anxiety diagnosis and monitoring. Leveraging machine learning and AI techniques enables the accurate classification of persons with anxiety based on real‐time cardiac data, thereby ushering in a new era of personalized and data‐driven mental health care. Identifying emerging themes and knowledge gaps lays the foundation for future research directions and offers a roadmap for scholars and practitioners to navigate this intricate field. In conclusion, this comprehensive review serves as a vital resource, consolidating diverse perspectives and fostering a deeper understanding of anxiety disorders from biological, engineering, and technological standpoints, ultimately contributing to advancing mental health research and clinical practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Classification

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Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies

Zulfiqar,

Ahmad,

Raza

et al. 2024

Methods in Molecular Biology

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Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods

Cited by 9 publications

References 95 publications

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies

Promoter Prediction in Agrobacterium tumefaciens Strain C58 by Using Artificial Intelligence Strategies

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