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

Zulfiqar, Hasan; Ahmed, Zahoor; Grace-Mercure, Bakanina Kissanga; Hassan, Farwa; Zhang, Zhao-Yue; Liu, Fen

doi:10.3389/fmicb.2023.1170785

Cited by 6 publications

(1 citation statement)

References 67 publications

(67 reference statements)

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“…In this study, we have used a feature selection approach in order to reduce the dimensions of the data matrix and to obtain an important set of genes that can accurately stratify the AD and NC samples. Here, we have used the information-theory-based feature-selection (IFA) method of minimum Redundancy and Maximum Relevance (mRMR), which has been previously implemented in similar types of studies analysis [17][18][19]. mRMR algorithm selects those genes/features which have a high correlation with the class (i.e., output) and a low correlation between themselves.…”

Section: Feature Selection Algorithmmentioning

confidence: 99%

Prediction of Alzheimer’s Disease from Single Cell Transcriptomics Using Deep Learning

Srivastava,

Dhall,

Patiyal

et al. 2023

Preprint

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Alzheimer's disease (AD) is a progressive neurological disorder characterized by brain cell death, brain atrophy, and cognitive decline. Early diagnosis of AD remains a significant challenge in effectively managing this debilitating disease. In this study, we aimed to harness the potential of single-cell transcriptomics data from 12 Alzheimer's patients and 9 normal controls (NC) to develop a predictive model for identifying AD patients. The dataset comprised gene expression profiles of 33,538 genes across 169,469 cells, with 90,713 cells belonging to AD patients and 78,783 cells belonging to NC individuals. Employing machine learning and deep learning techniques, we developed prediction models. Initially, we performed data processing to identify genes expressed in most cells. These genes were then ranked based on their ability to classify AD and NC groups. Subsequently, two sets of genes, consisting of 35 and 100 genes, respectively, were used to develop machine learning-based models. Although these models demonstrated high performance on the training dataset, their performance on the validation/independent dataset was notably poor, indicating potential overoptimization. To address this challenge, we developed a deep learning method utilizing dropout regularization technique. Our deep learning approach achieved an AUC of 0.75 and 0.84 on the validation dataset using the sets of 35 and 100 genes, respectively. Furthermore, we conducted gene ontology enrichment analysis on the selected genes to elucidate their biological roles and gain insights into the underlying mechanisms of Alzheimer's disease. While this study presents a prototype method for predicting AD using single-cell genomics data, it is important to note that the limited size of the dataset represents a major limitation. To facilitate the scientific community, we have created a website to provide with code and service. It is freely available at https://webs.iiitd.edu.in/raghava/alzscpred.

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Section: Feature Selection Algorithmmentioning

confidence: 99%

Prediction of Alzheimer’s Disease from Single Cell Transcriptomics Using Deep Learning

Srivastava,

Dhall,

Patiyal

et al. 2023

Preprint

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

Cited by 6 publications

References 67 publications

Prediction of Alzheimer’s Disease from Single Cell Transcriptomics Using Deep Learning

Prediction of Alzheimer’s Disease from Single Cell Transcriptomics Using Deep Learning

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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