Cited by Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods

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

Faijan Akhtar, Md Belal Bin Heyat, Arshiya Sultana

et al.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 4, 2024

Abstract 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 medical intelligence [medical + artificial (AI)]. By addressing fundamental research questions, this study investigated foundations underlying disorders, shedding light intricate interplay genetic factors contributing to etiology progression anxiety. Furthermore, delves into emerging implications biomaterials, defibrillators, state‐of‐the‐art devices for elucidating their roles in diagnosis, treatment, patient management. A pivotal contribution is development AI‐driven model analysis. innovative approach offers promising avenue enhancing precision timeliness diagnosis monitoring. Leveraging machine learning AI techniques enables accurate classification persons with based data, thereby ushering new era personalized data‐driven mental health care. Identifying themes knowledge gaps lays foundation future directions roadmap scholars practitioners navigate field. In conclusion, serves vital resource, consolidating diverse perspectives fostering deeper understanding disorders from biological, technological standpoints, ultimately advancing clinical practice. categorized under: Application Areas > Health Care Science Technology Technologies Classification

Language: Английский

Citations

IIFS: An improved incremental feature selection method for protein sequence processing DOI

Chaolu Meng,

Ye Yuan, Haiyan Zhao

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 167, P. 107654 - 107654

Published: Nov. 3, 2023

Language: Английский

Citations

TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach DOI

Matee Ullah, Shahid Akbar, Ali Raza

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions clathrin protein functions have been associated with wide range diseases, such as Alzheimer’s, neurodegeneration, viral infection, cancer. Therefore, correctly identifying is critical to unravel mechanism these fatal diseases designing drug targets. This paper presents novel computational method, named TargetCLP, precisely identify proteins. TargetCLP leverages four single-view feature representation methods, two transformed sets (PSSM-CLBP RECM-CLBP), one qualitative characteristics feature, deep-learned-based embedding using ESM. The features are integrated based on their weights differential evolution, BTG selection algorithm utilized generate more optimal reduced subset. model trained classifiers, among which proposed SnBiLSTM achieved remarkable performance. Experimental comparative results both training independent datasets show that offers significant improvements terms prediction accuracy generalization unseen data, furthering advancements research field.

Language: Английский

Citations

Computational identification of promoters in Klebsiella aerogenes by using support vector machine DOI

Yan Lin, Meili Sun, Junjie Zhang

et al.

Frontiers in Microbiology, Journal Year: 2023, Volume and Issue: 14

Published: May 5, 2023

Promoters are the basic functional cis-elements to which RNA polymerase binds initiate process of gene transcription. Comprehensive understanding expression and regulation depends on precise identification promoters, as they most important component expression. This study aimed develop a machine learning-based model predict promoters in Klebsiella aerogenes (K. aerogenes). In prediction model, promoter sequences K. genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) position-correlation scoring function (PCSF). Numerical features obtained then optimized using mRMR combining with support vector (SVM) 5-fold cross-validation (CV). Subsequently, these inputted into SVM-based classifier discriminate from non-promoter aerogenes. Results 10-fold CV showed that could yield overall accuracy 96.0% area under ROC curve (AUC) 0.990. We hope this will provide help for

Language: Английский

Citations

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

Aman Srivastava, Anjali Dhall, Sumeet Patiyal

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: July 10, 2023

Abstract Alzheimer’s disease (AD) is a progressive neurological disorder characterized by brain cell death, atrophy, and cognitive decline. Early diagnosis of AD remains significant challenge in effectively managing this debilitating disease. In study, we aimed to harness the potential single-cell transcriptomics data from 12 patients 9 normal controls (NC) develop predictive model for identifying patients. The dataset comprised gene expression profiles 33,538 genes across 169,469 cells, with 90,713 cells belonging 78,783 NC individuals. Employing machine learning deep techniques, developed prediction models. Initially, performed processing identify expressed most cells. These were then ranked based on their ability classify groups. Subsequently, two sets genes, consisting 35 100 respectively, used learning-based Although these models demonstrated high performance training dataset, validation/independent was notably poor, indicating overoptimization. To address challenge, method utilizing dropout regularization technique. Our approach achieved an AUC 0.75 0.84 validation using respectively. Furthermore, conducted ontology enrichment analysis selected elucidate biological roles gain insights into underlying mechanisms While study presents prototype predicting genomics data, it important note that limited size represents major limitation. facilitate scientific community, have created website provide code service. It freely available at https://webs.iiitd.edu.in/raghava/alzscpred . Key Points Predictive Model Disease Using Single Cell Transcriptomics Data Overoptimization trained data. Application technique ANN reducing overoptimization Ranking predict patients’ Standalone software package Author’s Biography Aman Srivastava pursuing M. Tech. Computational Biology Department Biology, Indraprastha Institute Information Technology, New Delhi, India. Anjali Dhall currently working as Ph.D. Sumeet Patiyal Akanksha Arora Jarwal Gajendra P. S. Raghava Professor Head

Language: Английский

Citations

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

Hasan Zulfiqar,

Ramala Masood Ahmad,

Ali Raza

et al.

Methods in molecular biology, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 44

Published: Jan. 1, 2024

Language: Английский

Citations

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods DOI

Chengyan Wu,

Zhi‐Xue Xu, Nan Li

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108207 - 108207

Published: Sept. 11, 2024

Language: Английский

Citations