Model Identification Research Articles

Coupled continuous time random walks for dispersion in spatio-temporal random flows

Dispersion in spatio-temporal random flows is dominated by the competition between spatial and temporal velocity resets along particle paths. This competition admits a range of normal and anomalous dispersion behaviours characterised by the Kubo number, which compares the relative strength of spatial and temporal velocity resets. To shed light on these behaviours, we develop a Lagrangian stochastic approach for particle motion in spatio-temporally fluctuating flow fields. For space–time separable flows, particle motion is mapped onto a continuous time random walk (CTRW) for steady flow in warped time, which enables the upscaling and prediction of the large-scale dispersion behaviour. For non-separable flows, we measure Lagrangian velocities in terms of a new sampling variable, the average number of velocity transitions (both temporal and spatial) along pathlines, which renders the velocity series Markovian. Based on this, we derive a Lagrangian stochastic model that represents particle motion as a coupled space–time random walk, that is, a CTRW for which the space and time increments are intrinsically coupled. This approach sheds light on the fundamental mechanisms of particle motion in space–time variable flows, and allows for its systematic quantification. Furthermore, these results indicate that alternative strategies for the analysis of Lagrangian velocity data using new sampling variables may facilitate the identification of (hidden) Markov models, and enable the development of reduced-order models for otherwise complex particle dynamics.

Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe Acute Kidney Injury.

Prior models for the early identification of acute kidney injury (AKI) have utilized structured data (e.g., vital signs and laboratory values). We aimed to develop and validate a deep learning model to predict moderate to severe AKI by combining structured data and information from unstructured notes. Adults (≥18 years) admitted to the University of Wisconsin (2009-20) and the University of Chicago Medicine (2016-22) were eligible for inclusion. Patients were excluded if they had no documented serum creatinine (SCr), end-stage kidney disease, an admission SCr≥3.0mg/dL, developed ≥Stage 2 AKI before reaching the wards or intensive care unit (ICU), or required dialysis (KRT) within the first 48 hours. Text from unstructured notes was mapped to standardized Concept Unique Identifiers (CUIs) to create predictor variables, and structured data variables were also included. An intermediate fusion deep learning recurrent neural network architecture was used to predict ≥Stage 2 AKI within the next 48 hours. This multimodal model was developed in the first 80% of the data and temporally validated in the next 20%. There were 339,998 admissions in the derivation cohort and 84,581 in the validation cohort, with 12,748 (3%) developing ≥Stage 2 AKI. Patients with ≥Stage 2 AKI were older, more likely to be male, had higher baseline SCr, and were more commonly in the ICU (p<0.001 for all). The multimodal model outperformed a model based only on structured data for all outcomes, with an area under the receiver operating characteristic curve (95% CI) of 0.88(0.88-0.88) for predicting ≥Stage 2 AKI and 0.93(0.93-0.94) for receiving KRT. The area under the precision-recall-curve for ≥Stage 2 AKI was 0.20. Results were similar during external validation. We developed and validated a multimodal deep learning model using structured and unstructured data that predicts the development of severe AKI across the hospital stay for earlier intervention.

LM-TLIPs: Integrating Large Model and Transfer Learning Technology for Precise Identification of Phosphorylation Sites in SARS-CoV-2.

In recent years, the rapid spread of SARS-CoV-2 has triggered a global health crisis and socio-economic challenges. As a crucial post-translational modification, phosphorylation plays a vital role in the regulation of cellular functions. Given its close relationship with SARS-CoV-2 infection, accurately identifying virus-induce phosphorylation sites is essential for understanding the molecular mechanisms of viral infection and its impact on host cells. Although the development of various computational tools for predicting phosphorylation sites, these tools have several shortcomings, such as insufficient data and limited model generalization ability, which limit their effectiveness in practical applications. To overcome these limitations, this study proposes a novel method for predicting SARS-CoV-2 phosphorylation sites, LM-TLIPs, based on the latest technology. This method uses the most advanced large model technology ESM-2 to extract information from S/T sites and Y sites; by fine-tuning the large model and introducing transfer learning technology, it addresses the challenge of accurately predicting Y sites due to insufficient data in this study. Independent testing on S/T sites(Acc:0.8309, Sn:0.8443, Sp:0.8174, MCC:0.6620, AUC:0.8993) and Y sites(Acc:0.9048, Sn:0.9524, Sp:0.8571, MCC:0.8132, AUC:0.9388) has validated that LM-TLIPs outperforms existing optimal prediction tools, demonstrating its superior ability in identifying phosphorylation sites. Furthermore, we conducted an exhaustive interpretability analysis based on attention weight heatmaps and feature importance ranking to enhance the transparency and confidence of prediction results. Additionally, to facilitate academic research and practical application, we not only provided the source code and dataset for LM-TLIPs: https://github.com/wmqskr/LM-TLIPs.git, but also set up a free online Web server for the widely used: www.lmtlips.com.

Breath biomarkers for esophageal cancer: identification, quantification, and diagnostic modeling.

Esophageal cancer is a major global health issue with a high mortality rate. Early diagnosis is crucial for improving patient outcomes, but traditional diagnostic methods are often invasive and costly. This study explores the potential of exhaled volatile organic compounds (VOCs) as a non-invasive diagnostic tool for esophageal cancer. Using gas chromatography-mass spectrometry (GC-MS), we analyzed the breath samples of 80 esophageal cancer patients and 60 healthy controls, identifying and quantifying over 100 VOCs. The results revealed significant differences in the concentrations of VOCs such as acetone, ethanol, and isoprene between the two groups. A multi-parameter regression diagnostic model based on a neural network algorithm achieved an accuracy of 90.3% in distinguishing esophageal cancer patients from healthy individuals. Further optimization incorporating physiological factors, including smoking, drinking, and dietary habits, improved the model's accuracy to 92.4%, with a specificity of 93.1%, representing a significant improvement over previous studies. These results suggest that VOCs analysis in exhaled breath holds great promise as a non-invasive, cost-effective, and accurate method for early detection of esophageal cancer.

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly pivotal in quantitative proteomics. In this study, we present DIA-BERT, a software tool that harnesses a transformer-based pre-trained artificial intelligence (AI) model for analyzing DIA proteomics data. The identification model was trained using over 276 million high-quality peptide precursors extracted from existing DIA-MS files, while the quantification model was trained on 34 million peptide precursors from synthetic DIA-MS files. When compared to DIA-NN, DIA-BERT demonstrated a 51% increase in protein identifications and 22% more peptide precursors on average across five human cancer sample sets (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma), achieving high quantitative accuracy. This study underscores the potential of leveraging pre-trained models and synthetic datasets to enhance the analysis of DIA proteomics.

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer

Breast cancer (BRCA) continues to pose a serious risk to women’s health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biomarkers and prognostic models. Programmed cell death (PCD) pathways serve as a critical factor in tumor development and treatment response. However, the relationship between the diverse patterns of PCD and NAC in BRCA remains unclear. We integrated machine learning and multiple bioinformatics tools to explore the association between 19 PCD patterns and the prognosis of NAC within a cohort of 921 BRCA patients treated with NAC from seven multicenter cohorts. A prognostic risk model based on PCD-related genes (PRGs) was constructed and evaluated using a combination of 117 machine learning algorithms. Immune infiltration analysis, mutation analysis, pharmacological analysis, and single-cell RNA sequencing (scRNA-seq) were conducted to explore the genomic profile and clinical significance of these model genes in BRCA. Immunohistochemistry (IHC) was employed to validate the expression of select model genes (UGCG, BTG22, TNFRSF21, and MYB) in BRCA tissues. We constructed a PRGs prognostic risk model by using a signature comprising 20 PCD-related DEGs to forecast the clinical outcomes of NAC in BRCA patients. The prognostic model demonstrated excellent predictive accuracy, with a high concordance index (C-index) of 0.772, and was validated across multiple independent datasets. Our results demonstrated a strong association between the developed model and the survival prognosis, clinical pathological features, immune infiltration, tumor microenvironment (TME), gene mutations, and drug sensitivity of NAC for BRCA patients. Moreover, IHC studies further demonstrated that the expression of certain model genes in BRCA tissues was significantly associated with the efficacy of NAC and emerged as an autonomous predictor of outcomes influencing the outcome of patients. We are the first to integrate machine learning and bulk and scRNA-seq to decode various cell death mechanisms for the prognosis of NAC in BRCA. The developed unique prognostic model, based on PRGs, provides a novel and comprehensive strategy for predicting the NAC outcomes of BRCA patients. This model not only aids in understanding the mechanisms underlying NAC efficacy but also offers insights into personalized treatment strategies, potentially improving patient outcomes.

A New Process Model of Study Identification Specific to the Identification of Randomised Studies for Systematic Reviews of Medical Interventions

ABSTRACTBackgroundRecent work has illustrated that the same process of study identification is used in systematic reviews irrespective of the studies or data needs required for synthesis. We question if different review types should have their own specific models of study identification, to ensure the appropriate and timely identification of studies/study reports and to minimise research waste.ObjectiveIn this paper, we aim to:1. illustrate and report a new process model to identify randomised studies for systematic reviews of medical interventions; and2. situate the model in context of current practice using a worked example from a recent systematic review.MethodOur model splits the identification of studies from the identification of study reports by searching in distinct phases. It begins with searches of trials registry resources to identify studies, followed by searches of bibliographic databases to identify study reports or unregistered studies. Supplementary search methods are then used to identify unpublished studies. The model includes the possibility of secondary searches, and we consider the role of update searches.ConclusionA case study illustrates the application of the method alongside operational guidance.

Research on A Single-Load Identification Method Based on Color Coding and Harmonic Feature Fusion

With the growing global focus on sustainable development and climate change mitigation, promoting the low carbonization of energy systems has become an inevitable trend. Power load monitoring is crucial to achieving efficient power management, and load identification is the key link. The traditional load identification method has the problem of low accuracy. It is assumed that the technique of fusing harmonic features through color coding can improve the accuracy of load identification. In this paper, the load’s instantaneous reactive power, power factor and current sequence distribution characteristics are used as the mapping characteristics of the R, G and B channels of the two-dimensional V–I trajectory color image of the load using color coding technology. The harmonic amplitude characteristics are integrated to construct the mixed-color image of the load. The void residual shrinkage neural network is selected as the classification training model. The advantages and disadvantages of two residual shrinkage construction units, RSBU-CS and RSBU-CW, are analyzed. A single-load identification model with three RSBU-CWs is built. Different datasets verify the performance of the model. Compared with the test results of the ordinary color image dataset, the accuracy of the mixed-color image dataset is above 98%, and the accuracy of load identification is improved.

On feature representations for marmoset vocal communication analysis

ABSTRACT The acoustic analysis of marmoset (Callithrix jacchus) vocalisations is often used to understand the evolutionary origins of human language. Currently, the analysis is largely carried out in a manual or semi-manual manner. Thus, there is a need to develop automatic call analysis methods. In that direction, research has been limited to the development of analysis methods with small amounts of data or for specific scenarios. Furthermore, there is lack of prior knowledge about what type of information is relevant for different call analysis tasks. To address these issues, as a first step, this paper explores different feature representation methods, namely, HCTSA-based hand-crafted features Catch22, pre-trained self supervised learning (SSL) based features extracted from neural networks trained on human speech and end-to-end acoustic modelling for call-type classification, caller identification and caller sex identification. Through an investigation on three different marmoset call datasets, we demonstrate that SSL-based feature representations and end-to-end acoustic modelling tend to lead to better systems than Catch22 features for call-type and caller classification. Furthermore, we also highlight the impact of signal bandwidth on the obtained task performances.

Study on the risk identification of abnormal gas outbursts based on the mechanism of biological immunity

With the depletion of shallow coal resources, mining is gradually extending to deeper levels, with increasing gas content and gas pressure. Aabnormal gas outbursts phenomena occurs from time to time, posing a serious threat to coal mine safety production. Therefore, the risk identification of abnormal gas outbursts is of urgent practical significance. However, the process of identifying abnormal gas outbursts risk imposes higher requirements on the ability of adaptability, self-learning, and self-organization. The artificial immune technology based on the biological immune mechanism provides a powerful information processing and problem-solving paradigm, showing certain advantages in dynamic risk identification, which can meet the needs of dynamic risk identification of abnormal gas outbursts. In order to achieve dynamic risk identification of abnormal gas outbursts under complex underground conditions, provide decision-making basis for rapid warning and early prevention of abnormal gas outbursts, the principles of immune recognition of biological immune systems were adopted. Research was conducted on the identification of spatial risk zones for abnormal gas outbursts, the adaptive recognition algorithm based on T-B cell principles, and the type recognition of abnormal gas outbursts based on the Dynamic Time Warping algorithm. A risk identification model for abnormal gas outbursts based on the biological immune mechanism was constructed and tested in the 9111 mining face of a mine in Huaibei. The results show: (1) The adaptive recognition algorithm based on T-B cell principles can adaptively recognize the characteristic vectors of abnormal gas outbursts by adaptive adjustment of detectors and cloning and mutation of learning vectors, achieving the recognition and memorization of known or unknown feature vectors under dynamically changing environmental conditions. (2) The adaptive recognition algorithm for abnormal gas outbursts based on T-B cell principles and the type recognition algorithm for abnormal gas outbursts based on the Dynamic Time Warping algorithm, combined with the characteristics of biological immune systems, construct a risk identification model for abnormal gas outbursts based on the biological immune mechanism, which has the characteristics of adaptability, learning, and memorization. (3) Taking a abnormal gas outburst event in a certain 9111 working face of a mine in Huaibei as an example, the model was verified by inputting the characteristic vectors of abnormal gas outbursts and the output of the risk identification of abnormal gas outbursts based on the biological immune mechanism. The research results show that the dynamic risk identification model for abnormal gas outbursts based on the biological immune mechanism can meet the requirements of problem-solving in constantly changing complex environments, achieve dynamic risk identification of abnormal gas outbursts, and provide a basis for risk warning and intelligent decision-making for abnormal gas outbursts in mines.

Investigation of Halogenated Metallic Phthalocyanine (InPcCl and F16CuPc)-Based Electrodes and Palm Substrate for Organic Solid-State Supercapacitor Fabrication

In this work, we report on the fabrication of a novel Organic Double-Layer Supercapacitor (ODLSC) using recycled palm as the substrate and electrodes based on halogenated indium and copper phthalocyanines. The electrodes were characterized using Reflectance, the Kulbeka–Munk function, and Fluorescence. Finally, their electrical behavior was evaluated, and the results were compared with those obtained for a more conventional supercapacitor fabricated on polyethylene terephthalate substrate and using indium tin oxide film for electrodes. Based on the experimental measurements of the fabricated ODLSC, the parameter identification of the classical equivalent circuit model was carried out using the Least Squares of Orthogonal Distances (LSOD) algorithm. The results indicated that the palm supercapacitor exhibited behavior more like that of traditional supercapacitors, as the root square mean error (RMSE) values in the model approximation of the experimental data were in the order of 10−7. Furthermore, the models obtained allowed a determination of the device’s Electrical Impedance Spectroscopy (EIS), revealing that the Palm SC-T1 exhibited capacitive behavior. In contrast, the manufactured Palm SC-T2, PET SC-T1, and PET SC-T2 devices exhibited inductive behavior. All the materials used in this work, such as the substrates, electrodes, separator membranes, and electrolytes, have a high potential to be used in organic supercapacitors.

MVCF-TMI: A Travel Mode Identification Framework via Contrastive Fusion of Multi-View Trajectory Representations

Travel mode identification (TMI) plays a crucial role in intelligent transportation systems by accurately identifying travel modes from Global Positioning System (GPS) trajectory data. Given that trajectory data inherently exhibit spatial and kinematic patterns that complement each other, recent TMI methods generally combine these characteristics through image-based projections or direct concatenation. However, such approaches achieve only shallow fusion of these two types of features and cannot effectively align them into a shared latent space. To overcome this limitation, we introduce multi-view contrastive fusion (MVCF)-TMI, a novel TMI framework that enhances identification accuracy and model generalizability by aligning spatial and kinematic views through multi-view contrastive learning. Our framework employs multi-view learning to separately extract spatial and kinematic features, followed by an inter-view contrastive loss to optimize feature alignment in a shared subspace. This approach enables cross-view semantic understanding and better captures complementary information across different trajectory representations. Extensive experiments show that MVCF-TMI outperforms baseline methods, achieving 86.45% accuracy on the GeoLife dataset. The model also demonstrates strong generalization by transferring knowledge from pretraining on the large-scale GeoLife dataset to the smaller SHL dataset.

Online Estimation Method and Verification of Sampling Mass for Lunar Drilling in the Chang’E-6 Mission

The Chang’E-6 lunar mission successfully collected the lunar back surface and subsurface lunar regolith by excavating and drilling and returned the lunar regolith samples to the earth. Drilling–sampling system exhibits highly nonlinear characteristics due to the stratified structure of lunar regolith and unknown physical property parameters, making it prone to abnormal operating conditions and sampling disturbances. Furthermore, constrained by extraterrestrial environmental limitations, the system can only obtain health parameters, operational protocol parameters, and drilling status parameters while lacking direct measurement data on sampling mass. The development of online estimation methods for sampling mass under nonlinear and under-sensing characteristics poses significant technical challenges. Based on the mechanism of machine–regolith interaction and the experimental data of ground drilling and sampling, this paper constructs a sampling status identification model and a fuzzy pre-judgment model of sampling mass based on the downhole WOB based on the response characteristic parameters of the drilling–sampling stage. According to the telemetry data of Chang’E-6 lunar surface drilling–coring operation, the drilling–sampling mass is predicted to be 292.4 g, and the error between the predicted result and the actual sampling mass of 320 g is within 10%. This estimation method provides a new idea for the prediction of the fidelity sampling efficiency of extraterrestrial objects.

Modulation of Hydrogen Evolution Reaction Performance of MXenes by Doped Transition Metals: Comprehensive Exploration of High-Throughput Computing and Machine Learning.

Due to the unique properties of MXenes, the doping of transition metals can modulate their catalytic properties and make them potential materials for hydrogen evolution reaction (HER). Nevertheless, the extensive combinatorial space poses a challenge for rapid screening of catalysts. To address this issue, we conducted high-throughput calculations on a series of transition metal atom-doped Ti3CNO2 and Zr2HfCNO2. Furthermore, the local structure and the corresponding electronic structure changes are analyzed, focusing on their influence on the HER properties. Furthermore, site identification features were introduced to train a multisite prediction model with a final model accuracy of R2 = 0.97 and predicted the trend of hydrogen adsorption Gibbs free energy (ΔGH*) across a range of MXenes structures, which were doped with TM atoms. The results show that Nb, Sc, Rh, W, Ti, and V doping resulted in |ΔGH*| < 0.2 eV for more than 38 M'2M″CNO2, respectively, and they are effective dopant atoms for enhancing the catalytic ability of M'2M″CNO2. This study not only demonstrates the potential of doped TM atoms in enhancing the performance of MXenes HER but also highlights the importance of multisite prediction models in the rapid identification and development of efficient HER catalysts.

Application of deep learning models for pest detection and identification

The quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus of this research. Traditional monitoring methodologies tend to be ineffective and incorrect, resulting in wasted resources and loss of money. By incorporating cutting-edge AI and deep learning technologies, this study unveils a fresh method for rapid and precisely identifying pests in agricultural settings. This research makes use of high-resolution image technologies and Convolutional Neural Networks (CNNs) to showcase the promise of deep learning models in automated pest detection. The generalizability and model performance may be improved using transfer learning techniques leading to more efficient use of available resources. Key goals of this research include extensive testing across varied pest types and environmental settings, combined with the design and refinement of a CNN model specifically engineered for accurate pest identification. The gap between traditional pest monitoring practices and data-driven procedures is filled by the suggested method which ensures a significant increase in agricultural productivity that will contribute to greater food security and overall economic prosperity. This research strengthens the influential effects on agriculture, including enhancement of pest control, increasing food security, and boosting economic expansion. To promote this cutting-edge use of deep learning in agriculture, continuous cooperation between academics, businesses, and farmers is essential.

Predicting prolonged work absence due to musculoskeletal disorders: development, validation, and clinical usefulness of prognostic prediction models.

Given the lack of robust prognostic models for early identification of individuals at risk of work disability, this study aimed to develop and externally validate three models for prolonged work absence among individuals on sick leave due to musculoskeletal disorders. We developed three multivariable logistic regression models using data from 934 individuals on sick leave for 4-12weeks due to musculoskeletal disorders, recruited through the Norwegian Labour and Welfare Administration. The models predicted three outcomes: (1) > 90 consecutive sick days, (2) > 180 consecutive sick days, and (3) any new or increased work assessment allowance or disability pension within 12months. Each model was externally validated in a separate cohort of participants (8-12weeks of sick leave) from a different geographical region in Norway. We evaluated model performance using discrimination (c-statistic), calibration, and assessed clinical usefulness using decision curve analysis (net benefit). Bootstrapping was used to adjust for overoptimism. All three models showed good predictive performance in the external validation sample, with c-statistics exceeding 0.76. The model predicting > 180days performed best, demonstrating good calibration and discrimination (c-statistic 0.79 (95% CI 0.73-0.85), and providing net benefit across a range of decision thresholds from 0.10 to 0.80. These models, particularly the one predicting > 180days, may facilitate secondary prevention strategies and guide future clinical trials. Further validation and refinement are necessary to optimise the models and to test their performance in larger samples.

Study protocol for a stepped-wedge, randomized controlled trial to evaluate implementation of a suicide risk identification model among behavioral health patients in three large health systems

BackgroundAge-adjusted suicide rates have increased in the U.S. over the past 25 years. Algorithm-based methods for identifying individuals at risk for suicide based on electronic health record and claims data have been validated but few studies have evaluated implementation or effects on population-level suicide attempt rates.MethodsThis hybrid type I effectiveness-implementation pragmatic clinical trial will test a suicide risk identification model in behavioral health clinics at three large health systems. Local decision-makers will determine implementation specifics at each site. Clinics within each health system will be randomized to determine order of implementation. A stepped-wedge design using repeated measures pre/post-implementation maximizes statistical efficiency and power with fewer participants compared to a parallel design while allowing all clinics to participate. A pre-implementation period will serve as the baseline. The primary outcome will be the rate of suicide attempt per 1000 visits at 90- and 180-days following a behavioral health visit in which an individual was identified by the suicide risk model compared with the baseline period (no use of suicide risk model). Secondary outcomes include identification of suicide risk and recognition of individuals at risk for suicide (e.g., completed risk assessment), both compared to the baseline period. Generalized linear mixed models will be used to account for clustering within clinics and repeated measures over time, adjusting for relevant covariates to estimate the effect of the suicide risk model on outcomes. Implementation outcomes, including system-level determinants and clinician acceptance and use of the suicide risk model, will also be measured.ConclusionsFew suicide risk models derived from administrative and clinical data have been tested in real world care settings. This trial will determine whether the use of such a risk model reduces suicide attempts compared to usual care. By describing important implementation factors, use of such risk models, if effective, may be accelerated for other health care systems.Trial registrationClinicalTrials.gov NCT06060535.

Bayesian Identification and Estimation of Growth Mixture Models

Bayesian Identification and Estimation of Growth Mixture Models

Comprehensive and Explainable Fragmentation: A Machine Learning Approach for Fast and Accurate Mass Spectrum Prediction.

Mass spectrometry (MS) is a fundamental tool for chemical identification. The current in-silico prediction tools can handle broad instrument conditions, large molecular libraries or fragment structures only on a very limited level. In this work, we propose a dual-model machine learning strategy that can solve this problem by jointly a classification model for fragment identification and noise filtering, and a regression model for spectral prediction. With the help of attention mechanism, our method outperforms other algorithms in accuracy and efficiency, providing a deeper understanding of the molecular fragmentation behavior in mass spectra. Our method can facilitate the large-scale in-silico spectra calculations and the analysis of unknown molecular structures, which may promote wider applications for MS.

CRISPR/Cas9-Based therapeutics as a promising strategy for management of Alzheimer’s disease: progress and prospects

CRISPR/Cas9 technology has revolutionized genetic and biomedical research in recent years. It enables editing and modulation of gene function with an unparalleled precision and effectiveness. Among the various applications and prospects of this technology, the opportunities it offers in unraveling the molecular underpinnings of a myriad of central nervous system diseases, including neurodegenerative disorders, psychiatric conditions, and developmental abnormalities, are unprecedented. In this review, we highlight the applications of CRISPR/Cas9-based therapeutics as a promising strategy for management of Alzheimer’s disease and transformative impact of this technology on AD research. Further, we emphasize the role of CRISPR/Cas9 in generating accurate AD models for identification of novel therapeutic targets, besides the role of CRISPR-based therapies aimed at correcting AD-associated mutations and modulating the neurodegenerative processes. Furthermore, various delivery systems are reviewed and potential of the non-viral nanotechnology-based carriers for overcoming the critical limitations of effective delivery systems for CRISPR/Cas9 is discussed. Overall, this review highlights the promise and prospects of CRISPR/Cas9 technology for unraveling the intricate molecular processes underlying the development of AD, discusses its limitations, ethical concerns and several challenges including efficient delivery across the BBB, ensuring specificity, avoiding off-target effects. This article can be helpful in better understanding the applications of CRISPR/Cas9 based therapeutic approaches and the way forward utilizing enormous potential of this technology in targeted, gene-specific treatments that could change the trajectory of this debilitating and incurable illness.