Accurate Selection Research Articles

2D Facial Analysis Performed by a Human Operator Versus an Artificial Intelligence Software

Background: Artificial intelligence (AI) has been a contribution in recent years to the development of new tools for dental, surgical, and esthetic treatment. In the case of image diagnosis, AI allows automated analysis of some facial parameters. The aim of this study was to evaluate the precision and reproducibility of these IA analyses compared with a human operator. Methods: Thirty subjects were selected, and frontal and profile photographs were taken of each. The photographs were previously calibrated and then analyzed by a human operator and an AI for vertical, horizontal, symmetry parameters, and facial profile angles. Results: Statistically significant differences were found in at least 1 vertical, horizontal, symmetry, and facial profile measurement. Conclusions: The 2D facial analysis performed on photographs using AI is an interesting and constantly evolving tool. However, there are still many differences with the analysis performed by the human operator, so it should be used carefully. Further studies using metric measurements should be performed to contrast the values obtained by human operators and AI and to analyze the accuracy of point selection in facial soft tissues.

Design and analysis of power decoupling based microinverter considering parasitic parameters

With fossil energy reducing year by year, more and more attention to the new energy sources greatly promotes the development of the distributed power generation system, especially the low-power photovoltaic system. However, the power injected into the single-phase grid is time-varying, an electrolytic capacitor with large bulk should be paralleled with PV to balance the input and output power ripple. But the electrolytic capacitor has a very short lifetime, which could shorten the whole inverter’s lifetime. In this paper, the operational principle of the power decoupling based microinverter considering parasitic parameters is proposed, in which a film capacitor with small capacitance value replaces the electrolytic capacitor to improve the lifetime of the inverter. Further, the operating mode of the transformer and the decoupling capability is analyzed, and obtains the reasons affecting the inverter steady-state performance. Finally, the simulation and experiment validation of the proposed microinverter have been accomplished. Base on the analysis method proposed in this paper, a more accurate device selection can be provided for industrial design.

Predictors of Right vEntricular Failure aftER LVAD implantation: PREFER LVAD study

Abstract Introduction The use of left ventricular assist devices (LVAD) is increasing both as bridge-to-transplantation or destination therapy, considering the lack of heart donors for transplantation in advanced heart failure (AHF) population. Right ventricular post-implantation failure, major bleedings and infective events remain the principal adverse events in LVAD carriers, thus an accurate selection of candidates is needed. Purpose To identify between clinical, laboratory, standard and advanced (Speckle Tracking, STE) echocardiography, and right heart catheterization (RHC) indices, the best predictors of outcome after LVAD implantation. Materials and Methods We screened in our third-level center 30 patients with AHF, followed up from May 2013 to February 2022, for the assessment of LVAD implantation suitability. They all underwent a complete cardiological history collection, electrocardiography, standard and STE left ventricular (LV) and left atrial (LA) size and function assessment. Regarding the right ventricle, SIENA score parameters, including RV sphericity index, RV free wall longitudinal strain (LS), right ventricular fractional area change (RVFAC), and 3D ejection fraction were used for exclude RV disfunction. RHC was performed on the same day of echocardiography. Follow-up was conducted at 1, 3, 6, 9 and 12 months repeating all the assessments, for the occurrence of RV failure, all-causes mortality, hemorrhagic events and re-hospitalization for heart failure. With a univariate and multivariate statistical analysis, we went to test among all the parameters the possible predictors of outcome. Results The final population consists of 29 patients (93% male, mean age of 63±11 years), in NYHA class III or IV and a severe reduction of left ventricular (LV) ejection fraction (EF), in whom arterial hypertension and smoking, present or previous, were present in 58% and 65% of cases, respectively. The population was divided into two groups according to the occurrence of at least one cardiovascular event. 11 major events were recorded: including 4 hemorrhagic events, 3 all-causes mortality and 4 hospital admission for HF. No RV failure occurred in the first 12 months of follow-up. No differences were identified between the two groups in terms of echocardiographic indices and RV function, including RVFAC or RV-FWLS. At univariate analysis (see Table 1), the only parameter able to predict major CV events was central venous pressure (CVP)/pulmonary capillary wedge pressure (PCWP) ratio, obtained by RHC, an index of balance between right and left heart filling pressure. Conclusions A careful application of SIENA score indices before LVAD implantation can significantly reduce the incidence of post-operative RV failure, as in our small population. Our results also confirmed the paramount role of RHC in the diagnostic work up of AHF.

GSOR03 Presentation Time: 11:40 AM: 3D-Printed Sizing Cylinders for HDR Endometrial Cancer Brachytherapy: Feasibility, Selection Accuracy, and Consistency

GSOR03 Presentation Time: 11:40 AM: 3D-Printed Sizing Cylinders for HDR Endometrial Cancer Brachytherapy: Feasibility, Selection Accuracy, and Consistency

Exploring Pointer Enhancement Techniques for Target Selection on Large Curved Display

Large curved displays are becoming increasingly popular due to their ability to provide users with a wider field of view and a more immersive experience compared to flat displays. Current interaction techniques for large curved displays often assume a user is positioned at the display's centre, crucially failing to accommodate general use conditions where the user may move during use. In this work, we investigated how user position impacts pointing interaction on large curved displays and evaluated cursor enhancement techniques to provide faster and more accurate performance across positions. To this effect, we conducted two user studies. First, we evaluated the effects of user position on pointing performance on a large semi-circular display (3m-tall, 3270R curvature) through a 2D Fitts' Law selection task. Our results indicate that as users move away from the display, their pointing speed significantly increases (at least by 9%), but accuracy decreases (by at least 6%). Additionally, we observed participants were slower when pointing from laterally offset positions. Secondly, we explored which pointing techniques providing motor- and visual-space enhancements best afford effective pointing performance across user positions. Across a total of six techniques tested, we found that a combination of acceleration and distance-based adjustments with cursor enlargement significantly improves target selection speed and accuracy across different user positions. Results further show techniques with visual-space enhancements (e.g., cursor enlargement) are significantly faster and more accurate than their non-visually-enhanced counterparts. Based on our results we provide design recommendations for implementing cursor enhancement techniques for large curved displays.

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid–based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student’s t test and Cohen’s D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values (<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}) and the majority of Cohen’s D values (>0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 0.5$$\\end{document}) showed that MEF-AlloSite’s 1–6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

Benign paroxysmal positional vertigo: diagnosis, treatment, rehabilitation. Current concepts on the role of betahistine in the complex treatment of patients with benign paroxysmal positional vertigo

The treatment of patients with benign paroxysmal positional vertigo (BPPV) is an urgent issue in modern medicine.Expert Council on the diagnosis and treatment of BPPV was held in Moscow on June 1, 2024. Timely diagnosis and effective treatment play a key role in the management of patients with BPPV, reduce the risk of falls and related injuries, prevent the development of emotional disorders and impairment of daily activities. Accurate patient routing and selection of appropriate treatment can significantly reduce the duration of disability and the burden on the healthcare system. Relatively simple positional tests aimed at detecting involvement of various semicircular canals have been found to be of paramount importance in the diagnosis of BPPV. Repositioning maneuvers are of paramount importance in the treatment of patients with BPPV. They vary depending on the canal affected and aim to move the otoliths out of the semicircular canals to the vestibule of the labyrinth. If repositioning maneuvers can not be performed, effective methods of vestibular gymnastics can be used. When discussing drug therapy, it was found that the use of betahistine in addition to repositioning maneuvers or vestibular gymnastics increases the efficacy of therapy, accelerates the recovery process and prevents the development of residual non-positional vertigo.

Study of substituents-regulated dyeing properties in non-aqueous media systems based on MD simulation and DFT

To investigate the relationship between N-acetoxyethyl group and dyeing properties of disperse dyes in D5 non-aqueous medium dyeing system, the adsorption and diffusion behaviors of three disperse dyes with different structures were studied by molecular dynamics (MD). The diffusion coefficients, the planar distribution densities of dyes in different directions, and the intermolecular interaction energies were calculated. Molecular dynamics simulation (MD) was used to calculate molecules polarity index (MPI). Meanwhile, the practical dyeing experiments also were investigated to compare with simulated results. The results showed that the adsorption and diffusion behavior of disperse dyes with different structures were significantly different. Compared with dye B257, D-1 and D-2 are more easily adsorbed and diffused into the interior of the fiber because of the introduction of polar acetoxyethyl group. Moreover, the diffusion coefficient of D-2 in the fiber was relatively low, indicating that the combination between dye and polyester fiber was stable. XY plane distribution density and MPI revealed that the introduction of acetoxyethyl was beneficial to increase the adsorption amount of dye on the fiber, which was mainly related to the high binding energy between dye molecule and polyester fiber. The results of dyeing experiments showed that the decreased solubility of disperse dyes in the D5 medium was conducive to improving the dyeing rate of disperse dyes. This was consistent with the simulated interaction between dyes and D5. This study will provide a basis for further rapid and accurate development and selection of disperse dyes with high dyeing rate in non-aqueous medium systems.

A new modeling strategy for the predictive model of chub mackerel (Scomber japonicus) central fishing grounds in the Northwest Pacific Ocean based on machine learning and operational characteristics of the light fishing vessels

The chub mackerel (Scomber japonicus) is one of the most influential small pelagic fish in the Northwest Pacific Ocean, and accurate modeling approaches and model selection are critical points in predicting the Scomber japonicus fishing grounds. This study investigated the changes in catches and fishing days on no moonlight and bright moonlight days (2014-2022) and compared the differences in predictive performance between the LightGBM and RF models on three datasets under the two modeling approaches [those based on the light fishing vessels operational characteristics (Approach one) and those not (Approach Two)]. The results were as follows: 1) Stronger moonlight intensity (e.g., full moon) can limit the fishing efficiency of light fishing vessels, with most years showing a trend of a higher percentage of fishing days on bright moonlight days than catches percentage, i.e., no moonlight days resulted in higher catches with lower fishing days; 2) Compared to Modeling Approach Two, under Modeling Approach one, RF model achieved better predictive performance on dataset B, while the LightGBM model achieved better predictive performance on both datasets A and B; 3) Overall, the Approach One achieved more satisfactory prediction performance, with the optimal prediction performance on the complete dataset C improved from 65.02% (F1-score of the RF model, Approach Two) to 66.52% (F1-score of the LightGBM model, Approach Two); 4) Under the optimal modeling approach (Approach One) and the optimal model (LightGBM model), the differences in the importance of the variables on dataset A (no moonlight days) and dataset B (bright moonlight days) were mainly centered on the environmental variables, with CV, SLA, and SSS being the most important in dataset A, and CV, DO, and SLA being the most important in dataset B. This study provides a more scientific and reasonable modeling undertaking for the research of light purse seine fishing vessels, which is conducive to guiding fishermen to select the operating area and operating time of the Scomber japonicus fishery more accurately and comprehensively and realizing the balanced development of fisheries in terms of ecology and economy.

A comprehensive approach with DTW-driven IMF selection, multi-domain fusion, and TSA-based feature selection for compound fault diagnosis

Fault diagnosis in industrial machinery ensures operational efficiency and prevents downtimes. However, compound faults present a significant challenge due to their complex nature. This manuscript proposes a novel methodology for identifying compound faults, integrating advanced techniques to address the challenges present in existing methods. Firstly, the ICEEMDAN algorithm is employed for signal decomposition, enabling the extraction of noise-free IMFs crucial for identifying fault signatures. A DTW-based selection criteria is introduced to enhance the accuracy of IMF selection. The Bessel Transform is utilized for time–frequency conversion. HU invariant moments are incorporated as image features, and multi-domain feature fusion is implemented to understand compound faults comprehensively. A novel TSA-based feature selection method is proposed to select optimal features from the fused feature set. The methodology’s effectiveness is evaluated through a case study of compound gear-bearing faults. The results demonstrate the superiority of the approach, with a testing accuracy of 99 %.

Research and clearance analysis on of steam twin-screw expander employed in indutrial waste heat recovery

In actual factories, screw expanders frequently operate under partial loads.To address the lack of theoretical research and the risks of errors in engineering design associated with steam pressure differential power generation under these conditions,a simulation of the twin-screw expander's working process was developed. The rotor profile was designed, geometric variables such as rotor mesh clearance were calculated, and models for leakage and heat transfer were established. A suction pressure correction model was proposed for the suction process under partial loads, along with correction parameters to simplify simulations.Experimental results demonstrate that the twin-screw expander operates stably under highly fluctuating conditions, demonstrating excellent variable load performance.As the load decreases, the loss of suction pressure reduces the expander's pressure differential, leading to lower leakage and improved efficiency. When the load is reduced from 100 % to 65 %, the volumetric efficiency increases from 78.87 % to 84.76 %, and the isentropic efficiency rises from 66.81 % to 74.25 %.Compared to experimental data, the flow calculation error of the suction pressure correction model presented in this study is controlled within 3 %.This model addresses performance calculations under partial loads, ensuring that theoretical models better align with real-world applications and supporting accurate selection of twin-screw expanders.This model enables appropriate selection of expanders to reduce pressure losses and enhance economic efficiency.

THE ADOPTION OF AI-DRIVEN CHATBOTS INTO A RECOMMENDATION FOR E-COMMERCE SYSTEMS TO TARGETED CUSTOMER IN THE SELECTION OF PRODUCT

The research looks into the Adoption of AI-Driven chatbots into a recommendation for E-Commerce systems to targeted customer in the selection of product, particularly their function in product selection and overall customer experience. The study is based on five assumptions: improved product selection accuracy, increased user happiness, influence on customer engagement and purchase decisions, enhanced user experience and retention rates, and effect on consumer trust. The findings show that AI-powered chatbots greatly improve product selection accuracy, with a regression weight of 0.693 and a beta coefficient of 0.480, representing a 48% improvement per unit of implementation. User satisfaction has also significantly improved, as evidenced by a regression weight of 0.897 and a beta coefficient of 0.840, both with high statistical significance. Chatbots have a favourable impact on consumer engagement and purchasing decisions, as indicated by substantial regression weights and beta coefficients for each variable. AI chatbots improve user experience and retention, as evidenced by high regression weights and R² values. Finally, chatbots improve customer trust, with a regression weight of 0.447 and a beta coefficient of 0.200. Overall, the study shows that AI-powered chatbots significantly increase several aspects of e-commerce, including product selection accuracy, user pleasure, engagement, retention, and trust.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Uncovering etiologic genes through whole exome sequencing in pediatric epilepsy: A case series from Thailand

Introduction Developmental and epileptic encephalopathy (DEE) is characterized by seizures that are difficult to control for a long time and affect development in children who are previously normal or delayed. Therefore, children with DEE should be diagnosed promptly because certain types of the disease respond well to specific medications. In developing countries with limited universal coverage for whole exome sequencing (WES), identifying key clinical features in this patient group will help us make more accurate selections for investigations. The purpose of this study was to determine the prevalence of WES and its common clinical features in children with epileptic encephalopathy. Methods Ten volunteers aged 0-15 years were diagnosed with epilepsy with two or more symptoms of drug-resistant epilepsy, developmental delays, and abnormal nervous system/or dysmorphic features, and their electroencephalogram (EEG) showed abnormal background or specific patterns of epileptiform discharges. These were subjected to WES for the standard &gt; 400 genes in the epilepsy panel. Results The established diagnosis was 4/10. Two known pathogenic variants, SCN2A and PCDH19. Two novel pathological variants, CHD2 and SCN1A. These are drug-resistant epilepsy, which is initially difficult to control and cannot stop antiseizure medications. Out of the 2/4 had moderate to severe intellectual disability. 3/4 had generalized epileptiform discharge activities. Conclusions This study showed a similar detection rate to that of a previous WES study. All the patients had difficult-to-treat epilepsy. For those who have not found abnormalities with the same clinical symptoms, further examinations using other methods should be conducted.

Research on single-phase grounding detection method in small-current grounding systems based on image recognition

In small-current grounding systems, the fault current during a single-phase grounding fault is small, and the transient process is complex, leading to challenges in line selection accuracy and reliability. This paper proposes a single-phase grounding line selection method based on transfer learning using the ResNet-50 model. Zero-sequence voltage and current waveforms at the fault moment are preprocessed and combined to generate training data for the model. Simulation results using PSCAD demonstrate that the proposed method achieves 99.77% validation accuracy, even under noisy conditions. These results confirm the method’s feasibility and reliability in identifying grounding faults in various conditions.

Novel intravesical therapies and delivery systems for the management of bladder cancer.

Bladder cancer is a substantial burden for public health worldwide. A risk-adapted treatment strategy is required for non muscle-invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). To date, treatment includes surgery with or without peri-operative local or systemic treatment. The aim of this review was to explore novel intravesical therapies and delivery systems emerging in NMIBC and MIBC. Several novel intravesical therapies and delivery systems for NMIBC and MIBC treatment recently emerged. Hyperthermic intravesical chemotherapy (HIVEC) allows a reasonable cancer control in selected high-risk NMIBC. Novel intravesical drugs such as nadofaragene firadenovec, Oncofid-P-B or Nogapendekin alfa-inbakicept seem to be safe and well tolerated. However, their efficacy in high-risk NMIBC should be further investigated. Hydrogels appear to be safe, well tolerated and potentially efficient in primary chemoablation in selected cases of low-grade intermediate-risk NMIBC tumors. Drug-releasing intravesical systems (drug-RIS) such as TAR-200 are safe and well tolerated, providing high partial and complete response rate in both NMIBC and MIBC patients. The armamentarium for the treatment of bladder cancer patients is expanding, notably with HIVEC, hydrogels, drug-RIS and novel therapies. However, accurate patients' selection is key to prevent disease progression in any bladder-sparing strategy, and radical cystectomy remains the gold-standard to date.

Novel Colorimetric and Near-Infrared Ratiometric Fluorescent Probe for Sensing Cysteine in Food Samples, Plants, and Living Cells.

Cysteine (Cys) is a crucial biothiol that acts a significant function in food samples and biological systems, including plant roots and living cells. Hence, we developed a novel colorimetric and near-infrared ratiometric fluorescent probe (CT), composed of coumarin and tetrahydroacridine-conjugated indole salt, for the detection of Cys. Upon reaction with Cys, the probe undergoes a specific N-substitution reaction, resulting in a notable colorimetric change and a significant ratiometric fluorescent response in both visible and near-infrared emission channels. These dual-channel ratiometric fluorescence changes are completely independent, enabling the probe to obtain great selectivity, sensitivity, and exceptional detection accuracy. Leveraging these attributes, the probe was employed to provide accurate quantitative analysis of Cys in food samples. Furthermore, confocal imaging demonstrated that the probe could monitor both exogenous and endogenous Cys levels in living cells and track Cys changes in plant roots under heavy metal stress. This work presents a dependable and accurate imaging solution for tracking and identifying Cys of real food, plants, and living cells.

Tumor fraction-based prognostic tool for cancer patients referred to early phase clinical trials

Selecting patients for phase I cancer trials is crucial to ensure a sufficient life expectancy. Frail patients, better suited for palliative care, should not be exposed to new drugs with minimal benefit. Enrolling patients at high risk of early death can jeopardize the study. Our analysis of two large precision medicine studies used tumor fraction from ctDNA to develop a predictive model, demonstrating notable predictive accuracy and aiding in patient selection.

Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.

Developing innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients. We analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs. We identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications. Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.