Prioritization Method Research Articles

A hybrid generalized TODIM based risk prioritization method for failure mode and effect analysis with linguistic Z-numbers

The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans.

A procedure for bridge visual inspections prioritisation in the context of preliminary risk assessment with limited information

Visual inspections represent the most resource-expensive operation in bridge management systems (BMSs). Consequently, their planning cannot be limited to the results of risk assessments, which are often performed under incomplete or uncertain information, but operating costs should also be accounted for. In this light, this paper presents a new methodology to prioritise visual inspections in BMSs based on risk condition and operating cost assessment. Framed in a context of limited information, the methodology exploits an information gain criterion to tackle potential uncertainties in the risk classification, so enabling to optimize the potential outcomes of the inspection plan. The proposed approach is conceived to be implemented in Geographical Information Systems (GISs) to facilitate the construction of intuitive risk maps and inspection plans. The effectiveness of the framework is demonstrated through its application to a simulated bridge stock whose risk condition was evaluated through the new guidelines for bridge risk classification and management recently adopted in Italy. The presented results highlight the benefits of simultaneously considering bridge risk conditions and inspection costs in defining the inspection plan, hence the usefulness of information theory to prioritise visual inspections. Limitations and future improvements of the proposed prioritisation method have also been highlighted and discussed.

Evaluation of open-ended, clustering, and discrete choice methods for user requirements development in a low-income country context

High quality user requirements are positively correlated with successful design outcomes, but engaging stakeholders within low-income contexts can present financial and time-related challenges to product developers from non-local industrial and academic institutions with limited knowledge of the context. Existing literature provides guidance for engaging stakeholders during the early stages of product design in high-income country contexts, but few studies have examined the effectiveness of these methods in low-income country contexts. This study evaluated three user requirements elicitation and prioritization methods including open-ended, clustering, and discrete choice. Ghanaian healthcare delivery stakeholders with varying types of expertise, years of experience, and from various types of healthcare facilities were recruited to allow for diversity of responses. Participants included physicians (n = 10), nurses/midwives (n = 16), biomedical technicians (n = 14), and public health officers (n = 7). A hypothetical mechanical device for managing and treating postpartum hemorrhage was chosen to characterize each method's ability to elicit and prioritize user requirements. The open-ended method captured general requirements of a design concept, yet resulted in predominantly generic requirements. The results from the open-ended method were used to inform the clustering and discrete choice methods. The clustering and discrete choice methods were useful for inferring in-depth user requirements and eliciting stakeholder priorities. The clustering method revealed that usability and affordability were high-priority requirements among all four stakeholder groups. An individual difference scaling analysis was performed using the clustering method outcomes, which indirectly identified ease-of-use, availability, and effectiveness as the priority user requirements categories. Stakeholders ranked ease-of-use as the highest-priority user requirement, followed by performance, cost, and place-of-origin requirements, using the discrete choice method. Given the significance of the ease-of-use requirement, an analytical framework based on sub-requirements was developed for quantifying stakeholder needs. Lastly, the relative merits of the three elicitation approaches and their implications for use with different stakeholder groups were examined.

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Objectives: Software researchers have been taking advantage of various evolutionary optimization approaches by digitizing them. Test case selection and prioritization based on fault coverage criteria within a time-constrained environment is important in regression testing problem. Methods: This work empirically evaluates different approaches that includes evolutionary approaches (Ant Colony Optimization, Bee Colony Optimization, a combination of Genetic Algorithms and Bee Colony optimization), and a Greedy approach. These tetrad techniques have been successfully applied to regression testing. Also, tools have been developed for their implementation. Eight open-source test programs, written in C language have been used for empirical evaluation of the regression testing approaches. Findings: The accuracy achieved by t-GSC, being a greedy technique, was found to be least; while that of ACO was found to be the best. All the tetrad approaches yielded borderline better or worse results, while all the four gave excellent time and size gains. Novelty: There are many studies available in the literature that compare various regression testing approaches of a similar kind. Instead of repeating the same, it is intended to evaluate two well-accepted approximation approaches: a hybrid approach, and a greedy approach. It has been tried to evaluate the efficiency of the greedy approach with the metaheuristic approach. It is imperative to compare approaches following different algorithmic paradigms, yet trying to solve the same problem. Keywords: Ant Colony Optimization; Bee Colony Optimization; Genetic Algorithms; Greedy Set Cover; Software Testing; empirical comparison

Abstract 3111: The Sygnature CHARMD platform - combinatorial high-throughput assembly and review of molecular degraders

Abstract Recent interest the ability to degrade oncogenic proteins has grown exponentially, and the advent of Protein-Targeting Chimeras (PROTACs) has allowed many previously undruggable targets to be specifically targeted. However, the successful development of these bioavailable heterobifunctional degraders relies on the identification of a combination of ligase recruiter, linker and exit vector that allow formation of a competent ternary complex and target degradation. To this end, conventional single-compound synthesis is time-consuming and poses challenges for selection of degrader components with predictive SAR and ADME properties of the assembled bifunctional compound. Here, we demonstrate a high-throughput integrated platform for generation and assessment of bifunctional degraders, incorporating combinatorial chemistry using both commercial and bespoke linkers, cell-based activity assays, and assessment of in vitro DMPK properties (lipophilicity, metabolic stability and experimental polar surface area). Alongside this, we have in place computational methods for ternary complex modelling, linker design and PROTAC prioritisation. Altogether, our approach facilitates rapid screening and identification of bifunctional lead compounds for challenging oncology targets with the potential to deliver significant patient benefit. Our platform approach has been exemplified through application to oncology targets of significant current interests and our results in this area will be highlighted. Citation Format: Roland Hjerpe, Louise Birch, Alex Brien, Lola Cusin, Lorna Duffy, Benoit Gourdet, Rachel Lawrence, Philip MacFaul, Hannah Mortlock, Hannah Neal, Christopher Pearce, Erica Pitti, Gonzalo Robles, Edith Rodrigues, Catriona Scott, Rajesh Singh, Stuart Thomson, Yujia Zhang, Allan Jordan. The Sygnature CHARMD platform - combinatorial high-throughput assembly and review of molecular degraders [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3111.

Methods for Identification and Prioritization of Stressors at Impaired Sites

Methods for Identification and Prioritization of Stressors at Impaired Sites

Priority ranking for the best-worst method

The best-worst method is a recently developed approach that addresses pairwise comparisons in multi-criteria decision-making problems. It has attracted the interest of many academics and has gained widespread use for its effectiveness in reducing the pairwise comparison times and for its strong performance in maintaining consistency between judgments. However, the existing best-worst method prioritization methods don't consider the indirect judgments and fail to meet several established criteria. This paper first develops two prioritization methods, the approximate eigenvalue method and the logarithmic least squares method, to the best-worst method. Then, the inconsistency thresholds of the proposed prioritization methods are given. Finally, the Monte Carlo simulation method is applied to generate random matrixes to compare the performance of various prioritization methods on the selected criteria and analyze the relationship between different consistency measures. It is found that the logarithmic least squares method is the best prioritization method because of simple calculation, considering indirect judgments and containing minimum violations.

Optimized Plant Diversity and Carbon Storage for Priority Protection Areas in China

Biodiversity and carbon storage are two key ecosystem functions that are crucial to protect and maintain ecosystem balance. However, there is often little overlap between hotspots for these two different conservation purposes. Additionally, it is not well understood how these different spatial metrics affect these functions in protected ecosystems in China. Here, we explored the relationships between plant diversity metrics and carbon storage by using a large vascular plant distribution dataset, as well as soil fragile organic carbon and biomass carbon datasets in specific spatial areas across China. We also defined priority protection areas (PPAs) using a conservation prioritization method, where 30% of the study areas displayed the highest combined conservation value in carbon storage and plant species richness (SR), phylogenetic diversity (PD), phylogenetic endemism (PE) and evolutionary distinctness (ED). Our results indicated that the correlations between biodiversity metrics and carbon storage were very weak in spatial relationship. However, by including both of these functions in conservation targets, the PPAs could account for more than 95% of the species and evolutionary diversity (PD, ED), and stored large amounts of carbon. Additionally, we broadly divided the PPAs into win–win, high plant diversity, and high carbon areas based on the overlap of biodiversity and carbon storage hotspots. Altogether, our results highlight the importance of understanding and optimizing conservation efforts for different ecosystem functions in different PPAs. Ultimately, this work establishes an urgent need to expand protection in these areas to support mutual biodiversity and carbon storage beneficial solutions.

Towards Optimal Triage and Mitigation of Context-Sensitive Cyber Vulnerabilities

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology and security personnel resources currently available in organizations to mitigate the vulnerabilities are highly inadequate. As a result, systems routinely remain unpatched, thus making them vulnerable to security breaches from the adversaries. The potential consequences of an exploited vulnerability depend upon the context as well as the severity of the vulnerability, which may differ among networks and organizations. Furthermore, security personnel tend to have varying levels of expertise and technical proficiencies associated with different computer and network devices. There exists a critical need to develop a resource-constrained approach for effectively identifying and mitigating important context-sensitive cyber vulnerabilities. In this article, we develop an advanced analytics and optimization framework to address this need and compare our approach with rule-based methods employed in real-world cybersecurity operations centers, as well as a vulnerability prioritization method from recent literature. First, we propose a machine learning-based vulnerability priority scoring system (VPSS) to calculate the priority scores for each of the vulnerabilities found in an organization’s network and quantify organizational context-based vulnerability exposure. Next, we propose a decision-support system, which consists of a two-step sequential optimization approach. The first model selects the high priority vulnerability instances from the dense report subject to resource constraints, and the second model then optimally allocates them to the security personnel with matching skill types for mitigation. Experiment results conducted using a real-world vulnerability data set show that our approach 1) outperforms both the rule-based methods and the vulnerability prioritization method from literature in prioritizing context-sensitive vulnerabilities, which are found across highly susceptible organizationally relevant host machines, and 2) maximizes the pairs of vulnerability instance type and the respective security analyst skill type for optimal mitigation.

Orthopaedic and trauma surgeons’ prioritisation of app quality principles based on their demographic background

BackgroundAlthough apps are becoming increasingly relevant in healthcare, there is limited knowledge about how healthcare professionals perceive “quality” in this context and how quality principles that can aid them in assessing health-related apps may be prioritised.The objective was to investigate physicians’ views of predefined (general) quality principles for health apps and to determine whether a ranking algorithm applied to the acquired data can provide stable results against various demographic influences and may thus be appropriate for prioritisation.MethodsParticipants of an online survey of members of two German professional orthopaedics associations conducted between 02/12/2019 and 02/01/2020 were asked about their perception of a set of quality principles for health apps (i.e., “practicality,” “risk adequacy,” “ethical soundness,” “legal conformity,” “content validity,” “technical adequacy,” “usability,” “resource efficiency,” and “transparency”). Structured as a Kano survey, for each principle, there were questions about its perceived relevance and opinions regarding the presence or absence of corresponding characteristics. The available data were evaluated descriptively, and a newly developed method for prioritisation of the principles was applied overall and to different demographic strata (for validation).ResultsThree hundred eighty-two datasets from 9503 participants were evaluated. Legal conformity, content validity, and risk adequacy filled ranks one to three, followed by practicability, ethical soundness, and usability (ranks 4 to 6). Technical adequacy, transparency, and resource efficiency ranked last (ranks 7 to 9). The ranking based on the proposed method was relatively stable, irrespective of demographic factors. The principles were seen as essential, with one exception (“resource efficiency”). Only those with little to no interest in digitisation (22/382, 5.8%) rated the nine principles indifferently.ConclusionsThe specified quality principles and their prioritisation can lay a foundation for future assessments of apps in the medical field. Professional societies build upon this to highlight opportunities for digital transformations in medicine and encourage their members to participate.

Mobile crowdsourced test report prioritization based on text and image understanding

AbstractIn the process of mobile crowdsourced testing, a large number of test reports are generated, which often consist of short text and rich image information. One of the critical issues is how to review these reports efficiently. Researchers have recently proposed clustering, classification, and prioritization techniques to solve this problem. However, existing studies directly use related technologies to text description and segment the text content without further understanding of the text content. By deeply digging into the text information and distinguishing it according to its actual meaning, the sentences described in the text can be categorized into two types: Describing abnormal system behavior and describing reproduction steps. This paper proposes a mobile crowdsourced test report prioritization technique to improve performance. First, we use a TextCNN trained on large‐scale projects to distinguish the text descriptions of reports, then extract features from the text and screenshot information, respectively. Then we apply a clustering technique to gather similar reports. Finally, the inspection order is sampled from the clustering results. To validate our approach, we conduct experiments on six industrial crowdsourced projects. The results show that our method can detect all bugs faster in a limited time than existing prioritization methods, which can improve the bug reports review efficiency.

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

Most software companies have extensive test suites and re-run parts of them continuously to ensure that recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This article analyses two decades of TCP research and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to system under test code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.

Optimal gene prioritization and disease prediction using knowledge based ontology structure

Prioritizing candidate genes is essential for genome-based diagnostics of various hereditary disorders. Furthermore, it is a difficult task with particular and noisy information about genes, illnesses, and relationships. Although several computer methods for disease gene prioritization have been developed, their efficiency is limited by manually created traits, network architecture, or pre-established data fusion criteria. Hence, this research proposes a unique gene prioritization and disease prediction model. Initially, the gathered information is pre-processed by a data cleaning model. In the proposed gene prioritization phase, the pre-processed data are tokenized. Then a new knowledge-based ontology structure is constructed with the improved skewness-based semantic similarity function. The ensemble classifier is constructed along Recurrent Neural Network (RNN), optimized fuzzy logic, and also Deep Belief Network (DBN) to forecast the gene disorders in the prediction phase. The retrieved features from the feature extraction phase are used to train RNN; while the extracted knowledge bases are used to train the DBN, then the results are fed into the optimized fuzzy logic. The fuzzy logic is the primary indication; its fuzzification function is fine-tuned employing a methodology to improve illness prediction accuracy. A recommended new hybrid system, named as Cauchy’s Mutated Corona Virus Optimization Algorithm (CMCOA), is the upgraded version of the CVOA, a typical coronavirus optimization technique. Finally, to evaluate the efficiency of the projected model, a comparison of the suggested and existent models is performed with respect to various measures. In particular, the proposed model has recorded the highest accuracy as 93 % at 60 % of training, which is 42.5 %, 36.1 %, 33.3 %, 41.1 %, 48.5 %, 48.5 %, 9 %, 8 %, 8 %, 8 %, 8 %, and 14.5 % improved over existing models like GCN, GCN [6], SVM, CNN, Bi-LSTM, LSTM, GRU, fuzzy, EC + GOA, EC + SSO, EC + CMBO, EC + SMA and EC + CCVOA, respectively. The precision of the suggested work with improved features &CMCOA is 15.5 %, and 14.42 % superior to the proposed work without existing features & CMCOA and proposed work with existing features & CMCOA approaches.

The MADAG Strategy for Fault Location Techniques

Spectrum-based fault localization (SBFL), which utilizes spectrum information of test cases to calculate the suspiciousness of each statement in a program, can reduce developers’ effort. However, applying redundant test cases from a test suite to fault localization incurs a heavy burden, especially in a restricted resource environment, and it is expensive and infeasible to inspect the results of each test input. Prioritizing/selecting appropriate test cases is important to enable the practical application of the SBFL technique. In addition, we must ensure that applying the selected tests to SBFL can achieve approximately the effectiveness of fault localization with whole tests. This paper presents a test case prioritization/selection strategy, namely the Minimal Aggregate of the Diversity of All Groups (MADAG). The MADAG strategy prioritizes/selects test cases using information on the diversity of the execution trace of each test case. We implemented and applied the MADAG strategy to 233 faulty versions of the Siemens and UNIX programs from the Software-artifact Infrastructure Repository. The experiments show that (1) the MADAG strategy uses only 8.99 and 14.27 test cases, with an average of 18, from the Siemens and UNIX test suites, respectively, and the SBFL technique has approximate effectiveness for fault localization on all test cases and outperforms the previous best test case prioritization method; (2) we verify that applying whole tests from the test suite may not achieve the better effectiveness in fault localization compared with the tests selected by MADAG strategy.

Deep learning predicts the impact of regulatory variants on cell-type-specific enhancers in the brain.

Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization. Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of individual genetic variants in these regions. We show that the variants highlighted by our deep learning models are targeted by purifying selection in the human population, likely indicating a functional role. We integrate our deep learning predictions with statistical fine-mapping results for 8 brain-related traits, identifying 63 distinct candidate causal variants predicted to contribute to these traits by modulating enhancer activity, representing 6% of all genome-wide association study signals analyzed. Overall, our study provides a valuable computational method that can prioritize individual variants based on their estimated regulatory impact, but also highlights the limitations of existing methods for variant prioritization and fine-mapping. The data underlying this article, nucleotide-level importance scores, and code for running the deep learning pipeline are available at https://github.com/Pandaman-Ryan/AgentBind-brain. mgymrek@ucsd.edu. Supplementary data are available at Bioinformatics Advances online.

Risk Assessment of Combined Exposure to Multiple Chemicals at the European Food Safety Authority: Principles, Guidance Documents, Applications and Future Challenges.

Human health and animal health risk assessment of combined exposure to multiple chemicals use the same steps as single-substance risk assessment, namely problem formulation, exposure assessment, hazard assessment and risk characterisation. The main unique feature of combined RA is the assessment of combined exposure, toxicity and risk. Recently, the Scientific Committee of the European Food Safety Authority (EFSA) published two relevant guidance documents. The first one "Harmonised methodologies for the human health, animal health and ecological risk assessment of combined exposure to multiple chemicals" provides principles and explores methodologies for all steps of risk assessment together with a reporting table. This guidance supports also the default assumption that dose addition is applied for combined toxicity of the chemicals unless evidence for response addition or interactions (antagonism or synergism) is available. The second guidance document provides an account of the scientific criteria to group chemicals in assessment groups using hazard-driven criteria and prioritisation methods, i.e., exposure-driven and risk-based approaches. This manuscript describes such principles, provides a brief description of EFSA's guidance documents, examples of applications in the human health and animal health area and concludes with a discussion on future challenges in this field.

Methods for living guidelines: early guidance based on practical experience. Paper 3: selecting and prioritizing questions for living guidelines

ObjectivesThis article is part of a series on methods for living guidelines, consolidating practical experiences from developing living guidelines. It focuses on methods for identification, selection, and prioritization of clinical questions for a living approach to guideline development. Study Design and SettingMembers of the Australian Living Evidence Consortium, the National Institute of Health and Care Excellence and the US Grading of Recommendations, Assessment, Development and Evaluations Network, convened a working group. All members have expertize and practical experience in the development of living guidelines. We collated methods, documents on prioritization from each organization's living guidelines, conducted interviews and held working group discussions. We consolidated these to form best practice principles which were then edited and agreed on by the working group members. ResultsWe developed best practice principles for (1) identification, (2) selection, and (3) prioritization, of questions for a living approach to guideline development. Several different strategies for undertaking prioritizing questions are explored. ConclusionThe article provides guidance for prioritizing questions in living guidelines. Subsequent articles in this series explore consumer involvement, search decisions, and methods decisions that are appropriate for questions with different priority levels.

Construction of group systems of expert advantages using the a-technology of applying classical decision-making criteria

Decision making is a more important system-forming characteristic of expert activity. Therefore, studying the specifics of the relevant choices and their optimization, especially from the perspective of the influence of the human factor, is an urgent scientific and practical task. Among the components of this influence, which simultaneously determine the attitude of specialists to the indicators and characteristics of the objects of examination, in particular the features of investment attractiveness, systems of advantages are identified, by which we mean an ordered series of these features: from the most significant, acceptable, weighty, etc. — to less significant. The qualimetry of the significance of the features of the investment attractiveness of objects of examination in the ordering scale is linear, therefore it makes the corresponding measurements “rough” and can even provoke statistical errors of the І-ІІ kind when moving from individual systems of preferences to group ones. The research involved m = 90 specialists who are constantly involved by SSI “UkrISTEI” in conducting various examinations and who, using our methodology, built individual systems of advantages on a spectrum of n = 18 characteristic features of the investment attractiveness of the objects of examination. Using a multi-step technology for identifying and filtering out marginal thoughts, as well as eliminating “survivor bias”, four subgroups were identified from the original sample, mC = 30 people, mH = 12 people, mM = 11 people, mT = 6 people, with consistency group opinions satisfies the range of system-information consistency criteria we introduced at a high level of significance α = 1 %. It is substantiated that the mC subgroup is the basic. A decision matrix has been constructed, where the ranks of investment attractiveness features are defuzzified by the corresponding normalized weight coefficients determined by the method of prioritization. To solve this matrix, classical decision-making criteria (Wald, Savage, Bayes-Laplace, Hurwitz) were applied and group systems of advantages were obtained, characterized by the features of these criteria. A high statistically probable coincidence of the advantages of group systems obtained by this method has been established and ways for further development of α-technology have been outlined.

Algorithms for making custom solutions in the digital economy

The purpose of the article is to find ways to solve the problem of making custom solutions in conditions of uncertainty, risk, poorly structured processes for the development of new product designs with characteristics that meet the rapidly changing needs of the business environment. The solution uses the method of prioritization with expert assessments. The author identifies the existing shortcomings of the methods of prioritization and suggests changes and additions that eliminate the shortcomings in relation to the problem being solved. The developed model, when used in management decision-making, allows us to identify the best functions of the product for their inclusion in the design of an innovative model, to make a rating of the significance of functional properties for the consumer and the manufacturer, which contributes to maximizing demand and competitiveness of the created model, operational innovative changes in the properties of the product corresponding to the rapidly changing demands of the competitive business environment. The article is intended for business leaders, researchers, university students.

PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology.

Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer. We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches. The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git). Supplementary data are available at Bioinformatics online.