Mahmoud Iskandarani | AI | Editorial Board Member

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Prof. Mahmoud Iskandarani | AI | Editorial Board Member

Professor | Al-Ahliyya Amman University | Jordan

Prof. Mahmoud Zaki Iskandarani’s research focuses on advancing wireless communication systems with specialization in wireless sensor networks (WSNs), intelligent reflecting surfaces (IRS), robotic communication platforms, adaptive beamforming techniques, and electromagnetic field modeling. With 84 publications, 167 citations, 5 h-index his scholarly output reflects sustained productivity and growing global recognition. His recent works address energy-efficient communication for robotic WSNs, SINR enhancement through adaptive IRS design, hybrid beamforming using Gaussian interpolation, and analytical–numerical modeling supported by neural predictors, demonstrating strong integration of computational intelligence with communication engineering. He has also contributed to transportation and mobility research through studies on BRT system impacts on congestion and safety, highlighting his capacity for multidisciplinary problem-solving. Collaborating with 11 co-authors across diverse domains, he publishes in reputable international journals such as IEEE Access, Journal of Communications, Journal of Robotics, and Cogent Engineering, reinforcing the breadth and relevance of his contributions. Collectively, his research advances theoretical models and practical frameworks that improve spectral efficiency, path-loss prediction accuracy, energy optimization, and network reliability in emerging 6G-oriented and autonomous systems, generating technological and societal value across communication, robotics, and smart mobility sectors.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Gardner, J. W., Iskandarani, M. Z., & Bott, B. (1992). Effect of electrode geometry on gas sensitivity of lead phthalocyanine thin films. Sensors and Actuators B: Chemical, 9(2), 133–142.

Cited by: 63

2. Iskandarani, M. Z. (2008). Effect of information and communication technologies (ICT) on non-industrial countries—Digital divide model. Journal of Computer Science, 4(4), 315.

Cited by: 41

3. Shilbayeh, N. F., & Iskandarani, M. Z. (2004). Quality control of coffee using an electronic nose system. American Journal of Applied Sciences, 1(2), 129–135.

Cited by: 41

4. Iskandarani, M. Z. (2025). Effect of Intelligent Reflecting Surface on WSN Communication with Access Points Configuration. IEEE Access, 13, 13380–13394.

Cited by: 29

5. Iskandarani, M. Z. (2025). Energy and path loss analysis of wireless sensor networks on a robotic body (WS Robotic). Bulletin of Electrical Engineering and Informatics, 14(3), 1794–1807.

Cited by: 25

Prof. Mahmoud Zaki Iskandarani’s work advances the performance, adaptability, and intelligence of wireless communication systems, contributing to the foundations of future 6G networks and autonomous robotic platforms. His research supports societal and industrial innovation by enabling more efficient connectivity, improved mobility systems, and smarter technological infrastructures worldwide.

Bhargav Prajwal Pathri | Robotics | Editorial Board Member

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Dr. Bhargav Prajwal Pathri | Robotics | Editorial Board Member

Associate Professor | Woxsen University | India

Dr. B. Prajwal is an emerging researcher specializing in swarm intelligence, swarm robotics, and computational optimization, with a growing scholarly footprint reflected in 19 publications, 147 citations, and an h-index of 5. His work focuses on designing scalable, adaptive algorithms—such as particle swarm optimization and rendezvous strategies—to enhance coordination and autonomy in multi-robot systems, exemplified by his 2025 article in the Journal of Field Robotics. With collaborations involving over 45 co-authors, his research bridges artificial intelligence, robotics engineering, and algorithmic design, contributing to interdisciplinary advancements and practical implementations. Dr. Prajwal’s studies support the development of resilient, low-cost autonomous systems with applications in environmental monitoring, disaster response, smart agriculture, and industrial automation. Through a combination of analytical rigor and application-oriented inquiry, his work strengthens global innovation in intelligent robotic systems while addressing societal needs for safer, more efficient, and scalable automation technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Mali, H. S., Prajwal, B., Gupta, D., & Kishan, J. (2018). Abrasive flow finishing of FDM printed parts using a sustainable media. Rapid Prototyping Journal, 24(3), 593–606.

Cited by: 69.

2. Sharma, A., Babbar, A., Tian, B., Prajwal, M., Gupta, R., & Singh, R. (2022). Machining of ceramic materials: A state-of-the-art review. International Journal on Interactive Design and Manufacturing, 16(3).

Cited by: 61.

3. Prakash, C., VK, 3., Mistri, A., Uppal, A. S., Babbar, A., Pathri, B. P., Mago, J., … (2021). Investigation of functionally graded adherents on failure of socket joint of FRP composite tubes. Materials, 14(21), 1–13.

Cited by: 27.

4. Unune, D., Aherwar, A., Pathri, B., & Kishan, J. (2014). Statistical and regression analysis of vibration of carbon steel cutting tool for turning of EN24 steel using design of experiments. International Journal of Recent Advances in Mechanical Engineering, 3(3).

Cited by: 18.

5. Pathri, M. K. D., & Prajwal, B. (2013). Numerical analysis of Kevlar-epoxy composite plate subjected to ballistic impact. International Journal of Mechanical Engineering, 41(1), 1117–1122.

Cited by: 16.

Dr. Prajwal’s work advances the future of autonomous multi-robot systems by integrating intelligent optimization with real-world robotics. His research supports scalable, resilient technologies with applications across industry, environmental management, and societal safety, contributing to global innovation in autonomous systems engineering.

 

Leila Malihi | Knowledge Distillation | Research Excellence Award

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Dr. Leila Malihi | Knowledge Distillation | Research Excellence Award

Research Assistant | Osnabrück University | Germany

Dr. Leila Malihi is an emerging scholar whose work advances the intersection of medical image analysis, digital health technologies, and clinical decision-support systems. With a developing portfolio of 10 scholarly publications, 88 citations and 5 h-index her research demonstrates both growing influence and clear relevance to contemporary healthcare challenges. Her primary focus lies in applying machine learning and computer-vision techniques to improve diagnostic accuracy, particularly in the context of wound analysis and healing-complication detection, including notable contributions to the automatic classification of wound images and the optimisation of algorithms to detect maceration—an area critical for improving patient care, reducing clinical workload, and supporting early intervention. Disseminated through open-access venues, this work reflects a strong commitment to practical, clinically meaningful impact. Malihi’s collaborative record, involving more than 20 co-authors across computer science, biomedical engineering, and clinical research, highlights her active engagement in interdisciplinary teams that blend methodological rigour with clinical insight, enhancing the translational quality of her contributions. Despite being in an early career stage, she has already established measurable academic impact through consistent citation uptake and growing recognition within the health-technology community. Her research carries significant societal value by enabling more accurate and automated assessment of wound healing, supporting the development of scalable healthcare solutions, strengthening telemedicine workflows, and ultimately contributing to improved patient outcomes, particularly in resource-limited environments.

Profile: Scopus

Featured Publication

1. Dührkoop, E., Malihi, L., Erfurt-Berge, C., Heidemann, G., Przysucha, M., Busch, D., & Hübner, U. H. (2024). Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration. In R. Rohrig et al. (Eds.), German Medical Data Sciences 2024.
Cited by: 2

Dr. Malihi’s research advances intelligent medical-image analysis tools that strengthen diagnostic precision and support clinicians in delivering timely, data-driven care. Her vision is to develop globally accessible digital-health solutions that reduce healthcare disparities and promote more efficient, technology-enhanced clinical workflows.

Komil Tashev | Cybersecurity | Editorial Board Member

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Dr. Komil Tashev | Cybersecurity | Editorial Board Member

Vice-Rector | Tashkent University of Information Technologies | Uzbekistan

Dr. Komil Tashev is an emerging researcher specializing in nano-electronics, quantum-dot device architectures, and Internet of Things (IoT) systems, with a strong emphasis on applications in healthcare and advanced communication networks. With 17 publications, 94 citations, 7 h-index and collaborations involving over 40 co-authors, his work reflects growing international recognition and active engagement in interdisciplinary research. His contributions focus on designing scalable, energy-efficient, and secure computational architectures that enhance the performance of next-generation IoT devices, particularly through innovations in quantum-dot multiplexers and nano-communication networks. Tashev’s research bridges theoretical advancements with practical implementations, addressing critical challenges such as system miniaturization, low-power operation, data reliability, and device interoperability—key factors for modern medical monitoring and diagnostic systems. His publications in reputable outlets highlight a commitment to integrating quantum-scale technologies with real-world IoT constraints, thereby advancing the efficiency and intelligence of healthcare infrastructures. Through sustained scholarly output, collaborative work, and a clear focus on technological impact, he contributes to shaping the future of nano-enabled IoT systems and their global applications.

Profile: Scopus

Featured Publication

1. Safoev, N., & Karimov, M. (2025). A nano-scale quantum-dot multiplexer architecture for logic units in Internet-of-Things healthcare systems. Nano Communication Networks.

Dr. Komil Tashev’s research drives progress in quantum-scale nano-architectures for IoT healthcare, enabling more reliable, efficient, and compact medical technologies. His work supports global scientific innovation by advancing nano-communication networks and strengthening the technological foundations of next-generation digital health ecosystems.

Giuseppe Di Gironimo | Remote Handling | Excellence in Research Award

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Prof. Giuseppe Di Gironimo | Remote Handling | Excellence in Research Award

Professor | University of Naples Federico II | Italy

Prof. Giuseppe Di Gironimo is a multidisciplinary researcher specializing in advanced mechanical design, digital engineering, and remote-handling technologies for large-scale scientific and industrial systems, with a strong focus on fusion energy devices such as DTT, ITER, and RFX-Mod2. With over 201 publications, 3,031 citations, and an h-index of 28, he has established a significant international presence in the fields of CAD/CAE modeling, electromechanical system analysis, ICRH/ICRF antenna design, and virtual alignment methodologies. His work integrates cloud-based engineering, digital twins, immersive VR training, and under-actuated robotic systems to improve maintainability, safety, and operational efficiency in high-complexity environments. Collaborating with nearly 1,000 co-authors across global research laboratories, universities, and industry partners, he contributes to multidisciplinary advancements in fusion energy development, aerospace manufacturing, and human–machine interaction. His research demonstrates both academic depth and industrial relevance, supporting innovation in automated assembly, virtual design reviews, and remote operations for next-generation scientific infrastructures.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Grazioso, S., Di Gironimo, G., & Siciliano, B. (2019). A geometrically exact model for soft continuum robots: The finite element deformation space formulation. Soft Robotics, 6(6), 790–811.
Cited by: 253

2. Del Nevo, A., Arena, P., Caruso, G., Chiovaro, P., Di Maio, P. A., Eboli, M., … (2019). Recent progress in developing a feasible and integrated conceptual design of the WCLL BB in EUROfusion project. Fusion Engineering and Design, 146, 1805–1809.
Cited by: 186

3. Singh, G., Akrigg, S., Di Maio, M., Fontana, V., Alitappeh, R. J., Khan, S., Saha, S., … (2022). ROAD: The road event awareness dataset for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 1036–1054.
Cited by: 159

4. Del Nevo, A., Martelli, E., Agostini, P., Arena, P., Bongiovì, G., Caruso, G., … (2017). WCLL breeding blanket design and integration for DEMO 2015: Status and perspectives. Fusion Engineering and Design, 124, 682–686.
Cited by: 155

5. You, J. H., Mazzone, G., Visca, E., Greuner, H., Fursdon, M., Addab, Y., … (2022). Divertor of the European DEMO: Engineering and technologies for power exhaust. Fusion Engineering and Design, 175, 113010.
Cited by: 151

Prof. Giuseppe Di Gironimo work accelerates progress toward reliable fusion energy systems and next-generation digital engineering, strengthening the technological foundations for future clean-energy infrastructures. Through innovations in virtual design, remote handling, and electromechanical systems, he contributes directly to safer, more efficient, and globally scalable industrial and scientific solutions.

Róbert Bata | Scientific Computing | Best Researcher Award

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Mr. Róbert Bata | Scientific Computing | Best Researcher Award

Assistant Lecturer | University of Debrecen | Hungary

Mr. Róbert Bata is an emerging public health researcher whose work focuses on social epidemiology, gender-related health disparities, and the behavioral and structural determinants of population health. With 5 peer-reviewed publications, 6 citations, and an h-index of 2, he has contributed evidence-based insights into how social perceptions, intimate partner violence, and sexual and reproductive behaviors shape health outcomes, particularly among women in low- and middle-income settings. His research integrates quantitative methods with socio-behavioral analysis to examine sensitive but globally significant issues such as sexually transmitted infections and violence-related health vulnerabilities. Collaborating with 16 co-authors across multidisciplinary fields—including public health, sociology, and international development—he works to illuminate the mechanisms through which social inequality influences individual and community health. Bata’s scholarship, exemplified by his recent open-access study on sexual perceptions, behavior, and intimate partner violence among Filipino women, seeks to inform health policy, strengthen gender-responsive interventions, and support equitable, culturally grounded public health strategies. His growing body of work demonstrates a commitment to advancing global understanding of the social determinants of health and contributing to more inclusive, evidence-driven approaches to improving health outcomes in vulnerable populations worldwide.

Profiles: Scopus | ORCID

Featured Publication

1. Bata, R. (2025). Association of sexual perceptions, behavior, and intimate partner violence with sexually transmitted infection (STI) among Filipino women. BMC Public Health, 25, Article .

Mr. Róbert Bata’s research advances global public health by generating evidence that strengthens women’s health, safety, and autonomy, particularly in marginalized communities. His work contributes to policy-relevant knowledge that supports equitable health systems and promotes social resilience through informed, data-driven interventions.

Zongran Dong | Computer Aided Design | Best Researcher Award

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Assoc. Prof. Dr. Zongran Dong | Computer Aided Design | Best Researcher Award

Teacher | Dalian University of Foreign Languages | China

Assoc. Prof. Dr. Zongran Dong, affiliated with Dalian Neusoft University of Information, China, is an expert in computational optimization and heuristic algorithms, with a focus on practical applications in logistics and industrial engineering. His research emphasizes the development and refinement of metaheuristic methods, including tabu search, to address complex operational challenges such as container-loading optimization, demonstrating both algorithmic innovation and real-world applicability. Despite a concise publication record of 3 peer-reviewed works, his research has garnered 58 citations and of 2 h-index, reflecting recognition and engagement within the scholarly community. Collaborating with six co-authors, Dong has contributed to interdisciplinary knowledge exchange that bridges computer science, applied mathematics, and engineering, enhancing the robustness and impact of his work. His contributions hold relevance for industrial practice, supporting efficiency, resource optimization, and sustainability in logistics and supply-chain systems. Overall, Dong’s research embodies a targeted, application-driven approach that advances intelligent optimization methods and demonstrates measurable influence on both academic research and operational industries worldwide.

Profiles: Scopus | ORCID

Featured Publication

1. A tabu search algorithm for container loading based on homogeneous blocks. (2011). Gaojishu Tongxin
Cited by: 1

Assoc. Prof. Dr. Zongran Dong’s research advances intelligent optimization techniques that enhance efficiency in global logistics and industrial operations. By improving algorithmic performance in container-loading and related systems, his work supports more sustainable, cost-effective, and data-driven decision-making across industries.

Saeid Barshandeh | Optimization | Best Researcher Award

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Dr. Saeid Barshandeh | Optimization | Best Researcher Award

Instructor-Researcher | Afagh Higher Education Institute | Iran

Dr. Saeid Barshandeh is a computational intelligence researcher specializing in metaheuristic optimization, machine learning, and advanced algorithm design, with a research portfolio comprising 14 scientific publications, 12 h-index and over 885 citations. His work focuses on developing innovative, nature-inspired optimization techniques capable of addressing complex, nonlinear problems prevalent in engineering, data science, and industrial systems. A key achievement is the development of the Puma Optimizer (PO), a novel metaheuristic algorithm that has rapidly gained global academic attention and demonstrates his expertise in algorithmic modeling, performance tuning, and machine-learning integration. His research activities are enriched by collaborations with more than 30 co-authors, reflecting active engagement in interdisciplinary networks spanning intelligent systems, evolutionary computation, and data-driven decision-making. Collectively, his contributions enhance the efficiency, scalability, and applicability of optimization methodologies across diverse domains such as energy management, automation, predictive analytics, and industrial process optimization. Through impactful publications and growing scholarly influence, Dr. Barshandeh advances the theoretical and practical foundations of intelligent optimization, contributing to technological innovation and the broader scientific community.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., & … (2024). Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing, 27(4), 5235–5283.
Cited by: 661

2. Barshandeh, S., & Haghzadeh, M. (2021). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers, 37(4), 3079–3122.
Cited by: 129

3. Gharehchopogh, F. S., Abdollahzadeh, B., Barshandeh, S., & Arasteh, B. (2023). A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT. Internet of Things, 24, 100952.
Cited by: 128

4. Gharehchopogh, F. S., Nadimi-Shahraki, M. H., Barshandeh, S., & … (2023). Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering, 20(1), 158–183.
Cited by: 89

5. Barshandeh, S., Piri, F., & Sangani, S. R. (2022). HMPA: An innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Engineering with Computers, 38(2), 1581–1625.
Cited by: 78

Dr. Barshandeh’s research advances the science of optimization by providing robust, efficient algorithms that strengthen machine-learning performance and industrial decision systems. His work contributes to global technological innovation, enabling smarter, more adaptive solutions for complex societal and engineering challenges.

Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

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Dr. Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

Senior Researcher | Tarbiat Modares University | Iran

Dr. Davoud Shahgholian-Ghahfarokhi is a researcher affiliated with Tarbiat Modares University whose work spans advanced structural engineering, materials mechanics, and computational modeling, with a focus on improving the performance, durability, and safety of modern engineering systems. With 20 peer-reviewed publications, 15 h-index and over 792 citations, his research demonstrates sustained scholarly influence and recognition within the global engineering community. His expertise encompasses mechanics-based structural design, vibration analysis, composite and sandwich structures, offshore pipeline integrity, auxetic and graded materials, and artificial-intelligence-assisted engineering assessment. Across his body of work, he has contributed analytical, numerical, and hybrid computational frameworks for understanding complex structural behaviors under dynamic and environmental loads. Notable contributions include the formulation of vibration models for sandwich folded plates with auxetic honeycomb cores and FG-GPLRC coatings, advancing next-generation lightweight, high-performance structures. Additionally, his research on corrosion-induced degradation of offshore pipelines—combining code-based methods, finite-element simulations, and neural-network prediction—provides industry-relevant tools for failure assessment and risk mitigation. Dr. Shahgholian-Ghahfarokhi has collaborated with at least 25 co-authors, reflecting a strong record of interdisciplinary and international engagement. His work supports structural reliability, materials innovation, and infrastructure resilience, offering direct societal benefits in safety-critical sectors such as offshore energy, transportation, and advanced manufacturing. Collectively, his research contributes to a more robust scientific understanding of complex structural systems while fostering emerging engineering solutions that balance performance, sustainability, and safety.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Shahgholian-Ghahfarokhi, D., Safarpour, M., & Rahimi, A. (2021). Torsional buckling analyses of functionally graded porous nanocomposite cylindrical shells reinforced with graphene platelets (GPLs). Mechanics Based Design of Structures and Machines, 49(1), 81–102.
Cited by: 119

2. Shahgholian, D., Safarpour, M., Rahimi, A. R., & Alibeigloo, A. (2020). Buckling analyses of functionally graded graphene-reinforced porous cylindrical shell using the Rayleigh–Ritz method. Acta Mechanica, 231(5), 1887–1902.
Cited by: 102

3. Shahgholian-Ghahfarokhi, D., & Rahimi, G. (2018). Buckling load prediction of grid-stiffened composite cylindrical shells using the vibration correlation technique. Composites Science and Technology, 167, 470–481.
Cited by: 81

4. Khodadadi, A., Liaghat, G., Taherzadeh-Fard, A., … (2021). Impact characteristics of soft composites using shear thickening fluid and natural rubber – A review of current status. Composite Structures, 271, Article 114092.
Cited by: 80

5. Ghahfarokhi, D. S., & Rahimi, G. (2018). An analytical approach for global buckling of composite sandwich cylindrical shells with lattice cores. International Journal of Solids and Structures, 146, 69–79.
Cited by: 77

Dr. Shahgholian-Ghahfarokhi’s research advances the scientific foundations and practical tools needed to design safer, lighter, and more resilient engineering structures. By integrating novel materials, computational intelligence, and rigorous mechanics, his work contributes to global innovation in sustainable infrastructure, industrial reliability, and engineering risk reduction.

Vasuk Gautam | Bioinformatics | Best Researcher Award

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Dr. Vasuk Gautam | Bioinformatics | Best Researcher Award

Sr.Scientist | Norton Research Institute | United States

Dr. Vasuk Gautam is an emerging researcher whose work spans metabolomics, biomarker discovery, and computational methods for enhancing analytical confidence in high-throughput biological studies. With a record of 24 peer-reviewed publications, 13 h-index and over 3,102 citations, Gautam has established a strong early-career footprint marked by methodological innovation and extensive interdisciplinary collaboration. His contributions focus particularly on developing frameworks for improving metabolite identification accuracy—an essential challenge in metabolomics that directly influences the reliability of biomedical and environmental research. Notably, his recent work introducing the concept of “Identification Probability” provides a transferable, automated metric for evaluating identification confidence, positioning it as a potentially transformative tool for large-scale metabolomic pipelines. In parallel, Gautam has contributed significantly to the development of MarkerDB 2.0, a comprehensive biomarker database that integrates molecular, clinical, and contextual information to support precision medicine, translational research, and global health initiatives. His scholarship reflects a blend of computational rigor, domain expertise, and a commitment to open-access scientific resources. Gautam’s collaborations include partnerships with over 180 co-authors, underscoring his active engagement with diverse research groups and his ability to contribute meaningfully to multi-institutional projects. This collaborative network spans biochemistry, bioinformatics, systems biology, and clinical sciences, highlighting the broad applicability and relevance of his expertise. Through both his methodological contributions and his involvement in global data-resource efforts, Gautam’s work supports reproducibility, accessibility, and evidence-based discovery in molecular life sciences. His growing citation impact and participation in influential open-access initiatives demonstrate both scientific merit and societal relevance, particularly in areas related to disease diagnostics, personalized healthcare, and data-driven research infrastructures.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Wishart, D. S., Guo, A. C., Oler, E., Wang, F., Anjum, A., Peters, H., Dizon, R., … (2022). HMDB 5.0: The human metabolome database for 2022. Nucleic Acids Research, 50(D1), D622–D631.

Cited by: 2116

2. Knox, C., Wilson, M., Klinger, C. M., Franklin, M., Oler, E., Wilson, A., Pon, A., Cox, J., … (2024). DrugBank 6.0: The DrugBank knowledgebase for 2024. Nucleic Acids Research, 52(D1), D1265–D1275.

Cited by: 1112

3. Wishart, D. S., Han, S., Saha, S., Oler, E., Peters, H., Grant, J. R., Stothard, P., … (2023). PHASTEST: Faster than PHASTER, better than PHAST. Nucleic Acids Research, 51(W1), W443–W450.

Cited by: 394

4. Wishart, D. S., Tian, S., Allen, D., Oler, E., Peters, H., Lui, V. W., Gautam, V., … (2022). BioTransformer 3.0: A web server for accurately predicting metabolic transformation products. Nucleic Acids Research, 50(W1), W115–W123.

Cited by: 160

5. Wang, F., Allen, D., Tian, S., Oler, E., Gautam, V., Greiner, R., Metz, T. O., … (2022). CFM-ID 4.0: A web server for accurate MS-based metabolite identification. Nucleic Acids Research, 50(W1), W165–W174.

Cited by: 115

Dr. Vasuk Gautam’s work advances the reliability and scalability of metabolomic and biomarker research, enabling more accurate diagnostics and deeper biological insight. By developing robust identification metrics and contributing to global data resources, he helps accelerate scientific discovery, support precision medicine, and enhance evidence-based decision-making across research, healthcare, and industry.