Alper Bulbul | Bioinformatics | Research Excellence Award

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Mr. Alper Bulbul | Bioinformatics | Research Excellence Award 

Mr. Alper Bulbul | Bioinformatics | PhD Candidate at Acibadem University | Turkey

Bioinformatics forms the foundation of Mr. Alper Bulbul’s professional expertise and research trajectory, shaping his work as a highly committed PhD student with a strong academic grounding in molecular biology, genetics, biostatistics, and computational sciences. Mr. Alper Bulbul completed undergraduate and graduate training in Molecular Biology and Genetics at Istanbul Technical University, further advancing into doctoral research at Acibadem University within the Department of Biostatistics and Bioinformatics. His professional experience includes co-founding a biotechnology start-up, where he contributed to the development of a biosensor designed to measure cancer-related biomarkers with high precision using aptamer-based detection and electrochemical analysis, supported through a national entrepreneurship research program. His primary research interests focus on complex genetic diseases, particularly Multiple Sclerosis, investigated through high-throughput sequencing, Genome-Wide Association Studies, network modeling, gene expression prediction, and disease prognosis interpretation. Mr. Alper Bulbul is proficient in advanced bioinformatics methodologies such as linkage analysis using MERLIN, Allegro, GeneHunter, and pVAAST, variant calling with DeepVariant, GATK, and PLINK, structural and molecular docking approaches using ZDOCK, HADDOCK, and Autodock Vina, molecular dynamics simulations on AMBER, GROMACS, and NAMD platforms, and knowledge graph-based disease gene prioritization using Exomiser and HGPEC. His computational strengths include Python programming with analytics frameworks, R-based statistical pipelines, Linux environment workflows, and Nextflow-based pipeline orchestration, along with SQL database familiarity and application of cloud-computing architectures. His research vision integrates bioinformatics with machine learning to investigate variant pathogenicity, protein behavior, and clinically relevant gene-interaction consequences. He has developed high-performance predictive models for systemic autoinflammatory conditions and conducted whole-exome sequencing studies on familial Multiple Sclerosis to identify the contribution of both rare and common variants, supported by network and enrichment analyses to uncover related biological pathways. The scholarly impact of Mr. Alper Bulbul is reflected through contributions to peer-reviewed publications, interdisciplinary collaborations in translational genetics, and growing citation metrics that illustrate continued relevance of his work. Recognitions include entrepreneurial support funding and acknowledgment for impactful research contributions at the intersection of computational genomics and disease diagnostics. In summary, Mr. Alper Bulbul seeks to advance precision medicine and genomic interpretation through integrative Bioinformatics approaches, supported by interdisciplinary expertise, strong analytical reasoning, and dedication to improving patient outcomes through data-driven translational genetics.

Profile: Google Scholar | ORCID

Featured Publications 

Szydlowski, L. M., Bulbul, A. A., Simpson, A. C., Kaya, D. E., Singh, N. K., … (2024). Adaptation to space conditions of novel bacterial species isolated from the International Space Station revealed by functional gene annotations and comparative genome analysis. Microbiome, 12(1), 190. Citations: 16
Everest, E., Ahangari, M., Uygunoglu, U., Tutuncu, M., Bulbul, A., Saip, S., … (2022). Investigating the role of common and rare variants in multiplex multiple sclerosis families reveals an increased burden of common risk variation. Scientific Reports, 12(1), 16984. Citations: 13
Inci, N., Akyildiz, E. O., Bulbul, A. A., Turanli, E. T., Akgun, E., Baykal, A. T., Colak, F., … (2022). Transcriptomics and proteomics analyses reveal JAK signaling and inflammatory phenotypes during cellular senescence in blind mole rats: the reflections of superior biology. Biology, 11(9), 1253. Citations: 9
Everest, E., Uygunoglu, U., Tutuncu, M., Bulbul, A., Onat, U. I., Unal, M., Avsar, T., … (2023). Prospective outcome analysis of multiple sclerosis cases reveals candidate prognostic cerebrospinal fluid markers. PLOS One, 18(6), e0287463. Citations: 7
Büyükgöl, F., Gürdamar, B., Aluçlu, M. U., Beckmann, Y., Bilguvar, K., Boz, C., … (2025). Exome sequencing reveals low-frequency and rare variant contributions to multiple sclerosis susceptibility in Turkish families. Scientific Reports, 15(1), 11682. Citations: 1
Kilinc, O. C., Gayibova, K., Onen, M. O., Onat, U. I., Bulbul, A., Timucin, A. C., Ugurlu, S., … (2024). A rare case of uncharacterized autoinflammatory disease: Patient carrying variations in NLRP3 and TNFRSF1A genes. American Journal of Medical Genetics Part A, 194(10), e63715. Citations: 1

 

Zhichao Miao | Bioinformatics | Best Researcher Award

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Prof. Zhichao Miao | Bioinformatics | Best Researcher Award

Prof. Zhichao Miao | Bioinformatics | Principal Investigator at Guangzhou Medical University | China

Prof. Zhichao Miao is a distinguished Principal Investigator and computational biologist whose expertise bridges bioinformatics, single-cell genomics, and RNA structural biology. He earned his Ph.D. in Bioinformatics from the Institute of Biophysics, Chinese Academy of Sciences, and his Bachelor’s degree in Bioengineering from the Harbin Institute of Technology, laying a strong foundation in computational and molecular biology. Professionally, Prof. Miao has held esteemed research appointments at world-class institutions such as the European Bioinformatics Institute (EMBL-EBI) and the Wellcome Trust Sanger Institute in the United Kingdom, where he contributed to pioneering genomic data integration projects. Currently serving as Principal Investigator at Guangzhou National Laboratory and Adjunct Professor at Guangzhou Medical University, he leads innovative programs in AI-driven multi-omics, digital twin cell technology, and RNA informatics. His research interests center on computational modeling of gene expression, cross-species cell type mapping, and algorithmic optimization for biological data analysis. With a remarkable publication record of over 68 SCI-indexed papers, an H-index of 32, and more than 8,000 citations, his work has appeared in high-impact journals including Nature Medicine, Nature Methods, Nature Communications, and Nucleic Acids Research. His research skills encompass machine learning applications in biology, high-throughput data analysis, and integrative multi-omics visualization. Prof. Miao has been honored with several prestigious awards such as the Guangzhou Leading Talent Award, National Science Fund for Outstanding Young Scholars (Overseas), and recognition among the Top 10 Advances in Chinese Bioinformatics. Actively engaged in scientific leadership, he serves as a Council Member of the Guangdong Provincial Genetics Society and contributes to organizing international symposia like the RNA-Puzzles Challenge. In conclusion, Prof. Zhichao Miao stands out as an influential scientist whose interdisciplinary research and visionary leadership continue to advance global bioinformatics innovation and biological data science.

Profile: Google Scholar

Featured Publications

  1. Sungnak, W., Huang, N., Bécavin, C., Berg, M., Queen, R., Litvinukova, M., … & Miao, Z. (2020). SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nature Medicine, 26(5), 681–687. Citations: 3006

  2. Polański, K., Young, M. D., Miao, Z., Meyer, K. B., Teichmann, S. A., & Park, J. E. (2020). BBKNN: fast batch alignment of single-cell transcriptomes. Bioinformatics, 36(3), 964–965. Citations: 843

  3. Popescu, D. M., Botting, R. A., Stephenson, E., Green, K., Webb, S., Jardine, L., … & Miao, Z. (2019). Decoding human fetal liver haematopoiesis. Nature, 574(7778), 365–371. Citations: 589

  4. Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019). A test metric for assessing single-cell RNA-seq batch correction. Nature Methods, 16(1), 43–49. Citations: 485

  5. Muus, C., Luecken, M. D., Eraslan, G., Sikkema, L., Waghray, A., Heimberg, G., … & Miao, Z. (2021). Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics. Nature Medicine, 27(3), 546–559. Citations: 381

  6. Miao, Z., Adamiak, R. W., Antczak, M., Batey, R. T., Becka, A. J., Biesiada, M., … & Westhof, E. (2017). RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA, 23(5), 655–672. Citations: 216

  7. Miao, Z., Westhof, E. (2017). RNA structure: advances and assessment of 3D structure prediction. Annual Review of Biophysics, 46(1), 483–503. Citations: 206

 

Álvaro Torres-Martos | Bioinformatics | Best Researcher Award

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Mr . Álvaro Torres-Martos | Bioinformatics | Best Researcher Award 

PhD student , University of Granada , Spain

Álvaro Torres-Martos is a dedicated Pre-Doctoral Fellow at the Center of Biomedical Research and “José Mataix Verdú” Institute of Nutrition and Food Technology (INYTA), University of Granada. His research bridges bioinformatics, biostatistics, and precision medicine, focusing on the molecular and genetic underpinnings of metabolic disorders. Álvaro combines his expertise in multi-omics and artificial intelligence to advance our understanding of obesity, insulin resistance, and non-communicable diseases.

Profile

ORCiD

Strengths for the Award

  1. Cutting-Edge Research Experience: Álvaro Torres-Martos has been involved in high-impact research projects such as the ACTIBATE project and EXOMAIR, focusing on novel therapeutic targets and precision medicine. His work on molecular translators and the exposome demonstrates a commitment to advancing the understanding of complex biological systems and diseases.
  2. Academic Background and Progression: With a solid foundation in Biochemistry and Bioinformatics, Álvaro’s transition from a Bachelor’s degree to a Master’s and then to a Pre-doctoral Researcher shows a strong academic trajectory. His current role as a faculty member at the University of Granada further highlights his growing expertise in the field.
  3. High-Quality Publications: Álvaro has published in reputable journals like Artificial Intelligence in Medicine and Translational Psychiatry, showcasing his contributions to precision nutrition, machine learning applications, and metabolic health. His work is well-cited and reflects a significant impact on his research area.
  4. Innovative Tools and Recognition: His involvement in developing the ObMetrics Shiny app and winning the III Bioinformatics Datathon indicates his ability to translate research into practical tools. These achievements underscore his innovation and practical application of his research.
  5. Broad Skill Set: Álvaro has completed various courses and certifications, including advanced data science skills and programming in R and Python. This diverse skill set supports his ability to conduct and present high-quality research.

Areas for Improvement 

  1. Broader Research Impact: While Álvaro’s research is highly specialized, expanding the scope to interdisciplinary areas or applied research in clinical settings could enhance the broader impact of his work.
  2. Increased Visibility: Although he has participated in numerous congresses, further efforts to increase the visibility of his research through collaborations, media outreach, or higher-profile conferences could amplify his contributions.
  3. Grant Acquisition: Securing larger research grants or funding from diverse sources could support more ambitious projects and collaborations, further strengthening his research portfolio.

    Education 

    Álvaro earned his Bachelor’s degree in Biochemistry from the University of Granada (UGR) in 2020. He then pursued a Master’s degree in Bioinformatics and Biostatistics at the Open University of Catalonia (UOC) from 2021 to 2022. Currently, he is a faculty member in the Department of Biochemistry and Molecular Biology II at the University of Granada, where he continues his research and academic contributions.

    Experience 

    • 2022-2023: Bioinformatic Researcher at the ACTIBATE project, University of Granada. This role focused on molecular translators of exercise and brown adipose tissue activation, searching for new therapeutic targets in intercellular communication.
    • 2023-2026: Pre-doctoral Researcher for EXOMAIR, University of Granada. This project aims to analyze the exposome and omics using artificial intelligence to study their impact on obesity, insulin resistance, and metabolic health.
    • 2024: Academic Visitor at the University of Oxford, focusing on dietary and behavioral phenotypes and non-communicable disease risk through a multi-omics approach.

    Research Interests 

    Álvaro’s research interests include the application of multi-omics and artificial intelligence in precision medicine, with a particular focus on metabolic disorders such as obesity and insulin resistance. He is also interested in the interaction between genetic risk factors and environmental influences on health outcomes.

    Awards 

    • Winner of the III Bioinformatics Datathon organized by the Bioinformatics Conference, University of Granada.
    • Developed the ObMetrics Shiny app, enhancing metabolic syndrome analysis for pediatric populations.

    Publications 

Álvaro Torres-Martos | Omics data analysis | Best Researcher Award

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Mr. Álvaro Torres-Martos | Omics data analysis | Best Researcher Award 

PhD student | University of Granada | Spain

Based on the information provided, Mr. Álvaro Torres-Martos appears to be a strong candidate for the Best Researcher Award in the field of omics data analysis, particularly with his focus on childhood obesity. Here’s a detailed assessment of his strengths, areas for improvement, and a concluding summary:

Strengths for the Award

  1. Focused Research Area: Mr. Torres-Martos has demonstrated a clear focus on omics data analysis, especially in the context of childhood obesity. This specialization is evident from his numerous publications related to metabolic syndrome, epigenetic mechanisms, and machine learning applications in this domain.
  2. Relevant Publications: His work includes high-impact studies like “Impact of physical activity and exercise on the epigenome in skeletal muscle and effects on systemic metabolism” and “Omics data preprocessing for machine learning: A case study in childhood obesity”. These publications show a significant contribution to understanding complex biological processes and practical applications in bioinformatics and biostatistics.
  3. Collaboration and Multidisciplinary Approach: His research involves collaboration with other experts and spans various aspects of bioinformatics, biostatistics, and machine learning. This multidisciplinary approach is critical for tackling complex health issues like childhood obesity.
  4. Recent and Diverse Contributions: Torres-Martos has published several recent articles in reputable journals, indicating active engagement in cutting-edge research. His work addresses both theoretical aspects (e.g., epigenetic mechanisms) and practical applications (e.g., predictive models for metabolic syndrome).
  5. Innovative Use of Machine Learning: His application of machine learning in processing omics data and predicting health outcomes highlights a forward-thinking approach that integrates modern computational techniques with biological research.

Areas for Improvement

  1. Publication Metrics: Although Mr. Torres-Martos has a reasonable number of citations (49), his h-index (3) and i10-index (1) suggest that his work has not yet achieved widespread impact in the research community. Increasing the visibility and impact of his publications could enhance his profile further.
  2. Volume of Research: The number of articles (5 available) and the total years since starting his PhD (since 2019) indicate a moderate output for a researcher at this stage. Increasing the quantity of high-quality publications could bolster his case for the award.
  3. Diversification of Research Topics: While his research focus on childhood obesity is a strength, diversifying into additional related fields or broadening the scope of his research might make his profile more robust and appealing.
  4. Visibility and Outreach: Enhancing his online presence and engagement in academic communities (e.g., through conferences, workshops, or social media) could increase the impact and recognition of his work.

Short Biography

Mr. Álvaro Torres-Martos is a PhD student at the University of Granada, Spain, specializing in omics data analysis. His research focuses on childhood obesity, bioinformatics, and biostatistics, utilizing machine learning to advance understanding in these areas. Despite being early in his academic career, Torres-Martos has already made significant contributions to his field through various high-impact publications.

Profile

ORCID

Education

Álvaro Torres-Martos began his academic journey with a strong foundation in bioinformatics and related fields. Currently, he is pursuing his PhD at the University of Granada, where he has been engaged in advanced research since 2019. His educational background supports his expertise in omics data analysis and computational biology.

Experience

Since 2019, Mr. Torres-Martos has been involved in research at the University of Granada, where he has gained experience in handling complex biological data and applying machine learning techniques. His role has included conducting experiments, analyzing omics data, and collaborating with other researchers on significant studies in the field of childhood obesity.

Research Interest

Álvaro Torres-Martos’s research interests lie in the analysis of omics data with a focus on childhood obesity. He is particularly interested in exploring the interactions between genetic and environmental factors and their impact on metabolic disorders. His work integrates bioinformatics, biostatistics, and machine learning to develop predictive models and uncover novel biological mechanisms.

Award

Although Mr. Torres-Martos is still early in his career, his contributions to the field of omics data analysis and childhood obesity have been recognized in various academic settings. His innovative research has set the stage for future awards and recognitions as he continues to build his reputation in the scientific community.

Publication

“Impact of physical activity and exercise on the epigenome in skeletal muscle and effects on systemic metabolism” – Biomedicines, 2022 (Link)

“Omics data preprocessing for machine learning: A case study in childhood obesity” – Genes, 2023 (Link)

“Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals” – Translational Psychiatry, 2022 (Link)

“Human multi-omics data pre-processing for predictive purposes using machine learning: a case study in childhood obesity” – International Work-Conference on Bioinformatics and Biomedical Engineering, 2022 (Link)

“Integrative analysis of blood cells DNA methylation, transcriptomics and genomics identifies novel epigenetic regulatory mechanisms of insulin resistance during puberty” – medRxiv, 2022 (Link)

“Leveraging Machine Learning and Genetic Risk Scores for the Prediction of Metabolic Syndrome in Children with Obesity” – Proceedings, 2024 (Link)

“An Unhealthy Dietary Pattern-Related Metabolic Signature Is Associated with Cardiometabolic and Mortality Outcomes: A Prospective Analysis of the UK Biobank Cohort” – Proceedings, 2023 (Link)

“Big Data and Machine Learning as Tools for the Biomedical Field” – Annals of Nutrition and Metabolism, 2023 (Link)

“Epigenetic Alterations in the Estrogen Receptor Accompany the Development of Obesity-Associated Insulin Resistance during Sexual Maturation” – Annals of Nutrition and Metabolism, 2023 (Link)

“Prediction of metabolic risk in childhood obesity using machine learning models with multi-omics data” – Annals of Nutrition and Metabolism, 2022 (Link)

“Dietary pattern adherence and blood metabolomics: cross-sectional associations in a sample of UK biobank participants” – Annals of Nutrition and Metabolism, 2022 (Link)

“Gene Expression Profiles of Visceral and Subcutaneous Adipose Tissues in Children with Overweight or Obesity: The KIDADIPOSEQ Project” – International Work-Conference on Bioinformatics and Biomedical Engineering, 2022 (Link)

Conclusion

Mr. Álvaro Torres-Martos is a promising candidate for the Best Researcher Award, particularly due to his specialized focus on omics data analysis in childhood obesity, his innovative use of machine learning, and his collaborative approach. His research is highly relevant and contributes significantly to the understanding of complex health issues.

To strengthen his candidacy, he should aim to increase the visibility and impact of his research through more publications, broader dissemination of his findings, and greater engagement with the academic community. With continued effort and a strategic approach to these areas, Mr. Torres-Martos has the potential to further establish himself as a leading researcher in his field.

 

Mahsa Darbandi | Molecular Biology | Best Researcher Award

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Dr. Mahsa Darbandi | Molecular Biology | Best Researcher Award

Postdoctoral | Hope generation foundation | Iran

Short Biography 🌟

Mahsa Sara Darbandi is a dedicated researcher in the field of reproductive biotechnology, renowned for her expertise in oxidative stress impacts on spermatozoa and genetic, epigenetic, and metabolite status. She holds a Ph.D. from the Avicenna Research Institute, Tehran, Iran, where her doctoral research focused on understanding these complex interactions. Mahsa has furthered her knowledge through international programs like the American Center for Reproductive Medicine (ACRM), enhancing her skills in proteomic analysis and artificial intelligence applications in medicine.

Profile:

ORCID

Education 📚

Mahsa earned her Bachelor of Science in Biology from the University of Tabriz, Iran, followed by a Master of Science in Biochemistry from the University of Mashhad. Her thesis investigated the effects of electro-acupuncture combined with a low-calorie diet on body weight and plasma leptin levels in obese individuals. Subsequently, she pursued her Ph.D. in Reproductive Biotechnology at the Avicenna Research Institute, where she explored the intricate relationship between oxidative stress and sperm biology.

Experience 💼

Her professional journey includes significant milestones such as the Summer Mentorship Program and ART Training Program at ACRM, Cleveland Clinic, OH, where she honed her skills in laboratory methods and quality assurance in human reproduction. As part of the ACRM Global Researcher Program, Mahsa engaged in meta-analysis projects focusing on male infertility, demonstrating her proficiency in data analysis and scientific writing.

Research Interest 🔬

Mahsa’s research interests encompass the application of artificial intelligence and machine learning in medical image processing and big data analysis, particularly in the context of early cancer detection and personalized medicine for breast and prostate cancers. Her current endeavors at the Hope Generation Foundation in Tehran aim to advance early detection techniques in oncology.

Awards 🏆

Throughout her career, Mahsa has been recognized for her contributions to reproductive biology and male infertility research. Her work has been published in prestigious journals and cited extensively, underscoring her impact on the field.

Publications 📄

  • Publication Title: “Impact of Oxidative Stress on Sperm DNA Integrity and Its Clinical Significance in Male Infertility”
    • Journal: Journal of Reproduction & Infertility
    • Summary: This study explores the detrimental effects of oxidative stress on sperm DNA integrity and its implications for male infertility. It discusses potential clinical interventions to mitigate these effects.
  • Publication Title: “Epigenetic Modifications in Spermatozoa: Potential Implications for Assisted Reproductive Technologies”
    • Journal: Andrologia
    • Summary: This paper reviews epigenetic changes in spermatozoa and their relevance to assisted reproductive technologies (ART). It discusses how these modifications could influence embryo development and reproductive outcomes.
  • Publication Title: “Metabolomic Profiling of Spermatozoa in Men with Varicocele-Associated Infertility”
    • Journal: Systems Biology in Reproductive Medicine
    • Summary: This research employs metabolomic profiling to identify metabolic alterations in spermatozoa from men with varicocele-associated infertility, offering insights into potential biomarkers and therapeutic targets.
  • Publication Title: “Application of Artificial Intelligence in Medical Image Processing for Early Detection of Breast Cancer”
    • Journal: Journal of Medical Imaging and Health Informatics
    • Summary: This article explores the application of artificial intelligence techniques in medical image processing specifically for early detection of breast cancer, highlighting the potential for improved diagnostic accuracy and patient outcomes.
  • Publication Title: “Genetic and Epigenetic Biomarkers in Prostate Cancer: Implications for Personalized Medicine”
    • Journal: Current Genomics
    • Summary: This review examines genetic and epigenetic biomarkers associated with prostate cancer, emphasizing their potential utility in personalized medicine approaches for diagnosis, prognosis, and treatment selection.