Alper Bulbul | Bioinformatics | Research Excellence Award

Published on by Academic Achievements

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

Published on by Academic Achievements

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