Justine Kipruto Kitony | Plant Genomics | Excellence in Research Awards

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Dr. Justine Kipruto Kitony | Plant Genomics | Excellence in Research Awards

Dr. Justine Kipruto Kitony | Salk Institute for Biological Studies | United States

Dr. Justine K. Kitony is a postdoctoral fellow in Plant Genomics and Breeding at the Salk Institute for Biological Studies. With over a decade of experience in plant genomics, bioinformatics, and breeding, he integrates cutting-edge sequencing technologies with field phenotyping and genomic prediction to uncover trait-function relationships in key crops. Him work bridges fundamental science and agricultural application, contributing to climate-resilient breeding strategies and sustainable seed systems. Justine has led and co-authored high-impact publications in top journals such as Nature and Nature Communications, with notable contributions in cannabis, baobab, and sorghum pangenomics. Passionate about collaborative science, he has mentored early-career researchers and coordinated cross-functional research teams across Asia, Africa, and the U.S. he is driven by the goal of enhancing crop performance under environmental stress while preserving biodiversity and advancing food and energy security globally.

Publication Profile: 

Google Scholar

Education:

Dr. Kitony holds a Ph.D. in Agricultural Sciences (Quantitative Genetics and Genomics) from Nagoya University, Japan, where he developed a novel nested association mapping (NAM) population in rice to dissect complex traits. he earned him M.Sc. in Bioinformatics from Fujian Agriculture and Forestry University, China, focusing on transcriptome analysis of rice blast resistance. Him academic foundation in computer science and databases was laid with a B.Sc. in Information Technology from RMIT University, Australia. Across these programs, he has acquired multidisciplinary expertise combining computational biology, statistical genetics, molecular biology, and plant breeding. This diverse educational background uniquely positions him to lead genomics-driven research for sustainable crop improvement. Him international academic journey reflects him adaptability and global research outlook, equipping him with the skills necessary to solve real-world agricultural challenges using cutting-edge tools.

Experience:

Currently a Postdoctoral Fellow at the Salk Institute, Dr. Kitony leads the sorghum pangenome project within the Harnessing Plants Initiative. he integrates ONT and HiFi sequencing with trait mapping, GWAS, and CRISPR target discovery for crop improvement. Previously, at Kenya Agricultural and Livestock Research Organization (KALRO), he designed and managed large-scale field trials for rice and cotton, implemented genotyping pipelines, and supported seed system delivery. he also has industry experience as a systems consultant, managing large-scale databases and automating data workflows. Him experience spans from field phenotyping and molecular biology to cloud-based bioinformatics and genomic prediction. A proven leader, he has mentored students, authored key publications, and collaborated across disciplines and geographies. Him field-to-lab translational research expertise makes him an invaluable asset in advancing data-driven, sustainable breeding solutions.

Awards and Honors:

Dr. Kitony’s contributions have earned him international recognition. he is a JICA Development Studies Fellow and an active member of the Japanese Society of Breeding. he serves as a Topic Coordinator for Frontiers in Plant Science and reviewer for multiple Springer Nature journals, reflecting him scientific leadership and credibility. Him research has received wide acclaim, including recent first-author publications in Nature and Nature Communications. he is frequently invited to contribute to major genomics projects and collaborative research efforts across institutions. Him educational and research fellowships reflect both academic merit and a commitment to global development goals. Through capacity-building roles and farmer-outreach programs, he has further shown a strong drive for science impact beyond academia.

Research Focus:

Dr. Kitony’s research focuses on plant genomics, trait discovery, and sustainable crop improvement. he specializes in GWAS, QTL mapping, pangenomics, transcriptomics, and genomic selection, aiming to uncover the genetic basis of traits related to stress tolerance, yield, and adaptation. he applies high-throughput sequencing (PacBio, ONT, Hi-C) and multi-environment field phenotyping using UAVs, LiDAR, and spectral imaging to support breeding decisions. He work emphasizes integrative multi-omics, applying CRISPR target prioritization and genomic prediction in crops like rice, sorghum, baobab, and cannabis. By connecting computational biology and real-world agriculture, him goal is to develop climate-smart, high-yielding, and biodiversity-supportive seed systems. he also champions open-access data practices, reproducible pipelines, and collaborative research, ensuring him innovations are scalable and impactful across regions, particularly in the Global South.

Publication Top Notes:

  1. Domesticated cannabinoid synthases amid a wild mosaic cannabis pangenome – Nature

  2. Chromosome-level baobab genome illuminates its evolutionary insights – Nature Communications

  3. Nested Association Mapping Population in Crops: Current Status and Future Prospects – J. Crop Sci. Biotech.

  4. Development of an aus-derived Nested Association Mapping (aus-NAM) Population in Rice – Plants

  5. Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites – Scientific Reports

  6. Utilization of genotyping-by-sequencing (GBS) for rice pre-breeding and improvement: A review – Life

  7. Chromosome-level baobab genome illuminates its evolutionary trajectory and environmental adaptation – Nature Communications

  8. Domesticated cannabinoid synthases amid a wild mosaic cannabis pangenome – Nature

  9. Pangenome of US ex-PVP and Wild Sorghum Reveals Structural Variants and Selective Sweeps – bioRxiv

  10. Soil depth determines the microbial communities in Sorghum bicolor fields – Microbiology Spectrum

Conclusion:

Dr. Justine K. Kitony exemplifies the qualities of an outstanding researcher worthy of a Research for Excellence Award. He deep expertise in plant genomics, leadership of high-impact projects, strong publication record, and dedication to mentoring mark him as a leading figure in crop genetics and breeding. While there are areas for growth such as expanding him international and public engagement, these do not detract from him significant scientific contributions. Recognizing Dr. Kitony would not only honor him achievements but also encourage continued innovation in sustainable agriculture, genomic research, and capacity development — fields critical to addressing global challenges related to food security and biodiversity conservation.

Shima Shafiee | Cell Structure Analysis | Best Researcher Award

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Dr. Shima Shafiee | Cell Structure Analysis | Best Researcher Award

Dr. Shima Shafiee, Razi University, Iran

Shima Shafiee is an accomplished Iranian researcher specializing in computer systems architecture and bioinformatics, with a strong focus on machine learning applications in biological data analysis. She recently earned her Ph.D. in Computer Engineering from Razi University, where she focused on predictive modeling of protein-peptide binding interactions. Currently under consideration at the IDEL Lab, Shahid Bahonar University of Kerman, Shima has authored numerous national and international publications. With a rich background in algorithm optimization and artificial intelligence, her research stands at the intersection of computational biology, deep learning, and evolutionary algorithms. Shafiee’s work has contributed to the development of predictive tools in bioinformatics, such as DP-site and SPPPred, and she consistently ranks at the top of her academic cohort. Her ability to integrate traditional computer engineering concepts with advanced biological research makes her a notable candidate for the Best Researcher Award.

Publication Profile: 

Google Scholar

Strengths for the Award:

  1. Strong Academic Foundation
    Dr. Shafiee has a stellar academic record, graduating first in her Ph.D. class at Razi University with a CGPA of 3.77 and a thesis grade of 3.98, under the supervision of respected experts in computer engineering and bioinformatics.

  2. Innovative Interdisciplinary Research
    Her research bridges computer systems architecture, machine learning, and bioinformatics, with notable contributions to protein-peptide binding prediction, a critical domain in drug discovery and computational biology.

  3. High-Impact Publications
    She has published in IEEE/ACM Transactions, Applied Soft Computing, and Methods, reflecting both quality and visibility in international forums. Tools like SPPPred and DP-site demonstrate her practical impact in bioinformatics.

  4. Research Originality and Versatility
    Dr. Shafiee has developed hybrid models combining genetic programming, support vector machines, and deep learning, with practical tools and open-source contributions.

  5. Early Recognition and Outreach
    She has been active in academic dissemination since 2015, with selected papers in national and international conferences, showing early promise and consistency.

  6. Teaching and Mentorship
    Through her roles as a lecturer at multiple institutions, she has contributed to academic growth at the grassroots level.

Areas for Improvement:

  1. International Collaboration & Visibility
    While her publication quality is strong, Dr. Shafiee could expand her global visibility through collaborations with international research labs, EU Horizon, or NIH-funded projects.

  2. Post-Ph.D. Grant Applications
    She could benefit from applying for independent research grants or postdoctoral fellowships to lead projects that could shape the future of AI in biology.

  3. Open-Source Software and Data Availability
    While her models are impactful, increased accessibility via open-source repositories (e.g., GitHub) would boost reproducibility and encourage broader adoption.

  4. Industry Impact Metrics
    More emphasis on industry collaborations, patents, or application of models in clinical/biotech settings would enhance translational impact.

Education:

Shima Shafiee completed her Ph.D. in Computer Engineering (2016–2024) from Razi University, specializing in Computer Systems Architecture. Her dissertation titled “Application of learning-based models in predicting of protein-peptide binding interactions” earned her a thesis grade of 3.98/4.00 and an overall CGPA of 3.77. She worked under the guidance of Dr. Abdolhossein Fathi and Dr. Ghazaleh Taherzadeh, focusing on bioinformatics using deep learning, ensemble learning, and evolutionary algorithms. Prior to her Ph.D., she was ranked third in her Master’s program (2015). Shafiee’s educational background is rooted in computational problem-solving, algorithm development, and cross-disciplinary research involving biological data. Her consistent academic excellence and high-ranking performance culminated in her being recognized as the top Ph.D. student in 2025, a testament to her dedication and scholarly capabilities. Her education blends rigorous theory with innovative applied research, making her exceptionally well-prepared for high-impact contributions in academia and industry.

Experience:

Shima Shafiee’s experience spans both academic and applied computer engineering roles. She began her journey with an internship at Kimia Pardaz Pars Company (2013). Between 2015 and 2016, she served as a lecturer for computer fundamentals at Fajr High School and Al-Zahra Seminary School in Jiroft, where she taught introductory computer science to pre-university students. These experiences highlight her foundational teaching skills and outreach to educational institutions in her community. Her major academic contribution began during her Ph.D., where she collaborated with IDEL Lab and contributed to developing tools like SPPPred and DP-site, combining genetic programming, support vector machines, and deep learning to predict protein-peptide binding regions. Her experience uniquely blends educational outreach, algorithmic development, and publication-driven research in machine learning, optimization, and computational biology, reflecting her versatility and impact across the scientific and academic spectrum.

Awards & Honors:

Shima Shafiee has earned multiple distinctions recognizing her academic and research excellence. In 2015, she was named the third-place student in her Master’s program, demonstrating early academic excellence. Her continuous dedication to research and scholarship led her to be recognized as the first-place student in her Ph.D. program in 2025. One of her papers was selected at the 2nd International Congress of Electrical Engineering, Computer Science, and Information Technology (2015), highlighting the innovation and relevance of her early research in optimization algorithms. Her high publication output, including appearances in top-tier venues like IEEE/ACM Transactions on Computational Biology and Bioinformatics and Applied Soft Computing, reflects a consistent standard of excellence. These honors collectively showcase her as a standout figure in her field, with both academic and applied contributions acknowledged at national and international levels.

Research Focus:

Shima Shafiee’s research lies at the intersection of machine learning, bioinformatics, and computational systems engineering. Her primary focus is the prediction of protein-peptide binding interactions using intelligent algorithms such as genetic programming, ensemble models, and deep learning techniques. She has proposed several innovative hybrid models combining sequence-based and structure-based features to identify critical interaction residues. Her doctoral thesis and publications have led to the development of tools like SPPPred and DP-site, which aid in biological sequence analysis, with applications in drug discovery, protein function prediction, and biomedical engineering. Shafiee also has a strong background in optimization algorithms, especially particle swarm optimization (PSO), applied to computationally intensive problems like bin packing. Her ability to blend theoretical computing with practical biological data analysis makes her contributions valuable to both computational scientists and biologists, and positions her as a leading candidate for research recognition awards in AI and bioinformatics.

Publications Top Notes: 

  • 🧠 SPPPred: sequence-based protein-peptide binding residue prediction using genetic programming and ensemble learning (IEEE/ACM TCBBS, 2022)

  • 🔍 Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms (CSICC, 2021)

  • 🧬 Combination of genetic programming and SVM-based prediction of protein-peptide binding sites (Journal of Computing and Security, 2021)

  • 🧪 Prediction of protein–peptide binding residues using classification algorithms (IEEE Bioengineering Conf, 2020)

  • 🧠 A Review of the Uses of AI in Protein Research (Peptide Science Conf, 2019)

  • 🤖 DP-site: dual deep learning method for protein-peptide interaction site prediction (Methods, 2024)

  • 🧬 Protein-peptide interaction region prediction using generative sampling & ensemble DL (Applied Soft Computing, 2025)

  • 🧠 Comparing classification vs. segmentation predictors in protein-peptide interaction (CSICC, 2025)

  • 🧬 Leveraging structure-based and learning-based predictors in protein-peptide interaction (ICCKE, 2024)

  • 📘 Application of learning-based models in protein-peptide binding (Ph.D. Dissertation, 2024)

Conclusion:

Dr. Shima Shafiee is a highly suitable candidate for the Best Researcher Award based on her academic excellence, interdisciplinary research achievements, and consistent contributions to the fields of artificial intelligence and bioinformatics. Her ability to bridge computer science and biological challenges has resulted in meaningful and applicable solutions. She has displayed originality, depth, and foresight in her work, developing novel methods that align with modern computational biology trends.

Irena Roterman | Protein structure | Best Researcher Award

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Irena Roterman | Protein structure | Best Researcher Award

Prof. Irena Roterman , Jagiellonian University – Medical College , Poland

Irena Roterman-Konieczna is a distinguished biochemist specializing in bioinformatics and protein structure. With a PhD in biochemistry from the Nicolaus Copernicus Medical Academy Krakow, she has held significant academic positions, including Professor of Medical Sciences at Jagiellonian University. Irena is recognized for her innovative contributions, particularly the fuzzy oil drop model, which emphasizes environmental influence on protein folding. She has published extensively, contributing to the understanding of protein dynamics and interactions. As a committed educator, she has guided numerous PhD students and served as the Chief Editor for the journal Bio-Algorithms and Med-Systems. Her work continues to impact the fields of protein folding, membrane proteins, and systems biology.

Publication Profile

Scopus

Strengths for the Award

Irena Roterman-Konieczna’s extensive academic background and innovative contributions to the field of bioinformatics and protein structure make her an exceptional candidate for the Best Researcher Award. Her pioneering work on the fuzzy oil drop model has provided critical insights into the environmental influences on protein folding. With a prolific publication record of 149 articles, she has consistently advanced the understanding of protein dynamics, particularly in membrane proteins and chaperonins. Additionally, her role as Chief Editor of the journal Bio-Algorithms and Med-Systems demonstrates her leadership in the scientific community. Her commitment to mentoring future researchers is evident through her advisory work with 15 PhD students, ensuring the continued growth of the field.

Areas for Improvement

While Irena’s contributions to theoretical models are significant, there may be opportunities to further integrate experimental validation into her research. Collaborating with experimentalists could enhance the practical applications of her models, particularly in understanding real-world protein behavior. Additionally, increasing outreach to interdisciplinary fields could broaden the impact of her research on medicine and biotechnology.

Education

Irena Roterman-Konieczna completed her basic education in theoretical chemistry at Jagiellonian University in 1974. She earned her PhD in biochemistry in 1984, focusing on the structure of the recombinant IgG hinge region at the Nicolaus Copernicus Medical Academy in Krakow. Following her doctoral studies, Irena undertook postdoctoral research at Cornell University from 1987 to 1989 in Harold A. Scheraga’s group, where she analyzed force fields in molecular modeling programs like Amber and Charmm. In 1994, she achieved habilitation in biochemistry at Jagiellonian University’s Faculty of Biotechnology and later attained the title of Professor of Medical Sciences in 2004. This strong educational foundation laid the groundwork for her extensive research and contributions to the field of biochemistry and bioinformatics.

Experience

Irena Roterman-Konieczna has a robust academic and research background spanning several decades. She has held key academic positions at Jagiellonian University, where she is currently a Professor of Medical Sciences. Irena’s postdoctoral research at Cornell University deepened her expertise in molecular modeling and protein interactions. Throughout her career, she has authored numerous publications and books, significantly advancing the understanding of protein folding and structure. As Chief Editor of the journal Bio-Algorithms and Med-Systems from 2005 to 2020, she played a vital role in disseminating research in the field. Additionally, she has supervised 15 PhD students, fostering the next generation of researchers. Irena’s collaborative efforts and advisory roles in various projects highlight her commitment to scientific advancement and education in biochemistry and bioinformatics.

Research Focus

Irena Roterman-Konieczna’s research centers on bioinformatics, particularly in understanding protein structure and dynamics. Her innovative fuzzy oil drop model explores the role of environmental factors in protein folding, proposing that external force fields influence hydrophobic core formation and overall structure. Irena investigates the effects of membrane environments on protein behavior, examining how hydrophobic factors can alter folding dynamics. Her work also delves into chaperonins and their role in facilitating proper protein folding under varying conditions. Additionally, she explores domain-swapping structures and their implications for complex formation in proteins. Irena’s research emphasizes the necessity of simulating external force fields in computational protein folding, integrating both internal and external interactions. Her contributions to systems biology and the development of quantitative models for protein behavior continue to advance the field, making significant impacts in both theoretical and practical applications.

Publications Top Notes

  • Chameleon Sequences─Structural Effects in Proteins Characterized by Hydrophobicity Disorder 🌊
  • Transmembrane proteins—Different anchoring systems ⚓
  • External Force Field for Protein Folding in Chaperonins─Potential Application in In Silico Protein Folding 💻
  • Structural features of Prussian Blue-related iron complex FeT of activity to peroxidate unsaturated fatty acids 🔬
  • Domain swapping: a mathematical model for quantitative assessment of structural effects 📊
  • Editorial: Structure and function of trans-membrane proteins 🧬
  • Model of the external force field for the protein folding process—the role of prefoldin 🌐
  • Role of environmental specificity in CASP results 📈
  • Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone 🔍
  • Secondary structure in polymorphic forms of alpha-synuclein amyloids 🧪

Conclusion

Irena Roterman-Konieczna’s innovative research, leadership in academia, and dedication to mentorship position her as a strong contender for the Best Researcher Award. Her groundbreaking work in bioinformatics not only advances scientific understanding but also lays the groundwork for future discoveries in protein dynamics and interactions. Recognizing her contributions would not only honor her achievements but also inspire ongoing research in the field.