About

Mission

Conduct cutting-edge methodological research to develop new statistical methods and ML/AI algorithms capable of extracting new insights from large-scale, high-throughput multi-omics data.

Collaborate with scientists and clinicians to facilitate new scientific discoveries that lead to better health and medicine through data science, machine learning and statistical modeling.

Train next generation biostatisticians equipped with advanced data science and ML/AI capabilities as well as scientific mind and vision.

At ENCOR, we strive to:

  1. Facilitate Interdisciplinary Collaboration: Create a dynamic and inclusive environment that encourages collaboration among researchers, scientists, and experts from diverse disciplines such as genomics, proteomics, metabolomics, and bioinformatics. By fostering cross-disciplinary partnerships, we aim to address complex biological questions and challenges.
  2. Advance Computational Methods: Lead the development and optimization of advanced computational methods, algorithms, and tools for the analysis and interpretation of omics data. Through continuous innovation, we aim to enhance the accuracy, efficiency, and scalability of computational approaches applied to genomics, transcriptomics, proteomics, and beyond.
  3. Enable Data Integration and Harmonization: Promote the integration and harmonization of omics data from various sources to generate holistic insights into complex biological systems. By developing standardized protocols and promoting open data sharing, ENCOR aims to create a collaborative ecosystem that accelerates scientific discoveries.
  4. Educate and Train the Next Generation: Provide training programs, workshops, and educational resources to empower the next generation of scientists and researchers with the skills necessary for computational omics research. Our commitment to education extends to supporting diversity and inclusion in STEM fields.
  5. Translate Discoveries to Applications: Bridge the gap between research and real-world applications by actively collaborating with industry partners, healthcare institutions, and other stakeholders. By translating scientific discoveries into practical solutions, ENCOR aims to contribute to advancements in personalized medicine, diagnostics, and therapeutic interventions.
  6. Promote Ethical and Responsible Research: Uphold the highest standards of ethical conduct in research, ensuring the responsible and transparent use of omics data. ENCOR is committed to promoting data privacy, security, and adherence to ethical guidelines in all aspects of computational omics research.

By embracing these principles, ENCOR aspires to be a global leader in computational omics research, driving innovation, and making significant contributions to our understanding of biological systems for the betterment of human health and the environment.

Our Goals:

  1. Integrative Analysis: We aim to develop and apply network-based methods that faithfully represent molecular interactions across different omics layers. By constructing biological networks, we can uncover intricate relationships and identify key players in cellular processes.
  2. Data Mining and Sharing: We emphasize the importance of data mining and sharing. Our research contributes to the development of reliable resources, tools, and approaches that facilitate multi-omics analyses. We promote the adoption of findable, accessible, interoperable, and reusable (FAIR) research practices.
  3. Statistical and Machine Learning: We explore the application of statistical techniques and machine/deep learning algorithms to extract meaningful insights from multi-omics data. Our goal is to enhance predictive modeling and unravel biological mechanisms.
  4. Best Practices and Reproducibility: We advocate for best practices in benchmarking, software engineering, and reproducibility. By sharing knowledge and promoting end-to-end workflow awareness, we empower new researchers in the field of multi-omics.

Impact:

Our research contributes to personalized medicine, disease diagnostics, and therapeutic target discovery. By bridging the gap between computational methods and biological understanding, we strive to make a positive impact on human health.

Join us in unraveling the intricate web of omics data and shaping the future of computational biology!