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International Conference on Computational Biology and Machine Learning (ICCBML-27)

12th - 13th Jan 2027,Philadelphia, USA

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Call for Paper

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Important Dates


Early Bird Registration

13th Dec 2026

Paper Submission Deadline

18th December 2026

Registration Deadline

28th December 2026

Conference Date

12th - 13th Jan 2027

Conference Updates:

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  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in Philadelphia ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in Philadelphia conference.
  • Peer Review Process:
    The peer review process will begin soon for Philadelphia conference.
  • Networking with Global Experts:
    Join global experts at our conference in Philadelphia.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICCBML bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Computational Biology and Machine Learning, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning in genomics and proteomics
  • Predictive modeling in computational biology
  • Bioinformatics applications of machine learning
  • Data mining techniques for biological data
  • Machine learning for drug discovery
  • Systems biology and machine learning integration
  • Machine learning for personalized medicine
  • Challenges in biological data analysis
  • Machine learning for disease prediction
  • Genetic algorithms in computational biology
  • Machine learning for protein structure prediction
  • Ethics in computational biology research
  • Machine learning for microbiome analysis
  • Data visualization in bioinformatics
  • Machine learning in clinical trials
  • Applications of ML in epidemiology studies
  • Machine learning for agricultural biotechnology
  • Future trends in computational biology
  • Integration of AI in biological research
  • Machine learning for environmental biology

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Submit Your Paper Now Confirm Your Participation Today

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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