International Conference on CRISPR Data Engineering and Bioinformatics Applications - (ICCRISPRBE-27)


13th - 14th January, 2027 | Lalitpur, Nepal

Multi-format (In-person/Virtual)

Registration Options

Explore conference registration categories designed for every mode of participation.

Important Dates

Pre-registration Deadline

14th December, 2026

Paper Submission Deadline

19th December, 2026

Last Date Of Registration

29th December, 2026

Date Of Conference

13th - 14th January, 2027

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Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 2 SDG 2 — Zero Hunger
SDG 3 SDG 3 — Good Health and Well-being
SDG 4 SDG 4 — Quality Education
SDG 9 SDG 9 — Industry, Innovation and Infrastructure
SDG 12 SDG 12 — Responsible Consumption and Production
SDG 16 SDG 16 — Peace, Justice and Strong Institutions
SDG 17 SDG 17 — Partnerships for the Goals
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All Session Tracks

Track 01
Advancements in CRISPR Data Engineering

This track focuses on the latest methodologies and technologies in CRISPR data engineering. It aims to explore innovative approaches for managing and analyzing CRISPR-related datasets.

Track 02
Bioinformatics Applications in Gene Editing

This session will highlight the integration of bioinformatics in gene editing processes, emphasizing practical applications and case studies. Participants will discuss the role of bioinformatics in enhancing CRISPR technology.

Track 03
Predictive Modeling Techniques in Bioinformatics

This track will delve into various predictive modeling techniques utilized in bioinformatics, including supervised and unsupervised learning approaches. Emphasis will be placed on their application in CRISPR data analysis.

Track 04
Deep Learning Innovations for Genomic Data

This session will explore the application of deep learning algorithms in the analysis of genomic data. Participants will discuss breakthroughs and challenges in leveraging deep learning for CRISPR-related research.

Track 05
Anomaly Detection in CRISPR Data Sets

This track will address the methodologies for detecting anomalies in CRISPR data sets, focusing on the implications for data integrity and reliability. Discussions will include both theoretical and practical aspects of anomaly detection.

Track 06
Feature Extraction Techniques for Bioinformatics

This session will cover advanced feature extraction techniques essential for bioinformatics applications. Participants will share insights on how these techniques enhance the analysis of CRISPR data.

Track 07
Workflow Automation in Bioinformatics

This track will examine the importance of workflow automation in bioinformatics, particularly in the context of CRISPR research. Discussions will focus on tools and frameworks that streamline bioinformatics processes.

Track 08
System Monitoring and Model Evaluation

This session will focus on the strategies for effective system monitoring and model evaluation in bioinformatics applications. Participants will discuss best practices for ensuring the reliability of predictive models in CRISPR research.

Track 09
Industrial IoT and Bioinformatics Integration

This track will explore the intersection of industrial IoT and bioinformatics, particularly in the context of CRISPR applications. Discussions will highlight how IoT technologies can enhance data collection and analysis.

Track 10
Digital Twin Technologies in Molecular Modeling

This session will investigate the use of digital twin technologies in molecular modeling and simulation. Participants will discuss the implications of digital twins for CRISPR research and bioinformatics applications.

Track 11
Genomic Analysis and System Optimization

This track will focus on genomic analysis techniques and their role in optimizing bioinformatics systems. The session aims to foster discussions on innovative strategies for enhancing system performance in CRISPR applications.