CALL FOR PAPERS

We are pleased to announce the Second Workshop on Generative Genomics at ICBCB 2027 on March 26 (Friday), 2027. The Generative Genomics Workshop will be held as a track of the International Conference on Bioinformatics and Computational Biology 2027, which will be held in Xi’an, China, on March 26-29, 2027. The conference will be held primarily offline, but online presentations may also be allowed.

Full papers or extended abstracts may be submitted via the ICBCB 2027 system or by email: 📩 icbcb_contact@163.com. The workshop also welcomes creative proposals for panels, tutorials, and program committee activities. Please contact: 📩 jklee@kaist.ac.kr.

GENERATIVE GENOMICS

Comparative Genomics

Comparative Genomics has traditionally aimed to discover homologous genes and proteins from the perspective of their evolutionary relationships through alignment-based procedures. The origins of orphan genes have been explored from the evolutionary origin perspective under the assumption that they arose either through mutational transformations of duplicated genes or as de novo genes derived from non-coding sequences. The inference of conserved ancestry has also been attempted through the identification of diverged homologs.

Under the assumption of a homologous sequence-function relationship, Protein Language Models were developed to predict the functions of genes based on the association between homologous sequences and annotated functions.

The Mystery of Orphan Genes

However, recent research has shown that these evolutionary origins alone cannot theoretically explain the origins of all orphan genes, and computational experiments have also demonstrated their limitations. Therefore, it has become necessary to conceptualize generative origins that are essential for the emergence of new descendant species. Generative-origin genes require taxon-restricted genes that were neither inherited from ancestors nor inherited by descendants.

The genomic study conducted from the perspective of generative origins is named Generative Genomics, which is essential for complementing the evolutionary origin paradigm in order to explain the emergence of the full diversity of species. The survival conditions inherited from ancestors need to be distinguished from the conditions required for the emergence of new descendant species. We also need to simulate the process to determine whether genetic drift can lead to the emergence of new species-specific orphan genes.

We observed that the functions of orphan genes are barely characterized or annotated at present. It is necessary to study the unique functions of species- and taxon-specific orphan genes, which have received little attention under the evolutionary origin paradigm. Therefore, we need to establish the complementary paradigm of generative origins, which may serve as a foundation for synthetic biology.

Generative Genes

We define Generative Genes as genes that did not originate through evolution but are essential for the generation of species- and taxon-specific functions. To extend the concept of generative origins beyond evolutionary origins, we need to computationally identify the boundary of the evolutionary origins of orphan genes. Therefore, putative generative genes can be computationally discovered by validating that no evolutionary origin is possible. If a generative gene enables a species-specific function that cannot be found elsewhere, it is reasonable to conclude that such a putative generative gene is indeed a genuine generative gene that did not emerge by accident. The probability of false positives from the perspectives of both evolutionary and generative origins needs to be assessed.

The types of generative genes can be classified into two categories: Taxon-Restricted Generative Genes and Founding Generative Genes. Taxon-Restricted Generative Genes (TRGGs) refer to genes that were neither inherited from ancestors nor inherited by descendants. TRGGs need to be studied as Species-specific Generative Genes (SSGGs) and Taxon-specific Generative Genes (TSGGs). In contrast, even though Founding Generative Genes emerged as generative genes, they were subsequently inherited by taxon-specific descendant lineages.

To validate generative genes computationally, orphan genes need to be examined for all possible evolutionary paths to determine whether they could have been evolutionarily inherited, such as through gene duplication and divergence, de novo emergence from non-coding sequences, or conserved ancestry evidenced by diverged homology.

Prediction of the Functions of Generative Genes

Protein Language Models (PLMs) were developed to predict the functions of genes based on the association between gene sequences and annotated functions. However, predicting the functions of taxon-restricted generative genes using conventional PLMs cannot be effectively realized because the association between orphan gene sequences and the functions of unannotated orphan genes cannot be easily established. Therefore, we need to develop a distinctive prediction model by mapping the unique phenotypic differences between very similar species to the unique genetic differences represented by SSGGs. The one-to-many and many-to-one relationships between generative genes and phenotypic traits need to be integrated into AI models for Generative Genomics. RNAi wet experiments will also be necessary to supplement the in silico characterization of unannotated generative genes.

To validate the functions of Founding Generative Genes, we need to examine the traits of orphan genes at their emergence stage. Non-homology should be tested against the genes available at the time of emergence. As founding genes, the subsequent pattern of inheritance should be identified as a network of well-conserved genes. This is a typical case in which generative genes and inherited genes work complementarily.

ILLUSTRATIVE RESEARCH TOPICS OF GENERATIVE GENOMICS

To advance the objectives of Generative Genomics and examine its implications for health science, we suggest to investigate the following research issues, as illustrated by, but not limited to, the five topics below. We believe these topics present valuable opportunities for researchers studying orphan genes and de novo genes.

1) Origins of Orphan Genes

• Standard procedures for identifying orphan genes: Sensitivity of E-values and interspecies comparisons
• Conditions for identifying founder genes
• Conditions for validating the emergence of de novo genes and horizontally transferred genes.
• Methods for detecting diverged homology and inferring conserved ancestry

2) Identification of Generative Genes

• Conceptual distinction between Generative Genomics and Comparative Genomics
• Identification of generative origins through the limits of simulating gene duplication and mutational divergence
• Identification of generative origins through the limits of identifying de novo genes from non-coding sequences
• Identification of generative origins through the limits of diverged homology with evidence of conserved ancestry using HMMER, PSI-BLAST, PLMs, etc.

3) Generative Tree of Life

• Association between Taxon-Restricted Generative Genes and the phylogenetic tree, leading to the construction of the Generative Tree of Life
• Analysis of Species-specific Generative Genes and Taxon-specific Generative Genes within the Generative Tree of Life
• Identification of Founding Generative Genes and their gene network structures
• Association between Taxon-Restricted Generative Genes and Founding Generative Genes in the Generative Tree of Life

4) AI Models for predicting Generative Gene Functions

• Building Protein Language Models and Agentic AI models that predict the functions of homologous genes
• Assessing the performance of PLMs in predicting the functions of generative genes
• Graph-based modeling of the one-to-many and many-to-one sequence-function relationships between generative genes and unique species-specific traits
• Prediction of unique functions by contrasting the trait differences between closely related species and their species-specific generative genes

5) Generative Genomics in Disease Pathway Analysis

• Generative Genomics AI models for molecular pathway analysis of diseases
• Discovery of disease-specific generative genes in humans and other species
• Identification of diseases that originate from the dysfunction of generative genes
• Pathological studies associated with generative genes in diverse organisms

PROGRAM COMMITTEE

• Chair: Jae Kyu Lee (Chair Professor, Xi’an Jiaotong University, China; Professor Emeritus, Korea Advanced Institute of Science and Technology, Korea)
• Co-chair: Ming Chen (Professor and Director of the Bioinformatics Lab, College of Life Sciences, Zhejiang University, China)
• Dae Kyun Chung (Professor and Dean, College of Life Sciences, Kyung Hee University)
• Kyong-Tai Kim (Chair Professor and Director of the Generative Genomics Lab, Handong University; Professor Emeritus, POSTECH)
• Wooju Kim (Professor and Director of the AI Technology Research Center, Yonsei University)
• Ohbyung Kwon (Professor and Former Vice-President of Kyung Hee University)
• Taesung Park (Professor and Director of the Bioinformatics and Biostatistics Lab, Seoul National University)
• Chuck Yoo (Professor of Computer Science and Director of the AI Research Center, Korea University)

Important Dates

📝 Intention to Submit (optional): September 30, 2026
📤 Submission Deadline: October 20, 2026
✅ Notification of Acceptance: November 20, 2026
📄 Camera-Ready Due: December 5, 2026
🧾 Registration Deadline: December 5, 2026
📚 Conference: March 26–29, 2027
🧠 Workshop Days: March 26, 2027

Submission Guidelines

📝 Submit via the Electronic Submission System or the conference email box: icbcb_contact@163.com, and please also notify us by email at jklee@kaist.ac.kr.
Full Papers: 4-5 pages in length, following the ICBCB 2027 paper template. Accepted papers may optionally be published in the ICBCB Proceedings. (DOC, LATEX)
Extended Abstracts: Up to 2 pages in length (presentation only) (Abstract)
Proposals: Panels, tutorials, and session organization are welcome.