Open Science Research Excellence

ICDBM 2021 : International Conference on Developmental Biology and Medicine

Singapore, SG
March 29 - 30, 2021

Conference Code: 21SG03ICDBM

Conference Proceedings

All submitted conference papers will be blind peer reviewed by three competent reviewers. The peer-reviewed conference proceedings are indexed in the Open Science Index, Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, and other index databases. Impact Factor Indicators.

Special Journal Issues

ICDBM 2021 has teamed up with the Special Journal Issue on Developmental Biology and Medicine. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue. The paper selection will be carried out during the peer review process as well as at the conference presentation stage. Submitted papers must not be under consideration by any other journal or publication. The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly. Selected full-text papers will be published online free of charge.

Conference Sponsor and Exhibitor Opportunities

The Conference offers the opportunity to become a conference sponsor or exhibitor. To participate as a sponsor or exhibitor, please download and complete the Conference Sponsorship Request Form.

Important Dates

Abstracts/Full-Text Paper Submission Deadline   February 15, 2021
Notification of Acceptance/Rejection   March 1, 2021
Final Paper (Camera Ready) Submission & Early Bird Registration Deadline   February 28, 2021
Conference Dates   March 29 - 30, 2021

Important Notes

Please ensure your submission meets the conference's strict guidelines for accepting scholarly papers. Downloadable versions of the check list for Full-Text Papers and Abstract Papers.

Please refer to the Paper Submission GUIDE before submitting your paper.

Selected Conference Papers

1) The Expression of Lipoprotein Lipase Gene with Fat Accumulations and Serum Biochemical Levels in Betong (KU Line) and Broiler Chickens
W. Loongyai, N. Saengsawang, W. Danvilai, C. Kridtayopas, P. Sopannarath, C. Bunchasak
2) Modulation of Lipopolysaccharide Induced Interleukin-17F and Cyclooxygenase-2 Gene Expression by Echinacea purpurea in Broiler Chickens
Ali Asghar Saki, Sayed Ali Hosseini Siyar, Abbass Ashoori
3) A Cuckoo Search with Differential Evolution for Clustering Microarray Gene Expression Data
M. Pandi, K. Premalatha
4) Application of KL Divergence for Estimation of Each Metabolic Pathway Genes
Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani
5) Performance Analysis of Genetic Algorithm with kNN and SVM for Feature Selection in Tumor Classification
C. Gunavathi, K. Premalatha
6) Comparative Study on Swarm Intelligence Techniques for Biclustering of Microarray Gene Expression Data
R. Balamurugan, A. M. Natarajan, K. Premalatha
7) Simultaneous Clustering and Feature Selection Method for Gene Expression Data
T. Chandrasekhar, K. Thangavel, E. N. Sathishkumar
8) Clustering Approach to Unveiling Relationships between Gene Regulatory Networks
Hiba Hasan, Khalid Raza
9) A Heat-Inducible Transgene Expression System for Gene Therapy
Masaki Yamaguchi, Akira Ito, Noriaki Okamoto, Yoshinori Kawabe, Masamichi Kamihira
10) An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions
R. Mallika, V. Saravanan
11) A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data
Chintanu K. Sarmah, Sandhya Samarasinghe, Don Kulasiri, Daniel Catchpoole
12) BIDENS: Iterative Density Based Biclustering Algorithm With Application to Gene Expression Analysis
Mohamed A. Mahfouz, M. A. Ismail
13) Reducing SAGE Data Using Genetic Algorithms
Cheng-Hong Yang, Tsung-Mu Shih, Li-Yeh Chuang
14) Differentiation of Gene Expression Profiles Data for Liver and Kidney of Pigs
Khlopova N.S., Glazko V.I., Glazko T.T.
15) Analysis of DNA Microarray Data using Association Rules: A Selective Study
M. Anandhavalli Gauthaman

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