E-ISSN: 2319-3433 | P-ISSN: 2349-3720 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access
Research and Reviews : Journal of Computational Biology [2319-3433(e)]Â is a peer-reviewed hybrid open-access journal launched in 2012 focused on the publication of current research work carried out under computational Biology. This journal covers all major fields of applications in Computational Biology.
rrjocb maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
Before genome-wide molecular markers became affordable, plant and animal breeding relied on pedigree-based prediction (P-BLUP), using the expected additive relationship matrix (A) derived from recorded ancestry.
Glioblastoma (GBM) and astrocytoma are aggressive primary brain tumors characterized by significant molecular heterogeneity, complicating effective treatment.
The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate.
Blending predictive data mining with metaheuristic optimization has become essential for tackling tough, real-world problems across all kinds of fields.
In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning.
Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles.
