A knowledge engineering approach to the recognition of genomic coding regions

ได้ทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.2556-255

Identifying Splice-Junction Sequences by hierarchical multiclassifier

none2Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Recently, several works have approached the splice-junction problem by applying methods from the field of machine learning techniques. In this work, we propose a hierarchicalmulticlassifier (HM) architecture, whose results show a drastically error reduction with respect to the performance of methods proposed in the literature.noneA. LUMINI; L. NANNIA., Lumini; Nanni, Lori

Identifying Splice-Junction Sequences by hierarchical multiclassifier

Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Recently, several works have approached the splice-junction problem by applying methods from the field of machine learning techniques. In this work, we propose a hierarchical multiclassifier (HM) architecture, whose results show a drastically error reduction with respect to the performance of methods proposed in the literature

Identifying Splice-Junction Sequences by hierarchical multiclassifier

Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Recently, several works have approached the splice-junction problem by applying methods from the field of machine learning techniques. In this work, we propose a hierarchical multiclassifier (HM) architecture, whose results show a drastically error reduction with respect to the performance of methods proposed in the literature

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications