Advanced Artificial Intelligence Techniques and its Applications in Bioinformatics

About the Conferences

Bioinformatics is a major benefactor of the recent advancements in Artificial Intelligence (AI). As an interdisciplinary field of science and technology, bioinformatics aims to develop methods, tools, and software to improve the understanding of biological data. Machine learning, a subfield of AI, has become a powerful tool for many bioinformatics applications. The more advanced machine learning/ deep learning techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Artificial Intelligence techniques such as Convolutional Neural Networks, Recurrent neural networks, and graphical models have been successful in analysing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.

The book is intended to cover various problematic aspects emerging in the field of bioinformatics with the help of advanced Artificial Intelligence Techniques. The book is intended to cover the recent approaches in Artificial Intelligence and machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: how Machine Learning(subset of AI) is being utilized in DNA sequencing, protein classification, and the analysis of gene expressions on DNA microarrays, feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in bio sequences; and much more.

The growing applications of Artificial Intelligence in Bioinformatics cannot be ignored, and it is becoming an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists.

Topics to be covered (Not limited to)

Drug discovery- screening of large number of molecules
Annotation and analysis of large data pools of DNA sequences
Protein structure prediction to understand the biological significance of data
AI and genomes for decisions regarding expression of genes.
Genome editing or gene editing by AI programs
Translational Medical Research using AI
‘Digital Pathology’ using AI and Deep Convolutional Neural Network
Integration of AI and prosthetics in 3-D bioprinting to mimic human development
Epigenomic networks deciphered using AI
Biomarkers to predict clocks in ageing and cancer
AI based Natural Language Processing for generation meaningful information Electronic Health Record (EHR) data
DNA sequences to learn sequential dependencies and analyze non- linear or multistep relationships
Variant calling by AI based algorithms
Phenotype-to-genotype mapping using AI based programs