Genomic sequencing has traditionally been costly, time-consuming, and labor-intensive, hindering advancements in science and medicine. However, major strides have reduced the cost of whole genome sequencing to about $1000. This is mainly applicable to personalized medicine; theoretical genetics development necessitates extensive data collection and analysis, a task well-suited for artificial intelligence (AI). AI can help automate self-correction in science and computing, paralleling genomic sequencing efforts. The significant bottleneck occurs during the assembly phase, where DNA fragments with various sequences align to a reference genome. Faster genome assembly could enhance AI-based sequencing, lower costs, and drive theoretical genetics forward by improving data accuracy in variant calling and phenotypic studies.

Cancer, once uncommon, is now a leading cause of death worldwide. This article looks at the rise in cancer cases since 2000, by continent, and their current status. Advances in genetics and gene therapy are paving the way for earlier detection and treatment, with the potential to eliminate some cancers. Artificial intelligence (AI) is set to change cancer treatment by mapping genetic pathways and providing personalized care. We also examine the causes of cancer in various regions, considering environmental, genetic, and lifestyle factors. Although there are significant challenges ahead, the future of cancer prevention and treatment is hopeful with these advancements.

The demand for high-speed DNA sequencing in science and medicine has grown significantly, leading to advancements in these technologies. Illumina sequencing remains the leading method, using fluorescent labels and flow cells for large-scale sequencing, while nanopore and SOLiD technologies offer alternative approaches. Artificial intelligence has greatly benefited genetic sequencing, enhancing data analysis, speed, and accuracy. With the growing need for storage, human genome data could reach 40 exabytes by 2025, which will require faster data processing. Improved variant calling will enhance disease diagnosis and treatment, making these medical applications more accessible. Overall, enhanced data analysis promises deep insights into the human genome and gene expression.

Artificial Intelligence and agricultural genetics are changing how we breed and maintain crops. With growing global food security issues and a need for sustainable farming, AI tools are essential and inspiring solutions. This article explores how AI-driven methods in agricultural genetics are transforming crop breeding, improving resilience, and promoting sustainability, showcasing the potential of technology in agriculture.

This paper discusses how Artificial Intelligence (AI) can help identify and predict genes linked to pandemics. These "pandemic genes" can make pathogens more likely to cause large outbreaks. By using AI to analyze complex data, such as genetic information, environmental conditions, and population trends, researchers can better foresee pandemics and find genetic markers that increase infectivity. New methods, like using real-time environmental data and CRISPR gene-editing, present fresh opportunities for preventing and managing pandemics. However, there are ethical and practical issues to consider, like fair access to AI tools and ensuring data reliability. The paper emphasizes the importance of international teamwork and ethical guidelines in developing AI systems for predicting pandemics.

Direct-to-consumer (DTC) genetic testing is becoming more popular, allowing people to discover their genetic traits and health risks. These AI-driven tests give insights into ancestry, disease risks, and even personal preferences like coffee choices. However, the growth of AI in DTC genetic testing brings important ethical issues.

The rapid advancement of artificial intelligence (AI) significantly impacts genomics and genetic disease prediction. AI algorithms can process vast data, enhancing prediction accuracy. This paper explores AI integration in genetic models, innovative approaches, and how these technologies utilize available data to improve outcomes. It analyzes current methodologies and potential future innovations in genetic disease prediction.

Glioblastoma (GB) is the most aggressive primary brain tumor, with a median survival of about 15 months despite surgical, radiotherapy, and chemotherapy advancements. This grim prognosis underscores the need for innovative treatments. Recent years have seen a multi-omics approach—integrating genomics, transcriptomics, proteomics, metabolomics, and epigenomics—emerge as a powerful tool in developing targeted GB therapies. This article examines how this comprehensive approach helps unravel GB's complexity and informs personalized treatment strategies.

Artificial intelligence has advanced to a point where it can automate decisions and insights traditionally made by scientists and doctors. Medical diagnosis accuracy has improved significantly; machine learning algorithms analyze data with greater precision, approaching perfect efficiency, while deep learning creates necessary theories from models. Effective cancer detection relies on understanding cancer genetics, which has progressed slowly. AI promises advancements in cancer genetics—linking genes to cancer and treatment options. This breakthrough could yield immense benefits, enhancing our understanding of cancer and marking a significant milestone for AI's impact.

As we move further into the 21st century, the study of heredity continues to amaze with its rapid advancements. One area gaining attention from researchers is epigenetics — the study of how environmental factors and lifestyle choices can affect gene expression without changing the DNA sequence. However, the complexity of epigenetic changes poses significant challenges for scientists. Enter artificial intelligence, a groundbreaking tool that is transforming our understanding and use of epigenetic information. This article explores the intersection of AI and epigenetics, paving the way for a new era of personalized medicine.