Artificial Intelligence for RNA Editing and Programmable Transcriptomes With Python (Paperback)

B.D. Gula

ISBN 13: 9798248582464

Editore: Independently Published, 2026

Riguardo questo articolo

Paperback. A boundary crossing guide that fuses machine learning with molecular nuance to reimagine RNA not as a string to be edited, but as a living language whose meaning shifts with isoforms, structure, and time. This work shows how to design interventions that respect biology's grammar while exploiting AI's capacity for inference, uncertainty quantification, and control. You will learn to think in terms of causal pathways rather than proxy metrics, kinetic windows rather than steady states, and composable programs rather than one-off fixes. Every chapter includes full Python code demos that turn concepts into working analyses and design workflows.What makes this book different is its insistence on context. It treats off-targets as distribution shift, epitranscriptomics as active circuitry, and guide RNAs as information carriers in a noisy channel. It shows how to build tenant-aware systems that adapt to different tissues, organisms, and risk budgets, and how to move from single edits to programmatic control with reversibility and temporal containment. The result is a rigorous yet exhilarating toolkit for researchers who want more than recipes. Readers consistently report the same reaction: I didn't realize it could be approached this way, this is profound.Inside you will discover: Isoform-centric objectives that avoid repairing one transcript while breaking anotherStructure-aware design that treats secondary structure as a controllable surfaceTime-sensitive modeling that aligns edits with translation and decay kineticsRisk-aware learning with calibrated uncertainty and principled abstentionFederated and cross-domain transfer that separates invariants from lab artifactsLatent spaces that map edits to functional directions for phenotype-first designComposable transcriptome programs that balance modularity with biological cross talkIdeal for computational biologists, molecular engineers, and data scientists ready to navigate the hidden geometry of RNA with provable intuition. The synthesis of theory, practice, and code will upgrade how you reason, design, and decide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9798248582464

Dati bibliografici

• Titolo: Artificial Intelligence for RNA Editing and Programmable Transcriptomes With Python (Paperback)
• Editore: Independently Published
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798248582464
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

A boundary crossing guide that fuses machine learning with molecular nuance to reimagine RNA not as a string to be edited, but as a living language whose meaning shifts with isoforms, structure, and time. This work shows how to design interventions that respect biology’s grammar while exploiting AI’s capacity for inference, uncertainty quantification, and control. You will learn to think in terms of causal pathways rather than proxy metrics, kinetic windows rather than steady states, and composable programs rather than one-off fixes. Every chapter includes full Python code demos that turn concepts into working analyses and design workflows.

What makes this book different is its insistence on context. It treats off-targets as distribution shift, epitranscriptomics as active circuitry, and guide RNAs as information carriers in a noisy channel. It shows how to build tenant-aware systems that adapt to different tissues, organisms, and risk budgets, and how to move from single edits to programmatic control with reversibility and temporal containment. The result is a rigorous yet exhilarating toolkit for researchers who want more than recipes. Readers consistently report the same reaction: I didn’t realize it could be approached this way, this is profound.

Inside you will discover:

• Isoform-centric objectives that avoid repairing one transcript while breaking another
• Structure-aware design that treats secondary structure as a controllable surface
• Time-sensitive modeling that aligns edits with translation and decay kinetics
• Risk-aware learning with calibrated uncertainty and principled abstention
• Federated and cross-domain transfer that separates invariants from lab artifacts
• Latent spaces that map edits to functional directions for phenotype-first design
• Composable transcriptome programs that balance modularity with biological cross talk

Ideal for computational biologists, molecular engineers, and data scientists ready to navigate the hidden geometry of RNA with provable intuition. The synthesis of theory, practice, and code will upgrade how you reason, design, and decide.