Computational Bioengineering Library (16 risultati)

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. The error in modern longevity research is not biological. It is architectural.For decades, we have treated the genome as a static library of instructions, hoping to edit typos with clumsy chemical scissors. This text proposes a radical shift: treating the cellular environment as a stochastic weight matrix and the aging process as a high-dimensional alignment problem. We stop asking how to repair the damage and start asking how to rewrite the generative function that allows damage to exist.This volume is not an introduction to bioinformatics. It is a technical manifesto for the computational reconstruction of biological time. We dismantle the spliceosome using the logic of Transformers, mapping the "junk DNA" of silent exons not as noise, but as the hyperparameter constraints that regulate organismal stability. We explore how Generative Adversarial Networks (GANs) effectively model the adversarial mutations of cancer, and how reinforcement learning agents can navigate the latent space of epigenetic drift to locate the precise mathematical coordinates of youth.From minimizing the thermodynamic entropy of CRISPR off-targeting to deploying self-supervised algorithms that hallucinate novel, non-evolutionary protein isoforms, this work bridges the gap between silicon logic and carbon substrate.Crucially, this is not merely theoretical. Every concept is paired with a rigorous, executable Python implementation. You will not just read about the manifold geometry of alternative splicing; you will code the models that traverse it. You will build the attention heads that mimic synthetic promoters. You will run the gradients on the fitness landscape of cellular senescence.This is a guide for those who realize that biology is simply information processing by other means, and that the signal for longevity is waiting to be decoded. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. 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 multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. 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.

Paperback. Condizione: new. Paperback. The error in modern longevity research is not biological. It is architectural.For decades, we have treated the genome as a static library of instructions, hoping to edit typos with clumsy chemical scissors. This text proposes a radical shift: treating the cellular environment as a stochastic weight matrix and the aging process as a high-dimensional alignment problem. We stop asking how to repair the damage and start asking how to rewrite the generative function that allows damage to exist.This volume is not an introduction to bioinformatics. It is a technical manifesto for the computational reconstruction of biological time. We dismantle the spliceosome using the logic of Transformers, mapping the "junk DNA" of silent exons not as noise, but as the hyperparameter constraints that regulate organismal stability. We explore how Generative Adversarial Networks (GANs) effectively model the adversarial mutations of cancer, and how reinforcement learning agents can navigate the latent space of epigenetic drift to locate the precise mathematical coordinates of youth.From minimizing the thermodynamic entropy of CRISPR off-targeting to deploying self-supervised algorithms that hallucinate novel, non-evolutionary protein isoforms, this work bridges the gap between silicon logic and carbon substrate.Crucially, this is not merely theoretical. Every concept is paired with a rigorous, executable Python implementation. You will not just read about the manifold geometry of alternative splicing; you will code the models that traverse it. You will build the attention heads that mimic synthetic promoters. You will run the gradients on the fitness landscape of cellular senescence.This is a guide for those who realize that biology is simply information processing by other means, and that the signal for longevity is waiting to be decoded. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.