Gene Editing e-Machine Learning: The International and EU Legal Framework

To propel change forward we need not just a good sense of direction but also a sense of the prize, for patients and the health system, if we are successful. A wide range of new technologies, from technologies now coming into our hands, from gene editing to machine learning, have the potential to empower precision medicine to overcome some of mankind’s most intractable challenges: cancer, inherited diseases, aging, dementia—among many others. Taken together, the changes we propose to the innovation process could bring at least an order of magnitude greater net patient benefit over the lifetime of products, as a result of faster development, better targeting, more consistent reimbursement, swifter adoption, and better utilization.

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Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing

Briefings in Bioinformatics ◽

10.1093/bib/bbz145 ◽

2020 ◽

Cited By ~ 1

Author(s):

Aidan R O’Brien ◽

Gaetan Burgio ◽

Denis C Bauer

Keyword(s):

Machine Learning ◽

Gene Editing ◽

Genome Engineering ◽

Target Site ◽

Data Interoperability ◽

Domain Specific ◽

Accuracy Measures

Abstract The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly.

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CRISPR-Cas9 and the Promise of a Better Future

European Journal of Health Law ◽

10.1163/15718093-12264438 ◽

2019 ◽

Vol 26 (4) ◽

pp. 308-329

Author(s):

Vera Lúcia Raposo

Keyword(s):

Precautionary Principle ◽

Gene Editing ◽

Genetic Manipulation ◽

Low Cost ◽

Legal Framework ◽

The European Union ◽

Council Of Europe ◽

Ethical Concerns ◽

The Precautionary Principle ◽

Scientific Accuracy

Abstract Due to its simplicity, low cost and accuracy, CRISPR-Cas9 has become a promising new technique in the field of gene editing. However, despite its virtues, it is not yet immune to scientific hazards and ethical legal concerns. These concerns have been used to justify opposition to genetic manipulation, and have led to some regulations to ban or impose a moratorium based on the precautionary principle. In Europe, regulation mostly comes from the European Union and the Council of Europe, both very cautious towards gene editing. In this article, two arguments on the future legal framework of CRISPR-Cas9 are made. The first is that continued research will contribute to more scientific accuracy; thus, the precautionary principle should promote regulated research to achieve this aim. The second is that most of the legal and ethical concerns surrounding CRISPR-Cas9 are based on unfounded prejudice emanating from a mystical understanding of the human genome.

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Tuning EU Equality Law to Algorithmic Discrimination: Three Pathways to Resilience

Maastricht Journal of European and Comparative Law ◽

10.1177/1023263x20982173 ◽

2021 ◽

pp. 1023263X2098217

Author(s):

Raphaële Xenidis

Keyword(s):

Machine Learning ◽

Predictive Analytics ◽

Legal Framework ◽

Fundamental Rights ◽

Eu Law ◽

Legal Principle ◽

Increased Risk ◽

Effective Remedy ◽

Systemic Discrimination

Algorithmic discrimination poses an increased risk to the legal principle of equality. Scholarly accounts of this challenge are emerging in the context of EU equality law, but the question of the resilience of the legal framework has not yet been addressed in depth. Exploring three central incompatibilities between the conceptual map of EU equality law and algorithmic discrimination, this article investigates how purposively revisiting selected conceptual and doctrinal tenets of EU non-discrimination law offers pathways towards enhancing its effectiveness and resilience. First, I argue that predictive analytics are likely to give rise to intersectional forms of discrimination, which challenge the unidimensional understanding of discrimination prevalent in EU law. Second, I show how proxy discrimination in the context of machine learning questions the grammar of EU non-discrimination law. Finally, I address the risk that new patterns of systemic discrimination emerge in the algorithmic society. Throughout the article, I show that looking at the margins of the conceptual and doctrinal map of EU equality law offers several pathways to tackling algorithmic discrimination. This exercise is particularly important with a view to securing a technology-neutral legal framework robust enough to provide an effective remedy to algorithmic threats to fundamental rights.

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CRISPR/ Cas9 Off-Targets: Computational Analysis of Causes, Prediction, Detection and Overcoming Strategies

Current Bioinformatics ◽

10.2174/1574893616666210708150439 ◽

2021 ◽

Vol 16 ◽

Author(s):

Roshan Kumar Roy ◽

Ipsita Debashree ◽

Sonal Srivastava ◽

Narayan Rishi ◽

Ashish Srivastava

Keyword(s):

Machine Learning ◽

Gene Therapy ◽

Genome Editing ◽

Gene Editing ◽

Computational Analysis ◽

Regulatory Mechanisms ◽

Learning Approaches ◽

Cellular Mechanisms ◽

Wide Range ◽

Target Activity

: CRISPR/Cas9 technology is a highly flexible RNA-guided endonuclease (RGEN) based gene-editing tool that has transformed the field of genomics, gene therapy, and genome/epigenome imaging. Its wide range of applications provides immense scope for understanding as well as manipulating genetic/epigenetic elements. However, the RGEN is prone to off-target mutagenesis that leads to deleterious effects. This review details the molecular and cellular mechanisms underlying the off-target activity, various available detection and prediction methodology ranging from sequencing to machine learning approaches, and the strategies to overcome/minimise off-targets. A coherent and concise method increasing target precision would prove indispensable to concrete manipulation and interpretation of genome editing results that can revolutionise therapeutics, including clarity in genome regulatory mechanisms during development.

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Machine learning predicts new anti-CRISPR proteins

Nucleic Acids Research ◽

10.1093/nar/gkaa219 ◽

2020 ◽

Vol 48 (9) ◽

pp. 4698-4708 ◽

Cited By ~ 10

Author(s):

Simon Eitzinger ◽

Amina Asif ◽

Kyle E Watters ◽

Anthony T Iavarone ◽

Gavin J Knott ◽

...

Keyword(s):

Machine Learning ◽

Gene Editing ◽

Adaptive Immune System ◽

Streptococcus Iniae ◽

Sequence Information ◽

Bacterial Genomes ◽

Training Set ◽

Direct Identification ◽

Adaptive Immune ◽

Shared Set

Abstract The increasing use of CRISPR–Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR–Cas inhibitors as potential mechanisms of control for gene editing applications. Many anti-CRISPRs have been found that inhibit the CRISPR–Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method to aid direct identification of new potential anti-CRISPRs using only protein sequence information. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking. We then applied AcRanker to predict candidate anti-CRISPRs from predicted prophage regions within self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA20 (ML1) and AcrIIA21 (ML8). We show that AcrIIA20 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA21 inhibits SpyCas9, Streptococcus aureus Cas9 (SauCas9) and SinCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

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Machine Learning Predicts New Anti-CRISPR Proteins

10.1101/854950 ◽

2019 ◽

Author(s):

Simon Eitzinger ◽

Amina Asif ◽

Kyle E. Watters ◽

Anthony T. Iavarone ◽

Gavin J. Knott ◽

...

Keyword(s):

Machine Learning ◽

Gene Editing ◽

External Validation ◽

Adaptive Immune System ◽

Streptococcus Iniae ◽

Sequence Information ◽

Bacterial Genomes ◽

Training Set ◽

Adaptive Immune ◽

Shared Set

ABSTRACTThe increasing use of CRISPR-Cas9 in medicine, agriculture and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as potential mechanisms of control for gene editing applications. Many such anti-CRISPRs have been found in mobile genetic elements that disable the CRISPR-Cas adaptive immune system. However, comparing all currently known anti-CRISPRs does not reveal a shared set of properties that can be used for facile bioinformatic identification of new anti-CRISPR families. Here, we describe AcRanker, a machine learning based method for identifying new potential anti-CRISPRs directly from proteomes using protein sequence information only. Using a training set of known anti-CRISPRs, we built a model based on XGBoost ranking and extensively benchmarked it through non-redundant cross-validation and external validation. We then applied AcRanker to predict candidate anti-CRISPRs from self-targeting bacterial genomes and discovered two previously unknown anti-CRISPRs: AcrllA16 (ML1) and AcrIIA17 (ML8). We show that AcrIIA16 strongly inhibits Streptococcus iniae Cas9 (SinCas9) and weakly inhibits Streptococcus pyogenes Cas9 (SpyCas9). We also show that AcrIIA17 inhibits both SpyCas9 and SauCas9 with low potency. The addition of AcRanker to the anti-CRISPR discovery toolkit allows researchers to directly rank potential anti-CRISPR candidate genes for increased speed in testing and validation of new anti-CRISPRs. A web server implementation for AcRanker is available online at http://acranker.pythonanywhere.com/.

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