Table 2. Some acquisition strategies along with brief descriptions.

AI, artificial intelligence

Acquisition strategies	Description
Partnerships and collaborations	Access to large and varied genetic datasets can be gained through forming alliances and working together with academic institutions, biotechnology businesses, and healthcare organizations. These kinds of partnerships make it easier to develop and validate AI models more broadly.
Mergers and acquisitions	Businesses in the AI and genomics industries may merge or buy other companies to pool resources and expertise. Fusing AI technology with already existing genome sequencing tools can spur innovation and produce all-encompassing solutions.
Data licensing and sharing	Large-scale genomic dataset acquisition and curation are areas of expertise for some organizations. By granting licenses or sharing these datasets with AI-focused businesses, strong machine learning models can be developed and trained, improving the precision and applicability of genetic investigations.
In-house data generation	Businesses that leverage AI to drive genomic sequencing may make investments in internal data production resources. This gives more control over the caliber and variety of the information used to train AI models since it entails directly gathering, processing, and analyzing genomic data.
Strategic alliances with sequencing platforms	AI-focused businesses are able to directly incorporate their algorithms into the sequencing workflow by forming strategic agreements with DNA sequencing platform suppliers. This partnership makes it possible to analyze and understand data in real time, which expedites the process of sequencing a genome.
Crowdsourcing and citizen science initiatives	Including the public in citizen science or crowdsourcing projects can be a useful acquisition method. Encouragement of individuals to share their genomic data for study enables AI-powered platforms to access a larger and more varied pool of genetic data.
Investment in research and development	By allocating resources toward internal research and development, organizations can foster innovation in AI algorithms tailored for the interpretation of genomic data. This tactic aids in the development of proprietary technologies and keeps one ahead of the curve in the field of genomics and AI convergence.
Open-source collaboration	Collaboration and information sharing are encouraged by using or contributing to open-source initiatives in the AI and genomics sectors. Through pooled knowledge, open-source projects offer a platform for the creation and enhancement of AI algorithms for genome sequencing.
Clinical trial collaborations	Clinical genetic data can be accessed through working with pharmaceutical companies and clinical trial activities. Real-world patient data can be used to enhance and test AI applications in genome sequencing, leading to more accurate and therapeutically meaningful outcomes.
Global expansion and market access	AI-powered genome sequencing firms are able to collect a vast array of genetic variations by expanding their operations worldwide and acquiring access to varied populations. This tactic improves the AI models' resilience and generalizability to various racial and geographic groups.