A brief retrospective on our second blogiversary

It’s been an eventful two years, to the month, since we launched our omics and analysis blog. Our first-ever article traced how BioStrand evolved from an idea discussed over a pleasant family dinner, to an innovative and recognized genetic research technology business.

 

Today, the BioStrand idea has become LENSai™ Complex Intelligence Technology, offering a differentiating analytics capability within the IPA portfolio of innovative biotherapeutics services and programs. We mark this evolutionary milestone with a brief retrospective of some of our most well-received blogs, articles, and interviews from the past two years. 

 

The evolution from single- to multi-omics analysis

 

The imperative for biological research to transition from single-layer omics techniques to multi-layer, multi-domain, multi-omics analysis has been a key driver of development and innovation at LENSai. As a result, we have focused quite extensively on investigating and reviewing the broad, complex, and multidimensional practice of multi-omics analysis. 

 

Integrating all biological data

 

We have examined how truly effective multi-omics analysis can only happen when all omics data, irrespective of domain, source, type, or silo is computable out of the box. Of course, this would require a brand new paradigm of multidimensional scalability in multi-omics research which, in turn, would require addressing several challenges related to the integration of multi-omics data and the standardization of biological metadata. We even looked at scalability from the perspective of integrating all textual information lying unused in public biomedical journals and proprietary research dockets, to expand the analytical scope of multi-omics research. And we delved into the implications of multi-omics research in applications such as biomarker and antibody discovery. 

 

Advancing sectoral R&D

 

We have also honed in on R&D opportunities and challenges in different sectors. From accelerating innovation in the life sciences, integrating microbiome research for climate-resilient agriculture, and exploring the potential for novel therapeutic applications in dermatology from the analysis of skin microbiota.

 

Technological innovations in biological research

 

As a bioinformatics platform based on cutting-edge technology innovation, we have frequently taken a tech-led perspective on different concepts and sectors within biological research. We have examined how knowledge graphs will be critical to contextual, intelligent, data-driven decision-making in biomedical research. We looked at how artificial intelligence/machine learning (AI/ML) and natural language processing (NLP) technologies are impacting drug discovery and development, across early phase drug development, improving drug safety and fully autonomous drug discovery.  We reviewed the role of technology in the evolution and future of precision medicine. 

 

Illustrating LENSai research workflows

 

As our platform evolved in scope and sophistication, we also launched a specialised How-To series of articles with a step-by-step demonstration of LENSai workflows for different research applications. Topics include identifying homologous epitopes or species related to a sequence, characterising protein domains, and retrieving shared motifs across homologous sequences. 

 

Showcasing IPA talent

 

Another specialised series we introduced focuses on our most valuable resource, our human capital. This interview series introduces members of the multi-faceted and transdisciplinary team driving innovation at IPA with LENSai.

 

As we progress beyond our second blogiversary, we will continue to focus on contributing insights and analyses that will hopefully provide an original, informative, and objective take on all things related to biological research. We thank you for your patronage and hope to see you as we embark on the next phase of our journey. Please do get in touch with us and let us know how we can improve, expand, or augment our IPA, LENSai blog. 

 

 

A better way to analyse multi omics data

 


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