Bioinformatics, AI and Big Data - the way forward


Artificial intelligence (AI)


Artificial intelligence based computation and clinical interpretation and reporting capabilities stands to chance the way we approach disease prognosis and treatment for an individual.  AI - based computation and interpretation solution can only happen if the big data information technology and its integration with healthcare processes (including ‘electronic health record’ (EMR / EHR),  X-ray, imaging data, MRI scans, NMR, etc…..) together with research data from Proteomic, Genomics and Metabolomics is combined and crunched to develop and provide artificial intelligence (AI)-based computation and interpretation solutions






What is AI ?






Big Data

  • Data collection is now easier than ever – but with such high volumes of data at our fingertips, it is becoming difficult to ensure we’re using it well
  • Before accessing available data, it’s important to understand what was collected and how, to ensure that it’s fit for purpose
  • New kinds of data require new governance, new standards, and new interfaces in order to ensure good quality and comprehensibility
  • Big data will be increasingly important for a wide range of applications – but only if our technologies, and our methods, keep up!



  Challenges ahead in Big Data Analysis & Bioinformatics

  • Currently, we are lacking ‘end to end’ Bioinformatic solutions focused on clinical research and applications and this remains the unmet need moving forward.  Despite joint efforts from different industries to develop end to end products and solutions, the progress has been slow.  Many have now turned to strategic alliances, partnerships, and collaborations to enhance capabilities to offer an integrated and interoperable solutions to the scientific and medical community.  


The following would certainly help including:

  • High adoption of cloud-based solutions to support ever-increasing high-end data storage client requirements, with public and private cloud solutions integrated into service offerings;
  • Developing informatics products for precision medicine and clinical and molecular diagnostics
  • Combination of multi-omics data for interpretation of disease scenarios;
  • Driving big data analytics for clinical interpretation;
  • Provision of high-speed computation and data analysis tools
  • High-throughput sequencing and population-scale genome sequencing;
  • A move away from core informatics solutions toward 'sequencing-to-report' offerings with informatics platforms integrated with novel technologies
  • A 'software-as-a-service' (SAAS), with Proteomics, Genomic & Metabolomic 'sample-to-clinical' report capability.






What do we currently have in medicine?

Electronic medical records (EMRs) - a digital version of the paper charts in the clinician’s office. An EMR contains the medical and treatment history of the patients with advantages over paper records including:

  • Tracking of data over time
  • Easy identification of which patients are due for preventive screenings or checkups
  • Checking of patient parameters such as blood pressure readings, vaccinations, drug treatment monitoring, biological monitoring, etc..
  • Monitoring and improving overall quality of care within the health service

Experience with a doctor or GP today is almost identical to what it was 20 years ago - except that instead of looking at you, the doctor is begrudgingly documenting your exchanges into EHRs. Taking your blood pressure, a prescription and smile are the only ‘take-aways’  to make you feel better - was it worth a visit !



                                                  Biological testing is just the start of the process !



Every day around the world, millions of people take the wrong medications and suffer adverse reaction or death and this is unacceptable. We currently do nothing about it as we watch helplessly. Our genomic make-up and information about medications prescribed should have precise information about the outcome – but currently it is a ‘lottery’ or a gamble.  Today, both sets of these data are readily available and at our disposal.  Moving forward, ‘Machine learning’ and ‘AI’ will eventually close the loop to harness the predictive power of the data to ensure that one does not receive the wrong medication. We are now talking about the futuristic pharmacy.


Similarly, future Emergency departments and Surgeries will be very different. Rather than waiting for symptomatic escalation, many medical events will be detected and intercepted upstream. Emergency services powered by AI, wearables, and digital streaming data will facilitate this.  As we grapple with healthcare’s monumental challenges, we should consider whether our biggest opportunities are not just ones of medical science or technology, but ones closer to the ground—rethinking how to use technology and data to redefine healthcare itself.


'End-to-end informatics solutions' for clinical applications remain the greatest unmet need and much more needs to be done if we are to promise a better health-care service ! Through machine learning and artificial intelligence, we have the ability to reason about and utilize data at an unprecedented scale in order to predict, prevent, and treat disease more effectively.



...............reality is that researchers predict that in the next three years there will be examples of targets discovered through machine learning and ‘in silico' methods that unravel biology that wasn't appreciated before.  Drug discovery is time consuming, hampered by human biology, incomplete knowledge of disease states as well as high costs.  Hence, AI and the use of machine learning or other artificial intelligence approaches to comb through genomic databases, or draw links between molecular structures and drug activity, is an alternative way forward. 

In practice, AI has yet to make much of an impact on drug discovery success rates or speed and discovering new targets is only the start – as we know, many biological targets have been considered "undruggable" due to challenges in medicinal chemistry!  Whilst many talk about AI in drug discovery, there are only a hand-full of Pharama companies actually starting to think about it.  It will take a new type of pharma company to embark on AI from scratch and with a totally different & innovative approach if we are to see any positive outcome.