Big data has seen its fair share of uses across a wide variety of industries. Its impact on medicine, however, is truly remarkable. The massive amount of information that big data provides reduces the time needed to conduct research and collect results. Similarly, big data is having the same effect on insurance allowing for more complex coverage and accurate estimates.

All of this helps doctors process information in no time. Big data provides doctors and medical professionals with all of the requested information in the blink of an eye.

This means that a patient’s records could be combed through and analyzed to ensure a treatment’s success in monument notice. This influx of information and analytical data makes it possible for doctors to treat their patients from a distance, even at home.

Here are 3 ways big data is radically transforming medicine and healthcare:

1. Big data is helping further research

We all create massive amounts of data every day. Whether it’s metadata, text data, video data, or location data, we’re all generating trillions of data points every single day.

All of this data is undoubtedly useful to us, especially health and medical big data. However, it is impossible for a team of researchers to comb through petabytes of disparate data points and find the logical patterns within them.

However, in a mass of data, there are hidden patterns, correlations, and relationships, but they are essentially naked to the human eye. Thankfully, AI has advanced to the point where computer programs can intelligently sift through large data sets and find possible correlations. Then, researchers review these correlations and data points to see if they make sense.

Big data helps researchers find answers to cancer treatments, causes of diseases, and even identify mitigating factors that were previously unknown.

2. Big data is changing insurance

With wearable technology, we all have the ability to monitor our heart rate, our activity levels, and our sleep cycles. This health data is not only valuable to us as individuals, but it could also prove to be valuable to doctors, insurance companies, and hospitals.

What if you lower your risk for heart disease and find more affordable health insurance at the same time? With wearable tech, this might just be a possibility.

Of course, with monitoring there is a question of invasion of privacy and whether or not insurance companies should be able to use such personal data. Undoubtedly, big data will change the industry. But what regulations or restrictions may be passed still remain unseen.

3. AI and big data could change telehealth

AI and big data are like peanut butter and jelly. They are just meant for each other. Without AI, big data would be extremely difficult to understand, analyze, and organize. And without big data, it would be difficult for us to come up with training sets that accelerate AI.

AI and big data work together really well in many contexts. In addition to helping medical researchers, big data and AI are being used for telehealth applications.

Telehealth apps with personal AI assistants can help people with chronic illnesses get the support and the medical advice that they need on a daily basis.

Big data is becoming easier for doctors to process, making it easier to give long-distance diagnoses and exams. Ultimately, big data could accelerate healthcare tech and telehealth to new heights.

Conclusion

Though there are fair concerns as to how big data is passed through in other industries, healthcare has seen almost nothing but benefit. Access to technology that can accurately determine the status of a patient by quickly going through their records and analyzing their health has changed healthcare for the better. There is now much less guesswork and less time required for doctors to treat patients effectively and quickly.

Expanding the medical industry to applications that can be used to treat patients has already saved countless lives. Big data only ensures that more information can be analyzed and patterned for the benefit of the healthcare industry and its patients.

The effect big data is already having on technology will only expand over the course of the next few years as it continues to lead to new technologies and advancements in the medical industry.

Big data is the answer that the medical industry was seeking for its patient information needs. Big data is revolutionizing nearly every industry at a breakneck pace. Hopefully, these technologies will lead to an easier and healthier life for us all.

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Thanks to the technology that we have at our hands these days, our everyday lives are much easier. We are able to complete multiple tasks without even leaving the comfort of our homes, stay updated on the latest news, and much more. Medicine was one of the areas that progressed immensely due to technological advancements, and we couldn’t be happier about it.

People receive the proper care, get accurate diagnostics, and the devices used to make every service both effective and efficient. In the past couple of years, there have been talks of implementing artificial intelligence in the medicinal sector as a way to improve this industry and make it near-perfect. We wanted to discuss the question of how can AI help this sector, but we are also going to take a look at one industry where AI is used to the fullest potential.

Where is AI Used The Best?

One of the industries that have managed to incorporate this technology and use its full potential is the online casino industry. Casino sites use artificial intelligence to protect their players, but also to enforce fair-play. Let us explain how.

In order for every player to have equal chances of winning, online casinos use Random Number Generators. This AI system creates random outcomes of each game and gives equal chances of winning to all players. Casimba Casino is a good example of an online casino that features this and the security AI, which we are about to explain.

The AI-powered security system at the aforementioned casino and many other casino sites goes by the name SSL-encryption software. This software takes all the data from the players and turns it into an unbreakable code, thus making it impossible for unwanted third parties to gain access. Both of these AI systems utilize algorithms to ensure safety and fair-play.

How Wil AI Help Medicine?

Through the use of algorithms, medicine can reap great benefits. Medicine sites can use SSL certificates to keep their patients’ data safe and out of harm’s way. Not only that, but this type of AI can also impact other areas such as radiology, pathology, cardiology, and ophthalmology. How? The algorithms are ever-learning and can analyze the data from various patients much faster than a doctor can. Then, by comparing it with other diagnoses, it can aid the doctor and pinpoint the exact treatment needed for that particular case.

These AI systems would aid in practices like diagnosis, treatment protocol development, patient monitoring, drug development, personalized medicine, and care.

Being efficient in this line of work is very important. Additionally, AI has the potential to be less prone to errors, which is a big advantage, especially when it comes to determining the diagnosis and the right treatment.

The only problem here is; AI is still in its development stages and authorities do not trust it that much to give it such an important role. While basic artificial intelligence is used in some sectors, many believe that it is still early to fully incorporate it. But, as technology keeps evolving, we do not doubt that AI will help medicine become much more effective.

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Artificial Intelligence (AI) is increasingly being used in medicine to support human expertise. However, the potential of these applications and the risks inherent in the interaction between human and artificial intelligence have not yet been thoroughly researched. The fear is often expressed that in future, as soon as AI is of sufficient quality, human expertise will become dispensable and therefore fewer doctors will be needed. These fears are further fuelled by the popular portrayal of this as a “competition” between humans and AI. An international study led by MedUni Vienna has now illustrated the enormous potential of human/computer collaboration.

The international study led by Philipp Tschandl and Harald Kittler (Department of Dermatology, MedUni Vienna) and Christoph Rinner (CeMSIIS/Institute for Medical Information Management, MedUni Vienna) now debunks the idea of this alleged competition, highlighting instead the enormous potential of combining human expertise with Artificial Intelligence. The study published in Nature Medicine examines the interaction between doctors and AI from various perspectives and in different scenarios of practical relevance. Although the authors restrict their observations to the diagnosis of skin cancers, they stress that the findings can also be extrapolated to other areas of medicine where Artificial Intelligence is used.

AI does not always improve diagnosis

In an experiment created by the study authors, 302 examiners and/or doctors had to assess dermoscopic images of benign and malignant skin changes, both with and without the support of Artificial Intelligence. The AI assessment was provided in three different variants. In the first case, AI showed the examiner the probabilities of all possible diagnoses, in the second case the probability of a malignant change and, in the third case, a selection of similar images with known diagnoses, similar to a Google image search. As a main finding the authors observed that only in the first case did collaboration with AI improve the examiners’ diagnostic accuracy, although this was significant, with a 13% increase in correct diagnoses.

“Interestingly, less experienced examiners benefit more from AI support than experienced ones. Less experienced examiners trusted AI more than did the experienced ones. The latter only accepted the AI suggestions to change their original diagnosis in cases where they themselves were unsure,” the authors wrote. A second experiment showed that all examiners, even acknowledged experts, can be misled by AI, if the output was changed to indicate false diagnoses. “The study therefore shows that a certain amount of trust in AI is required in order to benefit from it but that this trust can also have a downside.”

AI could ease doctors’ workloads and improve quality

In a further step, the authors showed that good quality AI is able to filter out benign skin changes in a telemedicine scenario. This could significantly reduce the number of cases needing to be examined by a human expert.

The researchers then demonstrated using real, prospectively gathered data, that even inexperienced examiners can make expert-level telemedicine diagnoses with AI support. They also demonstrated in a final experiment that people can learn to use AI-generated concepts as diagnostic pointers in order to improve their own skills independently of AI.

The conclusion: “In future, human and artificial intelligence will supplement each other and jointly improve patient care. We should stop focusing on the contest but rather on the collaboration between human expertise and AI. Since not all users benefit equally from AI support and since the form of support plays a role, AI-based medical systems should not only undergo practical testing as stand-alone applications but also always in interaction with the typical user.”

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May 20, 2020 – A big data analytics tool that uses information from multiple cancer types could help researchers identify potential treatments and accelerate precision medicine, a study published in the Journal of Clinical Oncology Clinical Cancer Informatics revealed.

Developed by researchers at the University of Michigan Rogel Cancer Center, the tool combines multiple datasets to help turn information into meaningful clinical insights. Recent efforts to categorize the molecular data of multiple cancer types has produced an overwhelming amount of data, researchers noted, and this tool could help researchers make sense of it all.

“Our idea was to combine three sources of data sets – molecular data from both cancer cell lines and patients and drug profiling data – to understand proper preclinical models that are most representative of these tumors,” said Veerabhadran Baladandayuthapani, PhD, professor of biostatistics at the University of Michigan School of Public Health.

The tool, called TransPRECISE, uses data from 7,714 patient samples across 31 cancer types, collected as part of the Cancer Proteome Atlas. This information is combined with 640 cancer cell lines from the MD Anderson Cell Lines Project and drug sensitivity data representing 481 drugs from the Genomics of Drug Sensitivity in Cancer model system.

“The good thing is this is a very dynamic process. We can have this whole system set up in a computer. As new patients come in or new data comes in, you can keep adding it,” said Rupam Bhattacharrya, MStat, a doctoral student and first author on the paper. 

The new tool builds on an earlier model from the team, called PRECISE (personalized cancer-specific integrated network estimation model). The PRECISE model aimed to analyze the changes that occur to the molecular structure of individual patients’ individual tumors.

TransPRECISE adds in data from cell lines and drug sensitivity, which will be helpful for researchers translating cancer cell biology into drug discovery.

“Now that we have tens of thousands of tumors on these patients, we can evaluate what might be the potential therapeutic efficiency of these drugs. The key idea was to develop an analytic tool to do that,” said Baladandayuthapani, who is also director of the Rogel Cancer Center’s cancer data science shared resource. 

The research team validated the tool by comparing known drug responses and clinical outcomes in patient data. The model identified the differences in proteins among individual tumors, and accurately tied it back to actual patient outcomes.

Researchers also looked at several pathways to predict potential drug targets, which generated results that reflected current treatment recommendations or targets being tested in clinical trials, such as ibrutinib for BRCA-positive breast cancer.

In addition to the published study, researchers have made a comprehensive database and visualization of their findings publicly available. The team expects the tool to lead to accelerated drug discovery for different types of cancer.

“We have so much data, how do we drill it down to make it more informative so an oncologist can understand? Our work would potentially help oncologists or researchers develop concrete hypotheses based on which mechanism is working, potentially bringing to the top drugs that might warrant more evaluation,” Baladandayuthapani said.

The results demonstrate the potential for analytics tools to advance precision medicine for cancer and other types of complex diseases.

“In summary, TransPRECISE offers the potential to bridge the gap between human and preclinical models to delineate actionable cancer-pathway-drug interactions to assist personalized systems biomedicine approaches in the clinic,” researchers stated.

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What just happened? Of all the domains where machine learning is expected to be revolutionary, medicine is perhaps the most universal. In a major new milestone, a drug developed using machine learning is about to enter human trials.

Before a new medicine enters human trials, there is typically three to five years of work behind the scenes, researching causes for diseases and compounds that may help treat them. But working with British AI startup Exscientia, a Japanese drug development company called Sumitomo Dainippon Pharma Co. is about to start phase 1 clinical trials after only 12 months.

The drug in question is DSP-1181, a prospective treatment for obsessive-compulsive disorder (OCD). OCD affects millions of people worldwide, to varying degrees, and can be debilitating in its psychological effects.

Exscientia, based in Oxford, UK, operates an exciting machine learning platform called Centaur Chemist. The platform allegedly takes years off the time required to research new compounds, by combining A.I. techniques with existing knowledge of how medicines interact with the human body.

The benefit of machine learning is that it can happen virtually, and far quicker than scientists are able to work in the real world. The platform can analyze millions of molecular combinations and attempt to identify which may be the safest and most effective in treating a given disease.

Perhaps even more important is the potential savings associated with using machine learning to develop new medicines. Typically, it costs over $1 billion to bring a new drug through from conception to market, with a lot of those costs borne out during the research phases. But taking out years of painstaking research will save both time and money, speeding up development and freeing up resources to develop yet more medicines.

There’s a lot riding on Exscientia and Sumitomo Dainippon’s trial. The first phase is to check how the drug affects the body, and how the body metabolises the drug. So this will not prove the medication’s efficacy.

But if DSP-1181 is shown to be safe, phases two and three can proceed, to see whether the drug can help OCD patients in the real world. And if it does, we’ll witness the dawn of machine learning in medicine.

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Artificial intelligence or our capability of inventing systems that can think, create and act better and faster than humans has become a buzzword that everyone knows about. AI for medicine, AI for finance, AI for auto, we all seem to know and be fearful for the role of AI in next stage of human and industrial evolution.

So, why is AI important in this time and age?

Is it because the world is growing so rapidly in terms of our demands and we are not able to keep up with the pace of meeting those demands, as manual operations are inefficient? Or is it getting rid of human errors in execution? To me it is both, speed of execution and accuracy of results. It is fair to say that humans are born with limitations. Our minds as brilliant, but they cannot process information faster than supercomputers and execute on tasks with femtosecond capabilities needed to execute on millions of decisions needed every second in every industry to serve the growing human race.

Now let me ask a question, why do we need hyper decision making, and quick results, why are we now in a stage of human evolution where speed is everything?

Throughout human evolution on Planet Earth whenever humans have faced problems we have developed solutions to address those problems by sheer ingenuity that usually comes from prior knowledge and trial and error. Now imagine, the last 300 years or so in your minds, the rate of invention from the basic scientific discoveries to the invention of light bulb, automobiles, manufacturing systems has been so rapid that our brains have been learning and evolving extremely rapidly. From the development of the first basic computer code to the invention of smartphones that happened over a time period of mere 50 years or so, has changed how we think and behave. We do not realize but our minds are now influenced by enormous amounts of global data that make us behave and react in certain ways and patterns.

AI feeds off what we call machine learning, that is nothing but training a computer brain on a specific problem set. Almost like how the human mind learns from birth as it is exposed to the world. The machine learning process then allows an AI on top human cognition to be able to use the knowledge and make predictions that are not just fast but extremely accurate. The computer brain is limitless and can be trained with enormous amounts of data, the more you feed the more it learns. Unfortunately not all humans have this capability of learning to that level and this is why machine learning and AI is so important in executing rapidly and accurately.

Now let’s focus on how AI can impact in revolutionising healthcare.

Imagine yourself and the complex biology that helps you live on planet earth. The millions of biochemical processes in your body define how you think, behave and stay alive. The whole concept of human age and longevity is driven at a personal level. The complex biology is human data. If we can learn and control how every single human gene operates in human body and drives an individual’s existence, we can create a myriad of products and services doing exactly this. The last decade has seen the rise of technology in healthcare or what we call health tech.

Companies that build machine-learning systems that can read radiology scans can detect diseases at the earliest of stages. In India, we have many such startups leveraging this data science to help radiologists with their job. Niramai, Qure.ai and Sigtuple are some of the leading startups in this space.

Similarly wearables that track how much you walk, calories you burn, and the sleep you get have changed human behavior in a major way. Fitbit, Apple watch and local products like Goqii are driving human behavior change. What we eat is who we are. Nutrition tech companies like Healthifyme, curefit are not only helping us eat optimally but also collecting data on our nutritional patterns and the impact on our health at an individual level. This data can be used to train machine-learning systems that can better advise us on what to eat and drive enormous change in nutrition and food industry

On the secondary healthcare side, companies that are leveraging IPD and OPD data are able to better advise hospitals on how to operate efficiently. Collectively, we are creating an intelligent ecosystem that will be able to track how the human body is changing on a regular basis, predict diseases, develop targeted therapeutics and personalized supplements, advise on how much to move, what to eat.

On the commercial side, data and AI driven healthcare system can advise hospitals can be better prepared to handle patient load on a daily basis, insurance companies can price their premiums better, Governments can better plan budgets with individual level data on disease burden and then district or community level data on population health.

India is on the cusp of a major healthcare revolution driven by technology. With 1.2 billion human bodies and growing, we have the major leverage of training some of the best machine learning platforms on trillions of human health data point to create a truly unified health system that is driven via automation.

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Technologies like deep tech and big data are working with precision medicine to give us customized healthcare.

Big data in the medical field is helping experts see patterns in patient populations. Genetic data is helping them pinpoint root causes of many diseases like cancer.

This, in turn, is leading to R&D in deep tech, which is based on substantial scientific advances and high tech engineering innovation. Hence, every patient can now avail medication and treatment tailor-made for their condition, in other words, precision medicine.

Hunaid Hameed, a Data Scientist Trainee at Data Science Dojo told The Sociable:

“Big data and deep learning is a hot field for AI research. Most of the work done targets detection and identification done with the human eye using Convolutional Neural Networks.”

Deep learning is part of a wider family of machine learning methods based on artificial neural networks. In deep learning, a convolutional neural network (CNN, or ConvNet) is a type of deep neural network, most commonly implemented to analysis of visual imagery.

Like many sectors of today’s global stage, medicine and healthcare are getting a face lift, as they connect more and more with high technology.

Big Data

Big data predicts diseases, so that preventive plans can be implemented. If patients contract a disease, an accurate treatment plan based on previous data can be applied.

Keeping big data as the base, precision medicine benefits patients in disease prevention, early disease detection, and early disease treatment.

Hameed was engaged in a research conducted with the Civil hospital in Karachi, Pakistan for the identification of dermatological diseases in March of 2019. Data was collected from patients by taking pictures of these diseases.

A deep neural network was then used to classify these images, which achieved an accuracy of 86%.

“The ability of the model was comparable with human doctors,” says Hameed.

Deep Tech

Deep tech like Watson for oncology, an Artificial Intelligence (AI) program developed by IBM, provides optimal and individualized treatment options for patients. It does so using its vast medical database and the patient’s own unique medical and genetic information.

Watson has been introduced globally and used for actual diagnoses at several clinical centers. This is an example of modern medicine, where integration of technologies across multiple disciplines, such as genetics, genomics, big data, and deep learning is happening.

The Human Genome Project, which was launched in 1987, ensured that human genes and DNA sequences have been fully mapped out. Since human genes are akin to unique fingerprints, this project helps differentiate among individuals.

In a 2018 paper on precision medicine, the author says that in order to reap the benefits of genetic information, “it is essential to integrate big data, deep training, and bioinformatics, all of which contribute to making the connection between specific diseases and signatures.”

Apart from projects like Watson and the Human Genome Project, progress in robotics, stem cell therapy, bio ink, and creation of 3D printing tech enabled artificial tissue construction will change our future medical environment and culture.

Smart Medicine

While big data, deep training, and bioinformatics integrate to make connections between diseases and bio-signatures, smart medicines aid in health monitoring of the individuals. Thus, this creates a system that zeroes in on each individual’s health.

For instance, smart health options like Google Health and Microsoft Blot enable direct and real-time communication between health care providers and patients through the use of smart phones. This has resulted in more cooperative patient involvement in decision-making processes.

Watch Out for Bias

Potential for bias to enter the system is a possibility. Evidence shows that use of algorithms can worsen disparities currently intrinsic to the contemporary healthcare system.

Hameed’s team faced this problem during their research in documenting diseases, since some diseases, such as psoriasis, are spread all over the body and have a high number of images. Others, such as fungal infections (tinea), occur very frequently.

“Bias in AI can be developed due to an uneven distribution between classes. We had to strike a balance between the classes to make them even. Otherwise, the model was always highly inclined to detect psoriasis due to its higher frequency. The balance was achieved with weighted penalties,” he says.

Global Presence of Precision Medicine 

According to Deloitte’s 2019 Global life sciences outlook, the global personalized medicine market is expected to rise to more than 11% CAGR for the period 2017–2024, with the help of advances in health care analytics, AI, and blockchain.

China expects a major investment into the precision medicine sector by 2030. During the fourth Chinese Healthcare Industry Upgrade Summit held in July in Beijing, Liu Zhenzhen, co-founder of OrigiMed, a Shanghai-based molecular diagnostic information provider, said that for biopharmaceutical companies, precision medicine cuts the average drug R&D period from 10 to 12 years to three to five years.

In 2015, the US National Institutes of Health (NIH) introduced the Precision Medicine Initiative and invested more than $200 million to speed up biomedical research and provide clinicians with new tools for selecting personalized therapies.

This August, the Pittsburgh Health Data Alliance (PHDA) announced a machine learning research sponsorship from Amazon Web Services (AWS). The initiative aims to advance innovation in areas such as cancer diagnostics, precision medicine, voice-enabled technologies, and medical imaging.

Precision medicine bids the one-size-fits-all model of medicine goodbye and heralds the innovative approach of taking into account each individual’s different characteristics, such as genetic profile, environment, and lifestyle.

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NEW YORK — In a broad sense, AI is the ability to create programs that mimic elements of human intelligence and has been around for decades but made possible by three recent advances: massive computing power, improvements in machine learning techniques and big data, Justin Ko, MD, clinical associate professor at Stanford School of Medicine said in a session here.

He added that technological advances cannot solve all problems in the real world and joked with the audience, asking how many attendees came to his talk at the American Academy of Dermatology Summer Academy Meeting to hear how computers would replace the need for clinicians.

“To create an algorithm, you need a lot of data and a label of interest, essentially the answer key,” Ko said. “You can train a model to figure out the best way to match that data with the answers with a set of rules. You can then test the model by applying it to complete the task you created, and then you see how it learned. You can do this for any task you can imagine.”

It is powerful, as it can uncover complex relationships in data in a way that traditional statistics and the human brain cannot.

“When we start to gather new data streams — like from genetics, the microbiome, social media feeds, EHR data — we can start to interrogate the linkage between health and disease and essentially engage precision and personalized medicine,” Ko added.

AI performs reliably in the first task or the millionth. It can be trained to form a specific task and is geared in ways the human brain is not.

“It possesses no threat to us or our jobs. It’s a tool, not a solution and is dependent on us to make it work,” he said.

Moreover, augmented reality can help improve workflow and offer insight in clinical decisions; Ko suggests thinking of augmented reality like a virtual digital assistant.

Instead of spending most of the patient visit gathering information, you could spend it connecting with the patients to make sure they feel seen and heard, he said.

“Could deploying a virtual clinical system bring more humanity to your practice?” he asked.

The quality of data used is of the utmost importance, and is also a concern with this technology. “AI will magnify biases within the data set, so those need to be known.

“What we need to do is make sure high quality data are representative and inclusive of the populations we want to study,” Ko said.

Patients without access to care are unlikely to be part of any data sets but have the most potential to benefit from this technology and access, he added.

“We as physicians have to be thoughtful about the implementation that AI bridges, not worsens, these gaps. …We have to be consistent and relentless in advocating for those appropriate guardrails to be in place to protect our patients from harm and of unintended consequences in technology and make sure the algorithms are subject to rigorous external and internal validation.” – by Abigail Sutton

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his might seem a particularly bad time to be investing in big tech.

President Trump said Tuesday morning that his administration would look into accusations that Google has been secretly working with the Chinese military. The charge came from Peter Thiel, a PayPal (PYPL – Get Report) co-founder and strong supporter of the president.

On the other hand, Bloomberg reported Tuesday that DeepMind, the artificial-intelligence arm of Alphabet,  (GOOGL – Get Report)  might be on the cusp of a major breakthrough in the way new drugs are discovered.

It’s an important innovation. It’s hiding inside the search giant. And it couldn’t come at a better time.

This business is on to something really big. Using data, machine learning and AI, Alphabet managers are incubating vibrant new businesses with innovative business models. One or more of these will become exciting stand-alone businesses.

Some analysts are already doing sum-of-the-parts analyses and they like what they see.

A Jefferies analyst pegged the value of Waymo, Alphabet’s self-driving-car business, at $250 billion in December 2018, according to a story at Business Insider.

Alphabet’s market capitalization is $798 billion, with units including YouTube, Google Search, Google Cloud, Android, the Nest security camera and peripheral businesses, Google Capital, and Stadia, its new video game streaming service set to launch in November.

Together, these parts are probably worth well over $1 trillion.

Until now, the business opportunity for DeepMind was not even on investors’ radar.

The subsidiary has its roots in DeepMind Technologies, a British AI startup that was making progress teaching computers the quirks of human short-term memory. Alphabet acquired the business in 2014.

Two years later, its custom AlphaGo code was so advanced that it became the first computer program to defeat a human in a match of Go, the ancient Chinese strategy game. That human happened to be Lee Sedol, the 18-time world champion.

At the CASP13 meeting in Mexico in December 2018, DeepMind was at it again. This time its human challengers were the brightest minds in biology. The task was predicting the shapes of proteins.

Understanding these structures is important because they govern how diseases form. The problem is there are more possible protein shapes than there are atoms in the universe, Bloomberg notes.

The math has vexed computational biologists for the past 25 years. They have been trying to build more predictive software models for protein folding, the process that leads to proteins taking three-dimensional shapes.

Despite its limited experience with folding, AlphaFold, DeepMind’s entrant, predicted the most accurate structure for 25 out of 43 proteins, taking the top spot over 98 participating teams, according to a report in the Guardian.

For perspective, the second-place team accurately predicted only three of the 43 proteins.

This does not mean Alphabet has an inside track to the next big drug discovery. It doesn’t work that way. Developing new drugs is both expensive and fraught with regulatory hurdles, patient trials and marketing expenses.

Even then, a 2013 study published by Nature Review Drug Discovery found that only 10% of medicines in development ever reach patients.

The business opportunity is increasing those odds.

In The Future Awakens, a November 2017 research study by Deloitte Center for Health Solutions, analysts posit that by 2022 medicine will be predictive, preventative (based on risk), personalized and participatory.

Computational biologists in hoodies and jeans will build personalized drug treatments based on what they know about a patient’s individual genomic makeup. Behind the scenes, data scientists using A, will comb through algorithmic models, looking for previously unseen biomarkers.

DeepMind has come out of nowhere to be a major player in that ecosystem, and it is hiding inside Alphabet shares, practically for free.

The parent’s stock trades at 21 times forward earnings and 5.6 times sales. These metrics reflect the consensus view that Alphabet is an advertising business, subject to regulatory attacks.

The regulation is coming. That’s true.

But the story of the stock is its valuable pieces. Investors are fretting about a potential breakup of Alphabet. They should be embracing that possibility. It will lead to much higher stock prices as the value of its businesses comes to light.

Growth investors should consider buying Alphabet shares into any material weakness.

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Advances in next generation sequencing mean we are now enjoying the era of the sub-1000-dollar genome, and the world’s genomic databases are expanding exponentially with primary sequence data from more and more individuals daily. These terabytes of genomic data, alongside the growing wealth of digital health records and clinical trials, bring invaluable insights into the biological mechanisms of human health, aging and disease.

In order to fully exploit these data, it is essential to foster the cross-disciplinary connections between computational and experimental approaches, and the transfer of state-of-the-art knowledge between the two areas. This is what the [BC]2 Basel Computational Biology conference at BASEL LIFE sets out to do, through a series of workshops and plenary sessions detailing the current level status of research, discoveries and computational methods on “Big Data in Molecular Medicine”.

The [BC]2 conference highlights five focus areas, with invited experts and selected speakers: single-cell data, evolutionary medicine, clinical population genomics, systems biology of disease, and multi-level data integration. These focus areas converge into three key themes:

From single-cell data analysis to precision oncology. Being able to identify and interpret the consequences of mutations in the individual cells of a tumour is key to classify the tumour’s stage, and to apply adequate therapies. Keynote speaker Peter Kharchenko, from the Harvard Medical School (US), studies tumour heterogeneity, as well as the interactions between tumour cells and their microenvironment – his talk will provide new insights on the joint analysis of heterogenous single-cell data collections.

From pathogen sequencing to fighting infectious diseases. Many diseases are caused by rapidly mutating pathogens (bacteria, fungi, viruses) which can eventually become drug resistant. Understanding the molecular properties of pathogens is thus essential when designing drugs and vaccines, and when allocating resources for the monitoring of disease outbreaks. Keynote speaker Roy Kishony, from Technion (Israel Institute of Technology, IL) combines experimental and theoretical approaches on the evolution of pathogens and antibiotic resistance, and will present the latest views on predicting antibiotic resistance.

Biological big data analysis and methods. Big data from basic research is made up of many different sources and formats, including (but not limited to) RNA or DNA sequencing, genome-wide association studies, and mass spectrometry. Extracting useful information from these data requires tailored tools and methods, such as algorithms derived from machine learning. Keynote speaker and systems biologist Yves Moreau (KU Leuven, BE) studies how the genetic variation in a person’s genome can influence the risk or severity of a disease – he will talk about Bayesian matrix factorization and deep learning data fusion to predict drug-target interaction.

Selected presentations and posters will further highlight research and efforts to facilitate data-driven precision medicine and give scientists an unprecedented opportunity to learn from one another. Practical sessions will include tutorials on machine learning in the context of DNA sequencing and drug sensitivity prediction models, pathogen genome analysis strategies, and reproducible data management, curation and analysis.

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