How Much Dirt is Too Much Dirt — Quality Metrics in Gene Expression Analysis

At twoXAR we bring together a lot of disparate data to rapidly identify disease treatments. It’s through these different data that we gain our predictive power. However, more data isn’t always better — not if the new data is of poor quality. In other words, quantity doesn’t trump quality, and that’s because of a common data science saying: bad data in = bad data out. Because of this, we check the quality of our input data at multiple levels; some of this is a manual process, but we automate as much as possible.

In July’s post, (ML)²: Myths and Legends of Machine Learning, I touched on the messiness of real world data and mentioned quality control checks; here, I will expand on that with an example of one of the checks we use for gene expression data…


twoXAR Announces Business Advisory Board

From Day 1, our vision at twoXAR has been to “improve health through computation”. We’ve taken many steps along this journey, such as collaborations with leading companies like Santen and breaking new ground in the path to more efficacious treatments in liver cancer. As we continue to build momentum and scale our aspirations to help as many patients as possible, we’re increasingly drawing upon the expertise of industry veterans to guide our strategic decision-making.

Given this, I’m pleased to announce the formation of twoXAR’s Business Advisory Board and welcome Judy Lewent, Jonathan MacQuitty, and Howie Rosen as we move forward on our company’s journey. Each of our advisors brings a unique perspective to our business as we continue to launch disease programs in collaboration with biopharmaceutical companies, investors, and drug development teams…


(ML)²: Myths and Legends of Machine Learning

Skepticism is (and should be) a vital part of any science; statistics and data science are no exception. Statistician George Box nicely summed it up when he said, “all models are wrong, but some are useful”. Box reminds us that statistical models are just that: models. A simplified representation of the real-world will always have shortcomings. But we shouldn’t forget the last bit of Box’s saying: “some [models] are useful”. Although challenging, carefully constructed statistical models can be extremely…


How machines are able to help you find a parking spot, a great place to stay, and the next medication you might take

These three accomplishments are all possible today because of machine learning.

Machine learning continues to disrupt markets and transform peoples’ everyday lives. Yet, the public is far removed from the actual technology that drives these changes. To many, the idea of machine learning may elicit images of complex mathematical formulas and sentient robots. In fact, many of the general ideas behind machine learning are approachable to a wider audience…


Synergizing against breast cancer

I was about twelve when I found out my grandmother had breast cancer. My parents did a good job of shielding me from the worst of the details, but there is no way to avoid fear that comes from a loved one being diagnosed with cancer. As a kid, there wasn’t much I could do, but my grandmother loves to tell the story of me trying to comfort her by telling her I was going to do research to help cure her cancer. Little did I know at the time that treating cancer is not as simple as taking a pill once a day and that even identifying the right medicine is akin to finding a needle in a haystack.

Over the next seventeen years, as I pursued undergraduate and graduate studies in biology and genetics, I filled in those knowledge gaps, but felt no closer to changing the status quo of breast cancer…


Positive Preclinical Proof-of-Concept Results For Liver Cancer Candidate, TXR-311

In September 2016, we announced a collaboration with the Asian Liver Center at Stanford University School of Medicine (the Asian Liver Center). The goal of this collaboration was to identify new drug candidates targeting hepatocellular carcinoma (HCC, the major form of adult liver cancer). Today, we announced a lead candidate, TXR-311, that has shown positive results in cell-based assays. I wanted to share a bit more background on liver cancer and details on why these results are exciting.

HCC is a primary cancer of the liver that tends to occur in patients with… 


Seeing the power of AI in drug development

Today we announced our collaboration with Santen, a world leader in the development of innovative ophthalmology treatments. Scientists at twoXAR will use our proprietary computational drug discovery platform to discover, screen and prioritize novel drug candidates with potential application in glaucoma. Santen will then develop and commercialize drug candidates arising from the collaboration. This collaboration is an exciting example of how artificial intelligence-driven approaches can move beyond supporting existing hypotheses and lead the discovery of new drugs. Combining twoXAR’s unique capabilities with Santen’s experience in ophthalmic product development and commercialization… 


The AI 100 & Combining Artificial Intelligence with Human Intelligence in Drug Development

We at twoXAR were very honored to be included this week in The AI 100, CBInsight’s list of top private Artificial Intelligence companies. It’s given me a chance to reflect on how we employ AI relative to others in the industry.

 Our focus is on drug development — and being one of the few biopharma companies to be included in the list, we use AI in a unique way. Where others may be using AI as the sole ingredient…


Inspiration from the TEDMED Stage

As with many of my fellow Americans, I have been reflecting about events that have been highlighted in 2016 in the media. Racial strife, gun violence and a polarizing political environment were repeated themes throughout the year. Over dinners and social events, the conversations with friends and family have been morose at times, as many are wondering if society is taking a turn for the worse.

I’m here to tell you that isn’t the case — there is a dedicated group of talented individuals working quietly to make the world a better place.

As a recent speaker at TEDMED 2016, I was fortunate enough to meet dozens of these inspiring pioneers and watch them on stage answering a question…


Consider Your Biases

In the wake of Donald Trump’s victory over Hillary Clinton, pundits and politicians alike have wondered, “how did we not predict this?” Theories range from misrepresentative polling to journalistic bias to confirmation bias, fueled by the echo chambers of social media. These fervent debates about bias in politics had me reflecting on the role that bias plays in science and in R&D. Sampling bias, expectancy bias, publication bias… all hazards of the profession and yet science is held up against other disciplines as relatively bias-free by virtue of its data-centric approach.

Biopharma R&D has rapidly evolved over the last few years — it is more collaborative, demands greater speed to respond to competition, and challenges many notions of “conventional” drug discovery. In my reflections, I was curious whether this rapid evolution was a harbinger of biases not conventionally associated with science — and wanted to understand how we at twoXAR aim to stay aware and ahead of such biases.