Pharmaceuticals & Biotechnology

Semantic graph database underpins healthcare data lake


Franz Inc., in partnership with Montefiore Health System, is bringing the data lake to health IT using Franz's semantic graph database technology. Until its venture into the healthcare and pharmaceutical industries over the past few years, the 31-year-old Oakland, Calif., company had done business mainly in the worlds of national defense and intelligence, into which it sold its artificial intelligence-based triple store database that uses semantic, instead of relational, database technology. Using RDF technology, triple stores are a way to manage, manipulate and query many triples. Unlike most relational databases' linear representation and analysis of data, Franz's semantic graph database technology employs visual and spatial charting with which users can graphically see data elements and their relationships.

Gene-editing technology developer Feng Zhang awarded $500,000 Lemelson-MIT Prize

MIT News

Feng Zhang, a pioneer of the revolutionary CRISPR gene-editing technology, TAL effector proteins, and optogenics, is the recipient of the 2017 $500,000 Lemelson-MIT Prize, the largest cash prize for invention in the United States. Prior to harnessing CRISPR-Cas9, Zhang engineered microbial TAL effectors (TALEs) for use in mammalian cells, working with colleagues at Harvard University, authoring multiple publications on the subject and becoming a co-inventor on several patents on TALE-based technologies. Zhang was also a key member of the team at Stanford University that harnessed microbial opsins for developing optogenetics, which uses light signals and light-sensitive proteins to monitor and control activity in brain cells. Zhang's numerous scientific discoveries and inventions, as well as his commitment to mentorship and collaboration, earned him the Lemelson-MIT Prize, which honors outstanding mid-career inventors who improve the world through technological invention and demonstrate a commitment to mentorship in science, technology, engineering and mathematics (STEM).

Prospect of Synthetic Embryos Sparks New Bioethics Debate

MIT Technology Review

Two years ago, Shao, a mechanical engineer with a flair for biology, was working with embryonic stem cells, the kind derived from human embryos able to form any cell type. The work in Michigan is part of a larger boom in organoid research--scientists are using stem cells to create clumps of cells that increasingly resemble bits of brain, lungs, or intestine (see "10 Breakthrough Technologies: Brain Organoids"). Scientists have started seeking ways to coax stem cells to form more complicated, organized tissues, called organoids. Following guidelines promulgated last year by Kimmelman's international stem-cell society, Fu's team destroys the cells just five days after they're made.

AI and Sophia Genetics could be the key to diagnosing your illness


The Swiss company employs artificial intelligence to help doctors and other medical professionals diagnose and treat patients by way of genomic analysis. Promising to be "the most advanced artificial intelligence AI for data-driven medicine," Sophia Genetics takes the genomic profiles to improve upon diagnostic abilities across oncology, hereditary cancer, metabolic disorders, pediatrics, and cardiology. "Sophia acts as a real disruptor by breaking down the information silos in health care, meaning that the information from a patient in London or Paris can, for instance, help better diagnose and treat a patient in Lagos or Rio." "Sophia Genetics is a company at the forefront of two rapidly changing technologies: genomic medicine and artificial intelligence," Balderton Capital partner James Wise, one of Sophia Genetics' newest investors, noted.

Machine learning will bolster human expertise in every industry


"The basic idea behind machine learning is that we want to learn relationships and corelationships between the different elements of the data, whether it be recognising a face or identifying a potentially cancerous lesion on an X-ray image," says Council for Scientific and Industrial Research (CSIR) Mobile Intelligent Autonomous Systems unit principal researcher Dr Benjamin Rosman. Machine-learning algorithms are designed to determine which features best describe the data and thereby extract latent patterns, adds CSIR Mobile Intelligent Autonomous Systems unit data science senior researcher Nyalleng Moorosi. Supervised machine learning involves training the machine learning algorithm to recognise specific data characteristics and patterns, elaborates Moorosi. "Developing a supervised machine learning system requires a highly skilled expert who knows the expected output of the algorithm and a skilled algorithm writer knowledgeable in the mathematical principles underlying the algorithms," emphasises Rosman.

Deep Learning's Deepest Impact: AI Storming Through $6.5 Trillion Healthcare Industry - The Official NVIDIA Blog


That makes this year's gathering of the Medical Image Computing and Computer Assisted Interventions Society -- MICCAI 2017 -- in Quebec City, Canada, one of the best ways to understand how deep learning is improving the lives of people all around us. One sure sign of this: the number of AI-focused papers submitted by the health research community is surging. The number of AI and deep learning healthcare startups has grown more than 160 percent in the last five years, analysts estimate. Deep learning in healthcare is the leading industrial application of AI, according to venture capital tracker CB Insights, raising $1.8 billion across 270 deals since 2012.

5 important stories you may have missed

PBS NewsHour

The fatalities are largely driven by the opioid epidemic, responsible for 6 in 10 opioid deaths. China will set a deadline for automakers to stop making fuel-dependent cars, Bloomberg reported last week, making it the largest country yet to put such a restriction in place. And while people of Hispanic descent today are the fastest-growing immigrant group in the U.S., the latest numbers indicate Asians will become the country's largest immigrant group by 2055, making up 38 percent of all U.S. immigrants, compared to 31 percent who identify as Hispanic. The survey also showed 13 percent of the country's 11.1 million unauthorized immigrants were from Asia, largely from India, China, the Philippines and Korea.

Sophia Genetics raises $30 million to help doctors diagnose using AI and genomic data


Sophia Genetics, a big data analytics company that's using artificial intelligence (AI) to help medical professionals diagnose and treat patients through genomic analysis, has raised $30 million in a round of funding led by Balderton Capital, with participation from 360 Capital Partners, Invoke Capital, and Alychlo. Its platform learns from thousands of patients' genomic profiles to improve and expedite patient diagnosis across oncology, hereditary cancer, metabolic disorders, pediatrics, and cardiology. Elsewhere, cancer screening firm Guardant Health recently raised $360 million to further develop technology that helps patients avoid risky and expensive biopsies, using genomic tests that pair cancer patients with targeted therapies and clinical trials. "Sophia Genetics is a company at the forefront of two rapidly changing technologies: genomic medicine and artificial intelligence," added Balderton Capital partner James Wise.

The world's 10 smartest companies are all in the US and China


US and Chinese firms dominate the list overall, taking all the top spots. According the MIT Technology Review editors, this company earns its place among the smartest because of this and its novel work to combat haemophilia B using intravenous treatment with viruses carrying a corrected gene. California sci-tech company Kite Pharma deals in immunotherapy, engineering T-cells – one of the body's natural defenders – to fight cancer. MIT Technology Review considers Nvidia the leader in processing power for AI software, citing deals with autonomous carmakers, ensuring these smart cookies have a solid future in an emerging tech revolution.

Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome


Antibiotic resistance in pathogens is extensively studied, yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leverage genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. Using a machine learning approach, we identified genes that are predictive of an organism's direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics and predict how organisms in the gut microbiome will respond to antibiotic administration.