A better approach to disease prediction through big data analytics

#artificialintelligence

Big data holds great promise to change health care for the better. However, much of the technology that will someday transform health care and its delivery is not yet mature enough for hospitals and other systems to use. The Second IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies will bring experts from academics, business and government together to share information and help accelerate health care's transformation. This leading international conference will take place in Philadelphia this week from July 17--19. Mooi Choo Chuah, professor of computer science and engineering at Lehigh University and co-director of Lehigh's undergraduate computer engineering program, is serving as technical co-chair, along with Professor Insup Lee of the University of Pennsylvania.


Artificial intelligence could build new drugs faster than any human team

#artificialintelligence

Artificial intelligence algorithms are being taught to generate art, human voices, and even fiction stories all on their own--why not give them a shot at building new ways to treat disease? Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis. The company has invited academic and non-profit researchers from around the country to detail which diseases they're trying to generate treatments for, so AtomNet can take a shot. The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it's seen. Atomwise's system only generates potential drugs--the compounds created by the neural network aren't guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market.


Solving Large Scale Phylogenetic Problems using DCM2

AAAI Conferences

Tandy J. Warnow Department of Computer Science University of Arizona Tucson AZ USA email: tandy cs, arizona, edu Abstract In an earlier paper, we described a new method for phylogenetic tree reconstruction called the Disk Covering Method, or DCM. This is a general method which can be used with an)' existing phylogenetic method in order to improve its performance, lCre showed analytically and experimentally that when DCM is used in conjunction with polynomial time distance-based methods, it improves the accuracy of the trees reconstructed. In this paper, we discuss a variant on DCM, that we call DCM2. DCM2 is designed to be used with phylogenetic methods whose objective is the solution of NPhard optimization problems. We also motivate the need for solutions to NPhard optimization problems by showing that on some very large and important datasets, the most popular (and presumably best performing) polynomial time distance methods have poor accuracy. Introduction 118 HUSON The accurate recovery of the phylogenetic branching order from molecular sequence data is fundamental to many problems in biology. Multiple sequence alignment, gene function prediction, protein structure, and drug design all depend on phylogenetic inference. Although many methods exist for the inference of phylogenetic trees, biologists who specialize in systematics typically compute Maximum Parsimony (MP) or Maximum Likelihood (ML) trees because they are thought to be the best predictors of accurate branching order. Unfortunately, MP and ML optimization problems are NPhard, and typical heuristics use hill-climbing techniques to search through an exponentially large space. When large numbers of taxa are involved, the computational cost of MP and ML methods is so great that it may take years of computation for a local minimum to be obtained on a single dataset (Chase et al. 1993; Rice, Donoghue, & Olmstead 1997). It is because of this computational cost that many biologists resort to distance-based calculations, such as Neighbor-Joining (NJ) (Saitou & Nei 1987), even though these may poor accuracy when the diameter of the tree is large (Huson et al. 1998). As DNA sequencing methods advance, large, divergent, biological datasets are becoming commonplace. For example, the February, 1999 issue of Molecular Biology and Evolution contained five distinct datascts of more than 50 taxa, and two others that had been pruned below that.


10 Mobile Health Startups Making You Feel Better - Nanalyze

#artificialintelligence

In 1983, the IBM PC XT debuted with 128K of RAM and a 10MB hard disk. In that same year, the first mobile phone debuted weighing about 2.5 pounds and with a $4,000 price tag. Fast forward to today and the average person unlocks their smartphone 76-80 times a day and relies on it for every aspect of their lives. These amazing pieces of hardware are millions of times more capable than all of NASA's computing power in the 1960s. Now that we have a supercomputer that never leaves people's sides, maybe it's time that we do some more innovation and see how that device can be used for "mobile health".


Twitter CEO Jack Dorsey promotes fitness 'entrepreneur' who claims vaccines cause autism

The Independent - Tech

Twitter chief executive Jack Dorsey has appeared on a podcast with a controversial fitness personality who has promoted scientifically disproven claims that vaccinations cause autism. Host Ben Greenfield – who tweeted in February that "vaccines do indeed cause autism" – thanked Mr Dorsey for an "epic podcast". The Twitter boss responded: "Great conversation and appreciate all you do to simplify the mountains of research focused on increasing one's healthspan! We'll tell you what's true. You can form your own view. His appearance comes as other tech firms like Facebook and Pinterest are cracking down on anti-vaccine content on their platforms. However, Twitter claimed Mr Dorsey was unaware of the host's controversial opinions. A Twitter spokesperson told The Independent that Mr Dorsey did not know about Mr Greenfield's views on vaccinations and that his podcast appearance was not an endorsement of those beliefs. Sheen fought a legal battle against ex-wife Denise Richards to try and ...