ENTRIES TAGGED "medicine"
Fit2Cure taps the public's visual skills to match compounds to targets
In the inspiring tradition of Foldit, the game for determining protein shapes, Fit2Cure crowdsources the problem of finding drugs that can cure the many under-researched diseases of developing countries. Fit2Cure appeals to the player’s visual–even physical–sense of the world, and requires much less background knowledge than Foldit.
There about 7,000 rare diseases, fewer than 5% of which have cures. The number of people currently engaged in making drug discoveries is by no means adequate to study all these diseases. A recent gift to Harvard shows the importance that medical researchers attach to filling the gap. As an alternative approach, abstracting the drug discovery process into a game could empower thousands, if not millions, of people to contribute to this process and make discoveries in diseases that get little attention to scientists or pharmaceutical companies.
The biological concept behind Fit2Cure is that medicines have specific shapes that fit into the proteins of the victim’s biological structures like jig-saw puzzle pieces (but more rounded). Many cures require finding a drug that has the same jig-saw shape and can fit into the target protein molecule, thus preventing it from functioning normally.
How the field of genetics is using data within research and to evaluate researchers
Editor’s note: Earlier this week, Part 1 of this article described Sage Bionetworks, a recent Congress they held, and their way of promoting data sharing through a challenge.
Data sharing is not an unfamiliar practice in genetics. Plenty of cell lines and other data stores are publicly available from such places as the TCGA data set from the National Cancer Institute, Gene Expression Omnibus (GEO), and Array Expression (all of which can be accessed through Synapse). So to some extent the current revolution in sharing lies not in the data itself but in critical related areas.
First, many of the data sets are weakened by metadata problems. A Sage programmer told me that the famous TCGA set is enormous but poorly curated. For instance, different data sets in TCGA may refer to the same drug by different names, generic versus brand name. Provenance–a clear description of how the data was collected and prepared for use–is also weak in TCGA.
In contrast, GEO records tend to contain good provenance information (see an example), but only as free-form text, which presents the same barriers to searching and aggregation as free-form text in medical records. Synapse is developing a structured format for presenting provenance based on the W3C’s PROV standard. One researcher told me this was the most promising contribution of Synapse toward the shared used of genetic information.
Observations from Sage Congress and collaboration through its challenge
The glowing reports we read of biotech advances almost cause one’s brain to ache. They leave us thinking that medical researchers must command the latest in all technological tools. But the engines of genetic and pharmaceutical innovation are stuttering for lack of one key fuel: data. Here they are left with the equivalent of trying to build skyscrapers with lathes and screwdrivers.
Sage Congress, held this past week in San Francisco, investigated the multiple facets of data in these field: gene sequences, models for finding pathways, patient behavior and symptoms (known as phenotypic data), and code to process all these inputs. A survey of efforts by the organizers, Sage Bionetworks, and other innovations in genetic data handling can show how genetics resembles and differs from other disciplines.
An intense lesson in code sharing
At last year’s Congress, Sage announced a challenge, together with the DREAM project, intended to galvanize researchers in genetics while showing off the growing capabilities of Sage’s Synapse platform. Synapse ties together a number of data sets in genetics and provides tools for researchers to upload new data, while searching other researchers’ data sets. Its challenge highlighted the industry’s need for better data sharing, and some ways to get there.
In which the question of whether research subjects have any rights to their data is pondered.
The GET (Genomes, Environments and Traits) conference is a confluence of parties interested in the advances being made in human genomes, the measurement of how the environment impacts individuals, and how the two come together to produce traits. Sponsored by the organizers of the Personal Genome Project (PGP) at Harvard, it is a two-day event whose topics range from the appropriate amount of access that patients should have to their genetics data to the ways that Hollywood can be convinced to portray genomics more accurately.
It also is a yearly meeting place for the participants in the Personal Genome Project (one of whom is your humble narrator), people who have agreed to participate in an “open consent” research model. Among other things, this means that PGP participants agree to let their cell lines be used for any purposes (research or commercial). They also acknowledge ahead of time that because their genomes and phenotypic traits are being released publicly, there is a high likelihood that interested parties may be able to identify them from their data. The long term goal of the PGP is to enroll 100,000 participants and perform whole genome sequencing of their DNA, they currently have nearly 2,300 enrolled participants and have sequenced around 165 genomes.
Michael Italia on making use of data collected in health care settings.
Michael Italia from Children's Hospital of Philadelphia discusses the tools and methods his team uses to manage health care data.
A merging of artificial intelligence and healthcare is tougher than many realize.
People will eventually get better care from artificial intelligence, but for now, we should keep the algorithms focused on the data that we know is good and keep the doctors focused on the patients.