Goto

Collaborating Authors

How big data and machine learning is revolutionising biological research

#artificialintelligence

Once the three-billion-letter-long human genome was sequenced, we rushed into a new'omics' era of biological research. Scientists are now racing to sequence the genomes (all the genes) or proteomes (all the proteins) of various organisms – and in the process are compiling massive amounts of data. For instance, a scientist can use'omics' tools such as DNA sequencing to tease out which human genes are affected in a viral flu infection. But because the human genome has at least 25,000 genes in total, the number of genes altered even under such a simple scenario could potentially be in the thousands. Although sequencing and identifying genes and proteins gives them a name and a place, it doesn't tell us what they do.


Cleveland Clinic to use IBM Watson for Genomic Research - Decide Software

#artificialintelligence

Cleveland Clinic to use IBM Watson for Genomic Research: Researchers at Cleveland Clinic will use IBM Watson technology in the area of genomic research to help oncologists deliver personalized medicine by uncovering new cancer treatment options for patients. The Lerner Research Institute's Genomic Medicine Institute at Cleveland Clinic plans to evaluate Watson's ability to help oncologists develop more personalized care to patients for a variety of cancers. Clinicians lack the tools and time required to bring DNA-based treatment options to their patients and to do so, they must correlate data from genome sequencing to reams of medical journals, new studies and clinical records. At a time when medical information is doubling every five years, a faster option is needed. This use of Watson aims to find the "needle in the haystack" through identifying patterns in genome sequencing and medical data to unlock insights that will help clinicians bring the promise of genomic medicine to their patients.


Update on Artificial Intelligence and Healthcare

#artificialintelligence

In a recent interview, AthenaHealth CEO Jonathan Bush noted the limitations of traditional doctors and said, "The human is wrong so freaking often, it's a massacre." "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." Stephen Hawking has said the development of full Artificial Intelligence (AI) could spell the end of the human race – and Elon Musk agreed. In a recent interview, AthenaHealth CEO Jonathan Bush noted the limitations of traditional doctors and said, "The human is wrong so freaking often, it's a massacre." "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries."


An executive's guide to cognitive computing

#artificialintelligence

Sethi describes a cognitive analytics application in a health care setting: Imagine you walk into the emergency room with red eyes and a fever. Cognitive systems in a triage room can analyze your vitals, correlate them with your medical and travel histories, and predict with accuracy whether you have the common flu, the Zika virus or some other illness. As this health care example illustrates, cognitive technologies are able to understand the world around us, read signs and understand what's happening – but in a highly focused context to complete a narrow but important task. "The goal of many cognitive systems is to provide assistance to humans without human assistance," says Schabenberger. "But it is important to think about who is being assisted by automated systems." In the health care example above, the doctor and nurse are being assisted as much as the patient.


Machine learning and systems genomics approaches for multi-omics data

#artificialintelligence

Multiple predictive models are generated by using various multi-omics data types; then a final predictive model is generated by using the multiple models. Predictive models can be consolidated from various multi-omics data types, and each data type can be gathered from a various set of patients with same phenotype. Multiple data matrices of different multi-omics data types are incorporated into a large input matrix; then a predictive model is generated by using the large input matrix. It is fairly easy to leverage various machine learning methods for analyzing continuous or categorical data once a large input matrix is formed. It may be challenging to combine a large input matrix. Datasets for various multi-omics data types are first converted into intermediate forms, which are united into a large input matrix; then a predictive model is generated by using the large input matrix. Unique variables such as patient identifiers can be used to link multi-omics data types and integrate a variety of continuous or categorical data values. It may be challenging to transform into intermediate forms.