How big data and machine learning is revolutionising biological research


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


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.

An executive's guide to cognitive computing


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.

Microsoft will 'solve' cancer within the next 10 years by treating it like a computer virus, company says

The Independent - Tech

Microsoft says it is going to "solve" cancer in the next 10 years. The company is working at treating the disease like a computer virus, that invades and corrupts the body's cells. Once it is able to do so, it will be able to monitor for them and even potentially reprogram them to be healthy again, experts working for Microsoft have said. The company has built a "biological computation" unit that says its ultimate aim is to make cells into living computers. As such, they could be programmed and reprogrammed to treat any diseases, such as cancer.

Machine learning and systems genomics approaches for multi-omics data


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.