Building Biology with Machine Learning GEN Genetic Engineering & Biotechnology News - Biotech from Bench to Business GEN


The tech world has embraced Machine Learning (ML) for its powerful intuitive capabilities--to increase click-through rates on ads, sell more books, and help you keep in touch with mom. Despite being increasingly common as a classification tool in applications ranging from transcriptomics, metabolomics, and neuronal synaptic activities, ML is still almost absent in the area of bioengineering. Why is that and what can we do to increase ML use in bioengineering? Machine Learning algorithms that date back half a century are now commonly used for pattern-based analysis, including Decision Trees, Nearest Neighbors, Neural Nets, and more recently with significant success Deep Learning--a version of Neural Net with more layers and more nodes--received significant attention when it won against the best human in the ancient Chinese game of Go. Deep Learning has been enabled by access to new powerful computational hardware, in particular the graphical processing units (GPUs) originally developed for the gaming industry.

Stephen Hawking - Wikipedia


Stephen William Hawking CH CBE FRS FRSA (8 January 1942 – 14 March 2018)[14][15] was an English theoretical physicist, cosmologist, author and Director of Research at the Centre for Theoretical Cosmology within the University of Cambridge.[16][17] His scientific works included a collaboration with Roger Penrose on gravitational singularity theorems in the framework of general relativity and the theoretical prediction that black holes emit radiation, often called Hawking radiation. Hawking was the first to set out a theory of cosmology explained by a union of the general theory of relativity and quantum mechanics. He was a vigorous supporter of the many-worlds interpretation of quantum mechanics.[18][19] Hawking was an Honorary Fellow of the Royal Society of Arts (FRSA), a lifetime member of the Pontifical Academy of Sciences, and a recipient of the Presidential Medal of Freedom, the highest civilian award in the United States. In 2002, Hawking was ranked number 25 in the BBC's poll of the 100 Greatest Britons. He was the Lucasian Professor of Mathematics at the University of Cambridge between 1979 and 2009 and achieved commercial success with works of popular science in which he discusses his own theories and cosmology in general. His book, A Brief History of Time, appeared on the British Sunday Times best-seller list for a record-breaking 237 weeks. Hawking had a rare early-onset slow-progressing form of motor neurone disease (also known as amyotrophic lateral sclerosis and Lou Gehrig's disease), that gradually paralysed him over the decades.[20][21] Even after the loss of his speech, he was still able to communicate through a speech-generating device, initially through use of a hand-held switch, and eventually by using a single cheek muscle. Hawking was born on 8 January 1942[22] in Oxford to Frank (1905–1986) and Isobel Hawking (née Walker; 1915–2013).[23][24] Despite their families' financial constraints, both parents attended the University of Oxford, where Frank read medicine and Isobel read Philosophy, Politics and Economics.[24] The two met shortly after the beginning of the Second World War at a medical research institute where Isobel was working as a secretary and Frank was working as a medical researcher.[24][26] They lived in Highgate; but, as London was being bombed in those years, Isobel went to Oxford to give birth in greater safety.[27] Hawking had two younger sisters, Philippa and Mary, and an adopted brother, Edward.[28] In 1950, when Hawking's father became head of the division of parasitology at the National Institute for Medical Research, Hawking and his family moved to St Albans, Hertfordshire.[29][30]

This Artificial Intelligence was 92% Accurate in Breast Cancer Detection Contest


A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed a way to train artificial intelligence to read and interpret pathology images. Scientists tested the artificial intelligence (AI) during a competition at the annual International Symposium of Biomedical Imaging, where it was tasked to look for breast cancer in images of lymph nodes. It turns out it can detect breast cancer accurately 92 percent of the time and won in two separate categories during the contest. Andrew Beck from BIDMC says they used the deep learning method, which is commonly used to train AI to recognize speech, images and objects. They fed the machine with hundreds of slides marked to indicate which parts have cancerous cells and which have normal ones.

Simultaneous Influencing and Mapping for Health Interventions

AAAI Conferences

Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches.