With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

The app boom is over. There are now more than 4.2 million apps available for Android and iOS, but three-quarters of American smartphone users download a grand total of zero new apps per month. They might be mostly free and easy to access, but apps are struggling to make it on to our phones and tablets. According to comScore, we spend the majority of our screen time using just three apps, with the average American spending almost half their time in just one. With the eyeballs of the world glued to WhatsApp, Facebook Messenger, WeChat and Skype, developers have started turning once-simple chat apps into complex ecosystems. And at the centre of this change is a horde of subservient bots. You've just walked into your kitchen after a long week. "Play Etta James," you say.

I would add HDT, Jackknife regression, density estimation, attribution modeling (to optimize marketing mix), linkage (in fraud detection), indexation (to create taxonomies or for clustering large data sets consisting of text), bucketisation, and time series algorithms.

Google and Microsoft redefined the definition of search through messaging technology like chatbots and apps for mobile and desktop by making a case that it can reach across a brand's or retailer's Web site to find and return information on consumer queries. Messaging creates a new form of search advertising simply by returning information based on chatbot queries. Last week at the Bing Ads Next event in Redmond, Washington, Microsoft demonstrated how companies like airline ticket site Skyscanner, as well as Delta Airlines in a demo, use a chatbot by pulling information from its Web site to answer questions. Microsoft also demonstrated a chatbot that serves up on bing.com in search results. A restaurant called Moksha is testing the technology in the Redmond, Washington area.

Campaign rhetoric will calm down. People will return to their daily lives and America will go back to focusing on its future. Trump's victory, largely led by the voices that got ignored by the previous administrations, provides a clear mandate for the Trump administration. The minor problem: when it comes to technology, the Trump mandate is mostly silent. The major problem:technology strategy by the Trump administration can be at odds with the low-to-medium skilled job creation promise on one hand or lead to decline in American competitiveness on the other hand.

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Misha Denil and her colleagues from the University of California, Berkeley announced that they have trained an AI to learn the "physical properties" of objects by interacting with them virtually. This includes numerous aspects of the world, including questions such as "Can I sit on this?" or "Is it squishy?" In their paper, the AI systems were experimented in two environments. The first involved introducing five blocks arranged in a tower. Others were stuck together to make larger blocks, while others did not.

At the University Hospital of Giessen and Marburg 6,000 patients are waiting for a diagnosis of their rare conditions. Most patients have spent years bouncing from one doctor to another, building up huge dossiers of medical notes. Rare diseases typically take at least five years to correctly name, and sometimes up to 30, by which time it can be too late for effective treatment. "This is an inefficient, costly business," Dr Jurgen Schafer, who heads the German university's medical team, said at a media conference at IBM Zurich in October. "The computer is not going to replace the physician. But with this amount of data, it is completely clear that we don't need more physicians – we need more computer power."

In this industry, it's a tired old cliche to say that we're building the future. The proliferation of personal computers, laptops, and cell phones has changed our lives, but by replacing or augmenting systems that were already in place. Email supplanted the post office; online shopping replaced the local department store; digital cameras and photo sharing sites such as Flickr pushed out film and bulky, hard-to-share photo albums. AI presents the possibility of changes that are fundamentally more radical: changes in how we work, how we interact with each other, how we police and govern ourselves. Fear of a mythical "evil AI" derived from reading too much SciFi won't help.

Just in time for Halloween, researchers at Data61 and MIT Media lab have created a deep learning algorithm to generate disturbing imagery. There are two parts to the Nightmare Machine project – Haunted Places and Haunted Faces – which are each terrifying and impressive in equal measure. For Haunted Places the team used algorithms to learn what it called a'nightmarifying' process, learning a variety of spooky artistic styles that can then be applied to idyllic imagery. "We use deep learning algorithms to learn first how haunted houses, then ghost towns, and more recently toxic cities look," explains principal research scientist at Data61, Manuel Cebrian. "Then, we apply the learned style to famous landmarks. It's surprising how well the algorithm can extract the element from the "scary" templates and plant it into the landmarks."