RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficient for determining whether the RNAs interact with the RBPs or not. In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first encode RNA sequences into one-hot encoding. Next, we design a deep learning model with a convolutional neural network (CNN) and an attention mechanism, which automatically search for important positions, e.g. binding motifs, to learn discriminant high-level features for predicting RBP binding sites. We evaluate iDeepA on publicly gold-standard RBP binding sites derived from CLIP-seq data. The results demonstrate iDeepA achieves comparable performance with other state-of-the-art methods.

The digitalization of stored information in hospitals now allows for the exploitation of medical data in text format, as electronic health records (EHRs), initially gathered for other purposes than epidemiology. Manual search and analysis operations on such data become tedious. In recent years, the use of natural language processing (NLP) tools was highlighted to automatize the extraction of information contained in EHRs, structure it and perform statistical analysis on this structured information. The main difficulties with the existing approaches is the requirement of synonyms or ontology dictionaries, that are mostly available in English only and do not include local or custom notations. In this work, a team composed of oncologists as domain experts and data scientists develop a custom NLP-based system to process and structure textual clinical reports of patients suffering from breast cancer. The tool relies on the combination of standard text mining techniques and an advanced synonym detection method. It allows for a global analysis by retrieval of indicators such as medical history, tumor characteristics, therapeutic responses, recurrences and prognosis. The versatility of the method allows to obtain easily new indicators, thus opening up the way for retrospective studies with a substantial reduction of the amount of manual work. With no need for biomedical annotators or pre-defined ontologies, this language-agnostic method reached an good extraction accuracy for several concepts of interest, according to a comparison with a manually structured file, without requiring any existing corpus with local or new notations.

Computing accurate estimates of the Fourier transform of analog signals from discrete data points is important in many fields of science and engineering. The conventional approach of performing the discrete Fourier transform of the data implicitly assumes periodicity and bandlimitedness of the signal. In this paper, we use Gaussian process regression to estimate the Fourier transform (or any other integral transform) without making these assumptions. This is possible because the posterior expectation of Gaussian process regression maps a finite set of samples to a function defined on the whole real line, expressed as a linear combination of covariance functions. We estimate the covariance function from the data using an appropriately designed gradient ascent method that constrains the solution to a linear combination of tractable kernel functions. This procedure results in a posterior expectation of the analog signal whose Fourier transform can be obtained analytically by exploiting linearity. Our simulations show that the new method leads to sharper and more precise estimation of the spectral density both in noise-free and noise-corrupted signals. We further validate the method in two real-world applications: the analysis of the yearly fluctuation in atmospheric CO2 level and the analysis of the spectral content of brain signals.

The construction of synthetic complex-valued signals from real-valued observations is an important step in many time series analysis techniques. The most widely used approach is based on the Hilbert transform, which maps the real-valued signal into its quadrature component. In this paper, we define a probabilistic generalization of this approach. We model the observable real-valued signal as the real part of a latent complex-valued Gaussian process. In order to obtain the appropriate statistical relationship between its real and imaginary parts, we define two new classes of complex-valued covariance functions. Through an analysis of simulated chirplets and stochastic oscillations, we show that the resulting Gaussian process complex-valued signal provides a better estimate of the instantaneous amplitude and frequency than the established approaches. Furthermore, the complex-valued Gaussian process regression allows to incorporate prior information about the structure in signal and noise and thereby to tailor the analysis to the features of the signal. As a example, we analyze the non-stationary dynamics of brain oscillations in the alpha band, as measured using magneto-encephalography.

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.

Many problems in artificial intelligence require adaptively making a sequence of decisions with uncertain outcomes under partial observability. Solving such stochastic optimization problems is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse AI applications including management of sensing resources, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.

As part of the IEEE RAS International Conference on Humanoid Robots in Birmingham, U.K., last month, the awards committee decided to organize a fun photo contest. Participants submitted 39 photos showing off their humanoids in all kinds of poses and places. I was happy to be one of the judges, along with Sabine Hauert from the University of Bristol and Robohub, and with Giorgio Metta, the conference's awards chair, overseeing our selection. All photos were posted on Facebook and Twitter, and users were invited to vote on them. Sabine and I then looked at the photos with the most votes and scored them for originality, creativity, photo structure, and tech or fun factor.

In Winston's Genesis Group, which is part of MIT's Computer Science and Artificial Intelligence Laboratory, Winston and squadrons of his students have painstakingly built technology that can analyze roughly 100-line texts written for computers on subjects such as Shakespeare, international cyber conflict, and fairy tales. Genesis compares stories; detects concepts such as love or revenge, even when they are not named; concludes whether a short-term gain leads to a long-term loss; and explains acts based on personality traits. The system can even analyze a text through a filter of cultural bias, thus interpreting an event like the cyber attack on Estonia by Russia in 2007 from the point of view of people in one or the other country.

Billionaire tech entrepreneur Mark Cuban has seen a ton of change since he first got in the technology business in 1982, but he argues that artificial intelligence (AI) is going to "change everything, 180 degrees." He warns that if the U.S. allows other countries to take the lead in AI, then it'll be "SOL," an acronym that employs profanity to communicate urgency. "All these things have happened that have changed how we do business, changed how we lived our lives, changed everything, right, the internet. But what we're going to see with artificial intelligence dwarfs all of that," Cuban said in an interview with hedge fund manager J. Kyle Bass of Hayman Capital on RealVision Television, a subscription financial video service. AI is expected to soon bring an increase in productivity, resulting in fewer jobs all while the population continues to grow. "It's not a question of how it plays out over 100 years.

Science author Simon Singh is stood beside an Enigma machine, talking about the 15,354,393,600 password variants the German encryption box allows with its spaghetti of wiring, pseudo-random rotors and reconfigurable plugboard. He's talking about the top secret work at Bletchley Park to break the code - the groundwork lain by Polish mathematicians; Alan Turing's bombe; years of frustrated efforts waiting for a breakthrough. Behind him, a screen shows that an artificial intelligence has cracked it in 13 minutes. The stunt is being made by a data analysis firm. It is showing off its machine learning toolset with a live demonstration, competing with the very best in 1930s encryption.