With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems biology and drug discovery, we propose a deep learning framework for conditional de novo design of antiviral candidate drugs tailored against given protein targets. First, we train a multimodal ligand--protein binding affinity model on predicting affinities of antiviral compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator (consisting of two VAEs), we showcase a framework that navigates the chemical space toward regions with more antiviral molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep RL, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling more binding ligands, with an average increase of 83% comparing to an unbiased VAE. We present a case-study on a potential Envelope-protein inhibitor and perform a synthetic accessibility assessment of the best generated molecules is performed that resembles a viable roadmap towards a rapid in-vitro evaluation of potential SARS-CoV-2 inhibitors.

To explore heterogeneous information networks (HINs), network representation learning (NRL) is proposed, which represents a network in a low-dimension space. Recently, graph neural networks (GNNs) have drawn a lot of attention which are very expressive for mining a HIN, while they suffer from low efficiency issue. In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN. Unlike traditional GNNs that have to train the whole model for each downstream task, PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness. Specifically, in pre-training phase, we first use a ranking-based BFS strategy to form the input node sequence. Then inspired by BERT, we adopt deep bi-directional transformer encoders to train the model, which is a variant of GNN aggregator that is more powerful than traditional deep neural networks like CNN and LSTM. The model is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction (ANP). Additionally, we leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification and node clustering. We use node sequence pairs as input for link prediction and similarity search, and a single node sequence as input for node classification and clustering. The experimental results of the above tasks on four real-world datasets verify the advancement of PF-HIN, as it outperforms state-of-the-art alternatives consistently and significantly.

With the increasing adoption of Deep Learning (DL) for critical tasks, such as autonomous driving, the evaluation of the quality of systems that rely on DL has become crucial. Once trained, DL systems produce an output for any arbitrary numeric vector provided as input, regardless of whether it is within or outside the validity domain of the system under test. Hence, the quality of such systems is determined by the intersection between their validity domain and the regions where their outputs exhibit a misbehaviour. In this paper, we introduce the notion of frontier of behaviours, i.e., the inputs at which the DL system starts to misbehave. If the frontier of misbehaviours is outside the validity domain of the system, the quality check is passed. Otherwise, the inputs at the intersection represent quality deficiencies of the system. We developed DeepJanus, a search-based tool that generates frontier inputs for DL systems. The experimental results obtained for the lane keeping component of a self-driving car show that the frontier of a well trained system contains almost exclusively unrealistic roads that violate the best practices of civil engineering, while the frontier of a poorly trained one includes many valid inputs that point to serious deficiencies of the system.

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The project and concepts exposed were good, But more explanations of libraries and comments for some lines of code will be welcome. Despite these, I learned a lot.

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively.

In a general sense, synthetic data is information that is artificially generated, as opposed to collected from the real-world. But it's important to note that synthetic data is used for a variety of purposes. As a result, there are several types of synthetic data with different properties depending on the use-cases. For instance, one scenario in which companies use synthetic data is the training of AI/ML models. Real-world data is sometimes expensive to collect, or simply hard to come by.

It's acceptable that Natural Language Processing, or NLP, is one of the most significant and demanded technologies of the present world. You can think that it's everywhere as individuals communicate nearly everything in language: it is available in web searches, advertisement, emails, customer service, language translation, summaries, etc. These days, with understanding that processing complex expressions is a significant part of artificial intelligence, deep learning approaches have gotten superior across various NLP tasks. Let's look at some of the top NLP courses which you can study at home in your comfort – Online! This course covers a wide scope of tasks in Natural Language Processing from essential to cutting-edge: sentiment analysis, summarization, dialogue state tracking, to give some examples.

Introduction to Machine Learning This class will teach you the end-to-end process of investigating data through a machine learning lens. This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more). Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge.