FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools

Discovering new drugs for disease treatment is challenging, requiring a multidisciplinary effort as well as time, and resources. With a view to improving hit discovery and lead compound identification, machine learning (ML) approaches are being increasingly used in the decision-making process. Although a number of ML-based studies have been published, most studies only report fragments of the wider range of bioactivities wherein each model typically focuses on a particular disease. This study introduces FP-MAP, an extensive atlas of fingerprint-based prediction models that covers a diverse range of activities including neglected tropical diseases (caused by viral, bacterial and parasitic pathogens) as well as other targets implicated in diseases such as Alzheimer’s. To arrive at the best predictive models, performance of ≈4,000 classification/regression models were evaluated on different bioactivity data sets using 12 different molecular fingerprints. The best performing models that achieved test set AUC values of 0.62–0.99 have been integrated into an easy-to-use graphical user interface that can be downloaded from https://gitlab.com/vishsoft/fpmap

Performance Analytics of Cloud Networks

As the world becomes more inter-connected and dependent on the Internet, networks become ever more pervasive, and the stresses placed upon them more demanding. Similarly, the expectations of networks to maintain a high level of performance have also increased. Network performance is highly important to any business that operates online, depends on web traffic, runs any part of their infrastructure in a cloud environment, or even hosts their own network infrastructure. Depending upon the exact nature of a network, whether it be local or wide-area, 10 or 100 Gigabit, it will have distinct performance characteristics and it is important for a business or individual operating on the network to understand those performance characteristics and how they affect operations. To better understand our networks, it is necessary that we test them to measure their performance capabilities and track these metrics over time. In our work, we provide an in-depth analysis of how best to run cloud benchmarks to increase our network intelligence and how we can use the results of those benchmarks to predict future performance and identify performance anomalies. To achieve this, we explain how to effectively run cloud benchmarks and propose a scheduling algorithm for running large numbers of cloud benchmarks daily. We then use the performance data gathered from this method to conduct a thorough analysis of the performance characteristics of a cloud network, train neural networks to forecast future throughput based on historical results and detect performance anomalies as they occur

Anomaly detection and dynamic decision making for stochastic systems

Thesis (Ph.D.)--Boston UniversityThis dissertation focuses on two types of problems, both of which are related to systems with uncertainties. The first problem concerns network system anomaly detection. We present several stochastic and deterministic methods for anomaly detection of networks whose normal behavior is not time-varying. Our methods cover most of the common techniques in the anomaly detection field. We evaluate all methods in a simulated network that consists of nominal data, three flow-level anomalies and one packet-level attack. Through analyzing the results, we summarize the advantages and the disadvantages of each method. As a next step, we propose two robust stochastic anomaly detection methods for networks whose normal behavior is time-varying. We develop a procedure for learning the underlying family of patterns that characterize a time-varying network. This procedure first estimates a large class of patterns from network data and then refines it to select a representative subset. The latter part formulates the refinement problem using ideas from set covering via integer programming. Then we propose two robust methods, one model-free and one model-based, to evaluate whether a sequence of observations is drawn from the learned patterns. Simulation results show that the robust methods have significant advantages over the alternative stationary methods in time-varying networks. The final anomaly detection setting we consider targets the detection of botnets before they launch an attack. Our method analyzes the social graph of the nodes in a network and consists of two stages: (i) network anomaly detection based on large deviations theory and (ii) community detection based on a refined modularity measure. We apply our method on real-world botnet traffic and compare its performance with other methods. The second problem considered by this dissertation concerns sequential decision mak- ings under uncertainty, which can be modeled by a Markov Decision Processes (MDPs). We focus on methods with an actor-critic structure, where the critic part estimates the gradient of the overall objective with respect to tunable policy parameters and the actor part optimizes a policy with respect to these parameters. Most existing actor- critic methods use Temporal Difference (TD) learning to estimate the gradient and steepest gradient ascent to update the policies. Our first contribution is to propose an actor-critic method that uses a Least Squares Temporal Difference (LSTD) method, which is known to converge faster than the TD methods. Our second contribution is to develop a new Newton-like actor-critic method that performs better especially for ill-conditioned problems. We evaluate our methods in problems motivated from robot motion control

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202