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Publications

2020

Deep learning for drug response prediction in cancer

Authors
Baptista, D; Ferreira, PG; Rocha, M;

Publication
Briefings in Bioinformatics

Abstract
Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:mrocha@di.uminho.pt

2020

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Authors
Oliveira, J; Oliveira, PM; Boaventura Cunha, J; Pinho, T;

Publication
Robotics

Abstract
The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

2020

Extracting architectural patterns of deep neural networks for disease detection

Authors
Ferreira, MF;

Publication
Proceedings of the 35th Annual ACM Symposium on Applied Computing

Abstract

2020

Condensed Graphs: A Generic Framework for Accelerating Subgraph Census Computation

Authors
Martins, M; Ribeiro, P;

Publication
Springer Proceedings in Complexity

Abstract
Determining subgraph frequencies is at the core of several graph mining methodologies such as discovering network motifs or computing graphlet degree distributions. Current state-of-the-art algorithms for this task either take advantage of common patterns emerging on the networks or target a set of specific subgraphs for which analytical calculations are feasible. Here, we propose a novel network generic framework revolving around a new data-structure, a Condensed Graph, that combines both the aforementioned approaches, but generalized to support any subgraph topology and size. Furthermore, our methodology can use as a baseline any enumeration based census algorithm, speeding up its computation. We target simple topologies that allow us to skip several redundant and heavy computational steps using combinatorics. We were are able to achieve substantial improvements, with evidence of exponential speedup for our best cases, where these patterns represent up to 97% of the network, from a broad set of real and synthetic networks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

2020

Mach-Zehnder interferometer-based evanescent refractometer inscribed at the surface of Eagle2000 by femtosecond laser writing

Authors
Amorim, VA; Maia, JM; Viveiros, D; Marques, PVS;

Publication
IEEE Sensors Journal

Abstract

2020

A comparison of matching algorithms for kidney exchange programs addressing waiting time

Authors
Monteiro, T; Klimentova, X; Pedroso, JP; Viana, A;

Publication
Central European Journal of Operations Research

Abstract

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