Showing papers by "Alfonso Rodríguez-Patón published in 2020"

Computational methods for identifying the critical nodes in biological networks.

Abstract: A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.

Deep Collaborative Filtering for Prediction of Disease Genes

Abstract: Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well-established framework and its superior performance in predicting gene-disease associations. However, the IMC method does not hierarchically extract deep features, which might limit the quality of recovery. In this case, the architecture of deep learning, which obtains high-level representations and handles noises and outliers presented in large-scale biological datasets, is introduced into the side information of genes in our Deep Collaborative Filtering (DCF) model. Further, for lack of negative examples, we also exploit Positive-Unlabeled (PU) learning formulation to low-rank matrix completion. Our approach achieves substantially improved performance over other state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database. Our approach is 10 percent more efficient than standard IMC in detecting a true association, and significantly outperforms other alternatives in terms of the precision-recall metric at the top-k predictions. Moreover, we also validate the disease with no previously known gene associations and newly reported OMIM associations. The experimental results show that DCF is still satisfactory for ranking novel disease phenotypes as well as mining unexplored relationships. The source code and the data are available at https://github.com/xzenglab/DCF .

LDCNN-DTI: A Novel Light Deep Convolutional Neural Network for Drug-Target Interaction Predictions

Abstract: In computational drug discovery, accurately predicting drug-target interaction (DTI) is vital for drug repositioning and developing new drugs. With DTI data rapidly accumulated in recent years, it is recently hot to use deep learning technology to predict DTIs, but still a challenge to design light learning frameworks by using less protein descriptors. In this work, to address the challenge, a novel light deep convolutional neural network (namely LDCNN) is proposed to predict DTIs, in which a small number of protein descriptors are produced by convolving amino acid sequences of different lengths. As results, it is obtained that LDCNN can reduce the number of neurons in convolution layers and filters by 50%, with lose of AUC 1.3% and AUPR 4% comparing with DeepConv method. Our LDCNN models can give hints in designing light deep learning models for DTI prediction.

Applying Evolutionary Metaheuristics for Parameter Estimation of Individual-Based Models

Abstract: Individual-based models are complex and they have usually an elevated number of input parameters which must be tuned for reproducing the observed population data or the experimental results as accurately as possible. Thus, one of the weakest points of this modelling approach lies on the fact that rarely the modeler has the enough information about the correct values or even the acceptable range for the input parameters. Consequently, several parameter combinations must be tried to find an acceptable set of input factors minimizing the deviations of simulated and the reference dataset. In practice, most of times, it is computationally unfeasible to traverse the complete search space trying all every possible combination to find the best of set of parameters. That is precisely an instance of a combinatorial problem which is suitable for being solved by metaheuristics and evolutionary computation techniques. In this work, we introduce EvoPER, an R package for simplifying the parameter estimation using evolutionary computation methods.