Automatic selection of molecular descriptors using random forest: Application to drug discovery

Cano, Gaspar; García-Rodríguez, José; García-García, Alberto; Pérez‐Sánchez, Horacio; Benediktsson, Jón Atli; Thapa, Anil; Barr, Alastair J.

doi:10.1016/j.eswa.2016.12.008

Cited by 110 publications

(58 citation statements)

References 31 publications

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“…RFs have been employed in a wide variety of classification and prediction problems (Scornet et al, 2015;Cano et al, 2017) as they are among the most effective computationallyintensive algorithms to extract information from unstable estimates (Scornet et al, 2015). They are especially well suited for large, high-dimensional datasets, where problem complexity and scale render direct discovery of a good model in a single step impossible (Büchlmann and Yu, 2002;Kleiner et al, 2014;Wager et al, 2014).…”

Section: Methodology Random Forestsmentioning

confidence: 99%

“…where c is the number of classes and p (i|t), p j|t are the estimated probabilities of classes i, j at node t (Cano et al, 2017). In this context, Mean Decrease Gini (MDG) aggregates the Gini gain over all splits and trees to assess the classifying capacity of a variable (Friedman et al, 2009) and is thus a metric of the homogeneity of nodes and leaves in the RF (Bluemke and Stepień, 2016).…”

Section: Evaluation Criteriamentioning

confidence: 99%

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Spatial Downscaling of Alien Species Presences Using Machine Learning

2017

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Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. RF results are processed to estimate variable importance and model performance. The method is tested with an ∼8 × 8 km 2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16 × 16 km 2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Uncertainty assessment of the spatial downscaling of alien species' occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution. Furthermore, the RF architecture allows for tuning toward operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

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Section: Methodology Random Forestsmentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Spatial Downscaling of Alien Species Presences Using Machine Learning

2017

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“…[26], Tse et al used genetic algorithms to automatically select parameters in the design of an optimal complex Morlet wavelet filter, which was applied to bearing fault detection. In the same way, the automatic selection of chemical features for the identification of bioactive molecules in drug discovery was presented by Cano et al [27].…”

Section: Figmentioning

confidence: 98%

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

Ilunga-Mbuyamba

Aviña-Cervantes

Cepeda-Negrete

et al. 2017

Computers in Biology and Medicine

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A. y Chalopi, Claire (2017). Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Computers in Biology and

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“…For example, ECFPs 20 are one of the most widely used fingerprints. To develop DTI prediction methods, a diverse set of ML techniques are employed (together with the feature vectors generated using abovementioned descriptors) such as random forest (RF) 21,22 , support vectors machines (SVM) 22,23 , logistic regression (LR) 24 .…”

Section: Introductionmentioning

confidence: 99%

DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

Rifaioğlu¹,

Atalay²,

Martin

et al. 2018

Preprint

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The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks. One of the main advantages of DEEPScreen is employing readily available simple 2-D images of compounds at the input level instead of engineered complex feature vectors that displayed limited performance in DTI prediction tasks previously. DEEPScreen learns complex features inherently from the 2-D molecular representations, thus producing highly accurate predictions.DEEPScreen system was trained for 704 target proteins (using ChEMBL curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against shallow classifiers such as the random forest, logistic regression and support † A preliminary version of this study have been orally presented at vector machines, to indicate the effectiveness of the proposed deep learning approach. Additionally, we compared DEEPScreen with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets and showed that DEEPScreen produces better or comparable results to the top performers. The method proposed here can be employed to computationally scan a large portion of the recorded drug candidate compound and protein spaces to aid the experimentalists working in the field of drug discovery and repurposing by providing a preselection of interesting novel DTIs. of correctly predicted non-interacting compound-target pairs, whereas FP (i.e., false positive) represents the number of non-interacting compound target pairs, which are predicted as interacting.

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Automatic selection of molecular descriptors using random forest: Application to drug discovery

Cited by 110 publications

References 31 publications

Spatial Downscaling of Alien Species Presences Using Machine Learning

Spatial Downscaling of Alien Species Presences Using Machine Learning

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

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