Search results for "decision tree"
showing 10 items of 170 documents
Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree
2021
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation p…
In vivo models and decision trees for formulation development in early drug development: A review of current practices and recommendations for biopha…
2018
The ability to predict new chemical entity performance using in vivo animal models has been under investigation for more than two decades. Pharmaceutical companies use their own strategies to make decisions on the most appropriate formulation starting early in development. In this paper the biopharmaceutical decision trees available in four EFPIA partners (Bayer, Boehringer Ingelheim, Bristol Meyers Squibb and Janssen) were discussed by 7 companies of which 4 had no decision tree currently defined. The strengths, weaknesses and opportunities for improvement are discussed for each decision tree. Both pharmacokineticists and preformulation scientists at the drug discovery & development interf…
EphrinB2 controls vessel pruning through STAT1-JNK3 signalling
2014
Angiogenesis produces primitive vascular networks that need pruning to yield hierarchically organized and functional vessels. Despite the critical importance of vessel pruning to vessel patterning and function, the mechanisms regulating this process are not clear. Here we show that EphrinB2, a well-known player in angiogenesis, is an essential regulator of endothelial cell death and vessel pruning. This regulation depends upon phosphotyrosine-EphrinB2 signalling repressing c-jun N-terminal kinase 3 activity via STAT1. JNK3 activation causes endothelial cell death. In the absence of JNK3, hyaloid vessel physiological pruning is impaired, associated with abnormal persistence of hyaloid vessel…
A local complexity based combination method for decision forests trained with high-dimensional data
2012
Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how thi…
Complexity of decision trees for boolean functions
2004
For every positive integer k we present an example of a Boolean function f/sub k/ of n = (/sub k//sup 2k/) + 2k variables, an optimal deterministic tree T/sub k/' for f/sub k/ of complexity 2k + 1 as well as a nondeterministic decision tree T/sub k/ computing f/sub k/. with complexity k + 2; thus of complexity about 1/2 of the optimal deterministic decision tree. Certain leaves of T/sub k/ are called priority leaves. For every input a /spl isin/ {0, 1}/sup n/ if any of the parallel computation reaches a priority leaves then its label is f/sub k/ (a). If the priority leaves are not reached at all then the label on any of the remaining leaves reached by the computation is f/sub k/. (a).
Cholesky decomposition techniques in electronic structure theory
2011
We review recently developed methods to efficiently utilize the Cholesky decomposition technique in electronic structure calculations. The review starts with a brief introduction to the basics of the Cholesky decomposition technique. Subsequently, examples of applications of the technique to ab inito procedures are presented. The technique is demonstrated to be a special type of a resolution-of-identity or density-fitting scheme. This is followed by explicit examples of the Cholesky techniques used in orbital localization, computation of the exchange contribution to the Fock matrix, in MP2, gradient calculations, and so-called method specific Cholesky decomposition. Subsequently, examples o…
Learning Improved Feature Rankings through Decremental Input Pruning for Support Vector Based Drug Activity Prediction
2010
The use of certain machine learning and pattern recognition tools for automated pharmacological drug design has been recently introduced. Different families of learning algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical properties and pharmacological activities to certain kinds of representations of the candidate compounds. The purpose of this work, is to select an appropriate feature ordering from a large set of molecular descriptors usually used in the domain of Drug Activity Characterization. To this end, a new input pruning method is introduced and assessed with respect to commonly used feature ranking algorithms.
The predictive power of game-related statistics for the final result under the rule changes introduced in the men’s world water polo championship: a …
2019
The objectives of this study were (i) to compare water polo game-related statistics by match outcome (winning and losing teams) after the application of the new rules, and (ii) to develop a classif...
Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix
2020
In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. The last years have seen a remarkable flowering of works about the use of decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and intuitive, but they are very unstable: small perturbations bring big changes. This is the reason why it could be necessary to use more stable procedures in order to clustering ranking data. In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be proposed in order to extract useful info…
Entropy-Based Classifier Enhancement to Handle Imbalanced Class Problem
2017
The paper presents a possible enhancement of entropy-based classifiers to handle problems, caused by the class imbalance in the original dataset. The proposed method was tested on synthetic data in order to analyse its robustness in the controlled environment with different class proportions. As also the proposed method was tested on the real medical data with imbalanced classes and compared to the original classification algorithm results. The medical field was chosen for testing due to frequent situations with uneven class ratios.