Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the ...myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.
Coevolution has already produced promising results, but its dynamic evaluation can lead to a variety of problems that preventmost algorithms from progressing monotonically. An important open question ...therefore is how progress towards a chosen solution concept can be achieved. A general solution concept for coevolution is obtained by viewing opponents or tests as objectives. In this setup known as Pareto-coevolution, the desired solution is the Pareto-optimal set. We present an archive that guarantees monotonicity for this solution concept. The algorithm is called the Incremental Pareto-Coevolution Archive (IPCA), and is based on Evolutionary Multi-Objective Optimization (EMOO). By virtue of its monotonicity, IPCA avoids regress even when combined with a highly explorative generator. This capacity is demonstrated on a challenging test problem requiring both exploration and reliability. IPCA maintains a highly specific selection of tests, but the size of the test archive nonetheless grows unboundedly. We therefore furthermore investigate how archive sizes may be limited while still providing approximate reliability. The LAyered Pareto-Coevolution Archive (LAPCA) maintains a limited number of layers of candidate solutions and tests, and thereby permits a trade-off between archive size and reliability. The algorithm is compared in experiments, and found to be more efficient than IPCA. The work demonstrates how the approximation of amonotonic algorithm can lead to algorithms that are sufficiently reliable in practice while offering better efficiency.
Ideal Evaluation from Coevolution Jong, Edwin D. de; Pollack, Jordan B.
Evolutionary computation,
06/2004, Letnik:
12, Številka:
2
Journal Article
Recenzirano
In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in game-playing. Evaluation on all ...tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult problem in itself, and approximations are likely to introduce human biases into the search process. Coevolution evolves the set of tests used for evaluation, but has so far often led to inaccurate evaluation.
We show that for any set of learners, a Complete Evaluation Set can be determined that provides ideal evaluation as specified by Evolutionary Multi-Objective Optimization. This provides a principled approach to evaluation in coevolution, and thereby brings
ideal evaluation within reach. The Complete Evaluation Set is of manageable size, and progress towards it can be accurately measured. Based on this observation, an algorithm named DELPHI is developed. The algorithm is tested on problems likely to permit progress on only a subset of the underlying objectives. Where all comparison methods result in overspecialization, the proposed method and a variant achieve sustained progress in all underlying objectives. These findings demonstrate that ideal evaluation may be approximated by practical algorithms, and that accurate evaluation for test-based problems is possible even when the underlying objectives of a problem are unknown.
Background Autosomal dominant polycystic kidney disease (ADPKD) is characterized by renal tubular cell proliferation and dedifferentiation, which may influence tubular secretion of creatinine ...(CCrTS). Study Design Diagnostic test study. Setting & Participants We therefore investigated CCr(TS) in patients with ADPKD and controls and studied consequences for the performance of glomerular filtration rate (GFR) estimating equations. Index & Reference Tests In patients with ADPKD and healthy controls, we measured GFR as125 I-iothalamate clearance while simultaneously determining creatinine clearance. Other Measurements 24-hour urinary albumin excretion. Results In 121 patients with ADPKD (56% men; mean age, 40 ± 11 SD years) and 215 controls (48% men; mean age, 53 ± 10 years), measured GFR (mGFR) was 78 ± 30 and 98 ± 17 mL/min/1.73 m2 , respectively, and CCr(TS) was 15.9 ± 10.8 and 10.9 ± 10.6 mL/min/1.73 m2 , respectively ( P < 0.001). The higher CCr(TS) in patients with ADPKD remained significant after adjustment for covariates and appeared to be dependent on mGFR. Correlation and accuracy between mGFR and CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) estimated GFR (eGFR) were 0.95 and 99%, respectively; between mGFR and MDRD (Modification of Diet in Renal Disease) Study eGFR, they were 0.93 and 97%, respectively. Values for bias, precision, and accuracy were similar or slightly better than in controls. In addition, change in mGFR during 3 years of follow-up in 45 patients with ADPKD correlated well with change in eGFR. Limitations Cross-sectional, single center. Conclusions CCr(TS) in patients with ADPKD is higher than that in controls, but this effect is limited and observed at only high-normal mGFR. Consequently, the CKD-EPI and MDRD Study equations perform relatively well in estimating GFR and change in GFR in patients with ADPKD.
In heterogeneous multi agent systems, communication is hampered by the lack of shared ontologies. Ontology negotiation is a technique that enables pairs of agents to overcome these difficulties by ...exchanging parts of their ontologies. As a result of these micro level solutions, a communication vocabulary emerges on a macro level. The goal of this paper is to ensure that this communication vocabulary contains words of the right level of generality, i.e. not overspecific and not overgeneralized. We will propose a number of communication strategies that enable the agents to achieve these goals. Using experimental results, we will compare their performance.
Deep learning research over the past years has shown that by increasing the scope or difficulty of the learning problem over time, increasingly complex learning problems can be addressed. We study ...incremental learning in the context of sequence learning, using generative RNNs in the form of multi-layer recurrent Mixture Density Networks. While the potential of incremental or curriculum learning to enhance learning is known, indiscriminate application of the principle does not necessarily lead to improvement, and it is essential therefore to know which forms of incremental or curriculum learning have a positive effect. This research contributes to that aim by comparing three instantiations of incremental or curriculum learning. We introduce Incremental Sequence Learning, a simple incremental approach to sequence learning. Incremental Sequence Learning starts out by using only the first few steps of each sequence as training data. Each time a performance criterion has been reached, the length of the parts of the sequences used for training is increased. We introduce and make available a novel sequence learning task and data set: predicting and classifying MNIST pen stroke sequences. We find that Incremental Sequence Learning greatly speeds up sequence learning and reaches the best test performance level of regular sequence learning 20 times faster, reduces the test error by 74%, and in general performs more robustly; it displays lower variance and achieves sustained progress after all three comparison methods have stopped improving. The other instantiations of curriculum learning do not result in any noticeable improvement. A trained sequence prediction model is also used in transfer learning to the task of sequence classification, where it is found that transfer learning realizes improved classification performance compared to methods that learn to classify from scratch.
Various multi--objective evolutionary algorithms (MOEAs) have obtained promising results on various numerical multi--objective optimization problems. The combination with gradient--based local search ...operators has however been limited to only a few studies. In the single--objective case it is known that the additional use of gradient information can be beneficial. In this paper we provide an analytical parametric description of the set of all non--dominated (i.e. most promising) directions in which a solution can be moved such that its objectives either improve or remain the same. Moreover, the parameters describing this set can be computed efficiently using only the gradients of the individual objectives. We use this result to hybridize an existing MOEA with a local search operator that moves a solution in a randomly chosen non--dominated improving direction. We test the resulting algorithm on a few well--known benchmark problems and compare the results with the same MOEA without local search and the same MOEA with gradient--based techniques that use only one objective at a time. The results indicate that exploiting gradient information based on the non--dominated improving directions is superior to using the gradients of the objectives separately and that it can furthermore improve the result of MOEAs in which no local search is used, given enough evaluations.
Driven by the recent advances in deep learning methods and, in particular, by the development of modern self-supervised learning algorithms, increased interest and efforts have been devoted to build ...foundation models (FMs) for medical images. In this work, we present our scalable training pipeline for large pathology imaging data, and a comprehensive analysis of various hyperparameter choices and training techniques for building pathology FMs. We release and make publicly available the first batch of our pathology FMs (https://github.com/kaiko-ai/towards_large_pathology_fms) trained on open-access TCGA whole slide images, a commonly used collection of pathology images. The experimental evaluation shows that our models reach state-of-the-art performance on various patch-level downstream tasks, ranging from breast cancer subtyping to colorectal nuclear segmentation. Finally, to unify the evaluation approaches used in the field and to simplify future comparisons of different FMs, we present an open-source framework (https://github.com/kaiko-ai/eva) designed for the consistent evaluation of pathology FMs across various downstream tasks.
DECA de Jong, Edwin D.; Bucci, Anthony
Genetic And Evolutionary Computation Conference: Proceedings of the 8th annual conference on Genetic and evolutionary computation; 08-12 July 2006,
07/2006
Conference Proceeding
Coevolution has often been based on averaged outcomes, resulting in unstable evaluation. Several theoretical approaches have used archives to provide stable evaluation. However, the number of tests ...required by some of these approaches can be prohibitive of practical applications. Recent work has shown the existence of a set of underlying objectives which compress evaluation information into a potentially small set of dimensions. We consider whether these underlying objectives can be approximated online, and used for evaluation in a coevolution algorithm. The Dimension Extracting Coevolutionary Algorithm (DECA) is compared to several recent reliable coevolution algorithms on a Numbers game problem, and found to perform efficiently. Application to the more realistic Tartarus problem is shown to be feasible. Implications for current coevolution research are discussed.
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