1,351 research outputs found

    Inductive queries for a drug designing robot scientist

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    It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

    Context-aware visual exploration of molecular databases

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    Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

    Fast training of self organizing maps for the visual exploration of molecular compounds

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    Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions

    Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics

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    The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research

    Fractional norms and quasinorms do not help to overcome the curse of dimensionality

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    The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using of the Manhattan distance and even fractional quasinorms lp (for p less than 1) can help to overcome the curse of dimensionality in classification problems. In this study, we systematically test this hypothesis. We confirm that fractional quasinorms have a greater relative contrast or coefficient of variation than the Euclidean norm l2, but we also demonstrate that the distance concentration shows qualitatively the same behaviour for all tested norms and quasinorms and the difference between them decays as dimension tends to infinity. Estimation of classification quality for kNN based on different norms and quasinorms shows that a greater relative contrast does not mean better classifier performance and the worst performance for different databases was shown by different norms (quasinorms). A systematic comparison shows that the difference of the performance of kNN based on lp for p=2, 1, and 0.5 is statistically insignificant

    Sobiva omaduste profiiliga ühendite tuvastamine keemiliste struktuuride andmekogudest

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    Keemiliste ühendite digitaalsete andmebaaside kasutuselevõtuga kaasneb vajadus leida neist arvutuslikke vahendeid kasutades sobivate omadustega molekule. Probleem on eriti huvipakkuv ravimitööstuses, kus aja- ja ressursimahukate katsete asendamine arvutustega, võimaldab märkimisväärset säästu. Kuigi tänapäevaste arvutusmeetodite piiratud võimsuse tõttu ei ole lähemas tulevikus võimalik kogu ravimidisaini protsessi algusest lõpuni arvutitesse ümber kolida, on lugu teine, kui vaadelda suuri andmekogusid. Arvutusmeetod, mis töötab teadaoleva statistilise vea piires, visates välja mõne sobiva ühendi ja lugedes mõni ekslikult aktiivseks, tihendab lõppkokkuvõttes andmekomplekti tuntaval määral huvitavate ühendite suhtes. Seetõttu on ravimiarenduse lihtsamate ja vähenõudlikkumade etappide puhul, nagu juhtühendite või ravimikandidaatide leidmine, edukalt võimalik rakendada arvutuslikke vahendeid. Selline tegevus on tuntud virtuaalsõelumisena ning käesolevasse töösse on sellest avarast ja kiiresti arenevast valdkonnast valitud mõningad suunad, ning uuritud nende võimekust ja tulemuslikkust erinevate projektide raames. Töö tulemusena on valminud arvutusmudelid teatud tüüpi ühendite HIV proteaasi vastase aktiivsuse ja tsütotoksilisuse hindamiseks; koostatud uus sõelumismeetod; leitud potentsiaalsed ligandid HIV proteaasile ja pöördtranskriptaasile; ning kokku pandud farmakokineetiliste filtritega eeltöödeldud andmekomplekt – mugav lähtepositsioon edasisteks töödeks.With the implementation of digital chemical compound libraries, creates the need for finding compounds from them that fit the desired profile. The problem is of particular interest in drug design, where replacing the resource-intensive experiments with computational methods, would result in significant savings in time and cost. Although due to the limitations of current computational methods, it is not possible in foreseeable future to transfer all of the drug development process into computers, it is a different story with large molecular databases. An in silico method, working within a known error margin, is still capable of significantly concentrating the data set in terms of attractive compounds. That allows the use of computational methods in less stringent steps of drug development, such as finding lead compounds or drug candidates. This approach is known as virtual screening, and today it is a vast and prospective research area comprising of several paradigms and numerous individual methods. The present thesis takes a closer look on some of them, and evaluates their performance in the course of several projects. The results of the thesis include computational models to estimate the HIV protease inhibition activity and cytotoxicity of certain type of compounds; a few prospective ligands for HIV protease and reverse transcriptase; pre-filtered dataset of compounds – convenient starting point for subsequent projects; and finally a new virtual screening method was developed

    Prediction of Metabolic Pathways Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

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    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations
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