Academic literature on the topic 'Bioinformatics server'

Il cancro rappresenta una delle principali cause di morte nel mondo, ma causa anche un enorme dispendio di risorse mediche. Sebbene negli ultimi anni siano stati sviluppati trattamenti innovativi come terapia genica, immunoterapia e trattamenti classici (cioè chemioterapia, radioterapia e rimozione chirurgica), ad oggi non esiste un trattamento efficace per curare questa patologia, anche perché i pazienti sviluppano spesso chemioresistenza, radioresistenza e alcuni resistono anche alla terapia genica e all'immunoterapia. La pelle è l’organo più grande del corpo umano. Il carcinoma a cellule basali, il carcinoma a cellule squamose e il melanoma sono i tre tumori della pelle più comuni. Il melanoma si sviluppa originariamente dai melanociti (cellule contenenti pigmento) e la sua incidenza è aumentata notevolmente negli ultimi 30 anni con una bassa sopravvivenza a cinque anni e un basso tasso di prognosi. Gli attuali approcci terapeutici del melanoma non solo potrebbero essere inefficaci, ma anche avere gravi effetti collaterali come la vitiligine. Nell'ambito dello studio del melanoma, l'attuale tesi ha studiato la soppressione del melanoma su diverse piattaforme. In primo luogo, il resveratrolo è stato utilizzato per identificare i potenziali biomarcatori del melanoma. In secondo luogo, attraverso studi precedenti e poi mediante studi in silico, alcuni biomarcatori sono stati ulteriormente valutati. Quindi sono stati studiati modelli di colture cellulari di melanoma in vitro. Abbiamo dimostrato che le cellule del melanoma erano inibite dall'interazione proteina-proteina. In terzo luogo, dopo analisi LC-MS/MS, il database delle proteine è stato utilizzato per analizzare e annotare le funzioni dei potenziali biomarcatori. Quarto, è stato riportato un raro caso di melanoma maligno amelanotico (AMM) che amplia la comprensione e integra i fonotipi del melanoma. Il resveratrolo (RSV) è un tipo di fitoalessina ampiamente distribuita nella dieta mediterranea che potrebbe agire anche da soppressore del tumore. Abbiamo valutato gli effetti dell'RSV sulle cellule di melanoma (A375) e abbiamo scoperto che l'RSV potrebbe inibire la proliferazione delle cellule di melanoma modulando il ciclo cellulare e innescando l'apoptosi; in particolare, l’espressione della Cyclin D1 e PCDH9 sono stati fortemente influenzati dalla durata del trattamento dell'RSV, mentre l’espressione di Rac1 non è stata affatto alterata. Abbiamo ulteriormente esplorato il meccanismo di questi geni mirati del melanoma. Studi in silico hanno mostrato che il PCDH9 potrebbe rappresentare il nuovo biomarcatore del melanoma. Pertanto, l'alterazione dell'espressione di PCDH9 (sovraespressione e interferenza) è stata valutata per esplorare gli effetti di PCDH9 sul melanoma. La comune metalloproteinasi della matrice (MMP) è responsabile della degradazione della matrice extracellulare. È stato dimostrato che MMP2, tra l'insieme di enzimi MMP, gioca un ruolo importante nella migrazione cellulare. I risultati della qRT-PCR hanno mostrato che PCDH9 potrebbe sopprimere le cellule di melanoma influenzando MMP2, CCND1 (Cyclin D1) e RAC1. Il melanoma e i tessuti sani sono stati analizzati analogicamente per dimostrare l'inibizione delle cellule di melanoma da parte del PCDH9. I metodi di Co-IP e LC-MS/MS sono stati utilizzati anche per indagare in profondità la correlazione tra PCDH9 e i suoi effetti di soppressione del melanoma. Abbiamo scoperto che PCDH9 e RAC1 possono predire la prognosi del melanoma maligno e abbiamo ipotizzato che PCDH9 possa modulare la progressione del melanoma attraverso MMP2 e RAC1 riducendo la generazione di ROS dipendente da RAC1 e migliorando il complesso di attività dell'ossidasi NADPH. Abbiamo anche riportato il raro caso di AMM diagnosticato per la prima volta come carcinoma cutaneo a cellule squamose. Questo caso mostra i vari fenotipi del melanoma.<br>With the medical improvement, the life expectancy has rocketed globally. Cancer as the main Noncommunicable disease (NCDs) is the major barrier to extend longevity, causing also a huge medical resource expense. Although innovative treatments as gene therapy, immunotherapy and classical treatments (i.e. chemotherapy, radiotherapy and surgical removal), there is no effective treatment to cure cancer, even because patients usually develop chemoresistance, radioresistance and some resist to gene therapy and immunotherapy as well. Skin is the largest organ first barrier of human body. The basal cell carcinoma, squamous cell carcinoma and melanoma are three common skin cancers Melanoma is a type of cancer that originally develops from melanocytes (pigment containing cells). Melanoma has dramatically increased during last 30 years with low five-year survival and prognosis rate. The current therapeutic approaches of melanoma not only could bring treatment assistance but also have serious side effects like vitiligo. Under the setting of melanoma study, the current thesis investigated the melanoma suppression on different platforms. Firstly, resveratrol, a common bioactive compound, was used to target the potential biomarkers of melanoma. Secondly, through previous studies and then in silico research, certain biomarkers were furtherly targeted. Then in vitro melanoma-cell-culture models were investigated. We demonstrated that the melanoma cells were inhibited by protein-protein interaction. Third, after LC-MS/MS, the protein database was used to analyze and annotate the functions of the potential biomarkers. Forth, the rare case of amelanotic malignant melanoma (AMM) was reported that enlarged the understanding and supplement the phonotypes of melanoma. Resveratrol (RSV) is a kind of phytoalexin that is widely distributed in Mediterranean diet, that as a bioactive natural product, could be a tumor suppressor. We evaluated the effects of RSV on melanoma cells (A375) and found that RSV could obviously inhibit the proliferation of melanoma cells by modulating cell cycle and triggering apoptosis; Cyclin D1 and PCDH9 were strongly affected by RSV duration while RAC1 was not influenced. We furtherly explored the mechanism of these targeted genes of melanoma. In silico and reference studies exhibited the PCDH9 would be the novel biomarker of melanoma. Therefore, the alteration of PCDH9 expression (overexpression and interference) were performed to explore the effects of PCDH9 on melanoma. The common matrix metalloproteinase (MMPs) is responsible for the extracellular matrix degradation. MMP2 -among the set of enzymes (MMPs)- has been demonstrated to play important roles in cell migration. The results of qRT-PCR exhibited that PCDH9 could suppress melanoma cells by affecting MMP2, CCND1 (Cyclin D1) and RAC1. The melanoma and healthy tissues were analogically analyzed to demonstrate the inhibition of melanoma cells by PCDH9. The methods of Co-IP and LC-MS/MS were used as well to deeply investigate the correlation between PCDH9 and its suppression effects of melanoma. We found that PCDH9 and RAC1 can predict the prognosis of malignant melanoma and hypothesized that PCDH9 can modulate melanoma progression through MMP2 and RAC1 by reducing RAC1-dependent ROS generation and enhancing NADPH oxidase activity complex. The rare AMM firstly diagnosed as cutaneous squamous cell carcinoma (cSCC) was reported. According to the results of immunohistochemical examination (Ki67 (+++), Melan-A (+++), human melanoma black (HMB)45 (+), CD20 (-), cytokeratin (CK)7 (-) and CK5/6 (-) were found), the AMM was confirmed and the patient was applied surgical resection. This case showed the various phenotypes of melanoma.

Process operators often have process faults and alarms due to recurring failures on process equipment. It is also the case that some processes do not have enough input information or process models to use conventional modelling or machine learning techniques for early fault detection. A proof of concept for online streaming prediction software based on matching process behaviour to historical motifs has been developed, making use of the Basic Local Alignment Search Tool (BLAST) used in the Bioinformatics field. Execution times of as low as 1 second have been recorded, demonstrating that online matching is feasible. Three techniques have been tested and compared in terms of their computational effciency, robustness and selectivity, with results shown in Table 1: • Symbolic Aggregate Approximation combined with PSI-BLAST • Naive Triangular Representation with PSI-BLAST • Dynamic Time Warping Table 1: Properties of different motif-matching methods Property SAX-PSIBLAST TER-PSIBLAST DTW Noise tolerance (Selectivity) Acceptable Inconclusive Good Vertical Shift tolerance None Perfect Poor Matching speed Acceptable Acceptable Fast Match speed scaling O < O(mn) O < O(mn) O(mn) Dimensionality Reduction Tolerance Good Inconclusive Acceptable It is recommended that a method using a weighted confidence measure for each technique be investigated for the purpose of online process event handling and operator alerts. Keywords: SAX, BLAST, motif-matching, Dynamic Time Warping<br>Dissertation (MEng)--University of Pretoria, 2012.<br>Chemical Engineering<br>unrestricted

Estimating selection is of key importance in evolutionary biology research. The recent price drop in sequencing and advances in NGS data analysis have opened up new avenues for novel methods that estimate selection quantitatively from time-series allele frequency data. However, it is not yet well understood which method performs best given specific model systems and experimental designs. Here, using popular quantitative metrics, we compared the performance of four prominent methods on a series of simulated data sets and on data from real biological experiments. We identified in three out of four methods the experi- mental conditions best suited for estimating selection. We also explored the limitations of these methods when estimating selection from complex patterns of allele frequency change in some relevant evolutionary scenarios. Our findings highlight the need for modification of population genomics models that are still used in inference of model parameters with the goal to develop new, more accurate methods for the quantitative estimation of selection in time-series genomic data.

Computer and Information Science<br>Ph.D.<br>Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems, e.g. a time series of several different classes can be created, by observing various patient attributes over time and the task is to classify unseen patient based on his temporal observations. In time-sensitive applications such as medical applications, some certain aspects have to be considered besides providing accurate classification. The first aspect is providing early classification. Accurate and timely diagnosis is essential for allowing physicians to design appropriate therapeutic strategies at early stages of diseases, when therapies are usually the most effective and the least costly. We propose a probabilistic hybrid method that allows for early, accurate, and patient-specific classification of multivariate time series that, by training on a full time series, offer classification at a very early time point during the diagnosis phase, while staying competitive in terms of accuracy with other models that use full time series both in training and testing. The method has attained very promising results and outperformed the baseline models on a dataset of response to drug therapy in Multiple Sclerosis patients and on a sepsis therapy dataset. Although attaining accurate classification is the primary goal of data mining task, in medical applications it is important to attain decisions that are not only accurate and obtained early, but can also be easily interpreted which is the second aspect of medical applications. Physicians tend to prefer interpretable methods rather than black-box methods. For that purpose, we propose interpretable methods for early classification by extracting interpretable patterns from the raw time series to help physicians in providing early diagnosis and to gain insights into and be convinced about the classification results. The proposed methods have been shown to be more accurate and provided classifications earlier than three alternative state-of-the-art methods when evaluated on human viral infection datasets and a larger myocardial infarction dataset. The third aspect has to be considered for medical applications is the need for predictions to be accompanied by a measure which allows physicians to judge about the uncertainty or belief in the prediction. Knowing the uncertainty associated with a given prediction is especially important in clinical diagnosis where data mining methods assist clinical experts in making decisions and optimizing therapy. We propose an effective method to provide uncertainty estimate for the proposed interpretable early classification methods. The method was evaluated on four challenging medical applications by characterizing decrease in uncertainty of prediction. We showed that our proposed method meets the requirements of uncertainty estimates (the proposed uncertainty measure takes values in the range [0,1] and propagates over time). To the best of our knowledge, this PhD thesis will have a great impact on the link between data mining community and medical domain experts and would give physicians sufficient confidence to put the proposed methods into real practice.<br>Temple University--Theses

In this thesis, a highly comparative framework for time-series analysis is developed. The approach draws on large, interdisciplinary collections of over 9000 time-series analysis methods, or operations, and over 30 000 time series, which we have assembled. Statistical learning methods were used to analyze structure in the set of operations applied to the time series, allowing us to relate different types of scientific methods to one another, and to investigate redundancy across them. An analogous process applied to the data allowed different types of time series to be linked based on their properties, and in particular to connect time series generated by theoretical models with those measured from relevant real-world systems. In the remainder of the thesis, methods for addressing specific problems in time-series analysis are presented that use our diverse collection of operations to represent time series in terms of their measured properties. The broad utility of this highly comparative approach is demonstrated using various case studies, including the discrimination of pathological heart beat series, classification of Parkinsonian phonemes, estimation of the scaling exponent of self-affine time series, prediction of cord pH from fetal heart rates recorded during labor, and the assignment of emotional content to speech recordings. Our methods are also applied to labeled datasets of short time-series patterns studied in temporal data mining, where our feature-based approach exhibits benefits over conventional time-domain classifiers. Lastly, a feature-based dimensionality reduction framework is developed that links dependencies measured between operations to the number of free parameters in a time-series model that could be used to generate a time-series dataset.

Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the dimensionality of the data, and the change of data over time. The genetic groups which are biologically interlinked can be identified through clustering. This project aims to clarify the steps to apply clustering analysis of genes involved in a published dataset. The methodology for this project includes the selection of the dataset representation, the selection of gene datasets, Similarity Matrix Selection, the selection of clustering algorithm, and analysis tool. R language with the focus of Kmeans, fpc, hclust, and heatmap3 packages in R is used in this project as an analysis tool. Different clustering algorithms are used on Spellman dataset to illustrate how genes are grouped together in clusters which help to understand our genetic behaviors.

New approaches are needed to manage and analyze the enormous quantity of biological data generated by modern technologies. Existing solutions are often fragmented and uncoordinated and, thus, they require considerable bioinformatics skills from users. Three applications have been developed illustrating different strategies to help users without extensive IT knowledge to take maximum profit from their data. SNPator is an easy-to-use suite that integrates all the usual tools for genetic association studies: from initial quality control procedures to final statistical analysis. CHAVA is an interactive visual application for CNV calling from aCGH data. It presents data in a visual way that helps assessing the quality of the calling and assists in the process of optimization. Haplotype Association Pattern Analysis visually presents data from exhaustive genomic haplotype associations, so that users can recognize patterns of possible associations that cannot be detected by single-SNP tests.<br>Calen noves aproximacions per la gestió i anàlisi de les enormes quantitats de dades biològiques generades per les tecnologies modernes. Les solucions existents, sovint fragmentaries i descoordinades, requereixen elevats nivells de formació bioinformàtica. Hem desenvolupat tres aplicacions que il•lustren diferents estratègies per ajudar als usuaris no experts en informàtica a aprofitar al màxim les seves dades. SNPator és un paquet de fàcil us que integra les eines usades habitualment en estudis de associació genètica: des del control de qualitat fins les anàlisi estadístiques. CHAVA és una aplicació visual interactiva per a la determinació de CNVs a partir de dades aCGH. Presenta les dades visualment per ajudar a valorar la qualitat de les CNV predites i ajudar a optimitzar-la. Haplotype Pattern Analysis presenta dades d’anàlisi d’associació haplotípica de forma visual per tal que els usuaris puguin reconèixer patrons de associacions que no es possible detectar amb tests de SNPs individuals.

Ce travail de thèse, réalisé au sein de l'entreprise MONDECA et du laboratoire de recherche INSERM, est né du besoin de disposer d'un serveur capable de supporter le processus éditorial de Systèmes d'Organisation de Connaissances (SOC) et soulève la problématique suivante: comment harmoniser la représentation des SOC et de leurs correspondances afin de proposer des services unifiés qui supportent l'édition, la publication et l'utilisation efficaces des connaissances de ces référentiels? Pour répondre à cette problématique, nous soutenons la thèse que l'élaboration d'un modèle de représentation commune de SOC est une solution adaptée pour (i) pallier l'hétérogénéité de ces référentiels, (ii) favoriser l'interopérabilité sémantique au sein d'un Système d'Information et (iii) proposer des services unifiés quel que soit le SOC. Nous utilisons pour cela des méthodes propres à l'Ingénierie des Connaissances couplées à celles de l'Ingénierie des modèles. Les contributions présentées se concentrent sur trois axes. Dans un premier axe, nous souhaitons obtenir une solution de modélisation de SOC la plus générique possible et qui puisse être étendue pour prendre en compte les spécificités de chacun des référentiels. Nous proposons donc un modèle extensible commun de représentation, nommé UniMoKR, construit à partir des standards, recommandations et projets existants. Notre modèle a été proposé et intégré en partie dans la future norme ISO 25964 qui porte sur la représentation des terminologies. Nous avons également soumis deux patrons de modélisation d'ontologie au portail Ontology Design Pattern. Le second axe est consacré à la proposition de services unifiés qui reposent sur cette modélisation. Parmi ces services nous distinguons l'export de tout ou partie de SOC dans un format standard d'échange ou encore des services Web de gestion de terminologies. Pour mettre ces services à disposition, nous préconisons la méthode de transformation de modèles qui utilise le langage SPARQL pour l'expression des règles de transformation. Dans un troisième axe, nous présentons l'application de notre solution testée et commercialisée pour divers projets dans différents domaines d'applications. Nous montrons ici la faisabilité de notre approche, ainsi que l'amélioration que la représentation formelle de notre modèle apporte à la qualité des informations. Ces implémentations ont permis d'effectuer une validation en condition d'utilisation.

Les systèmes de workflows disposent de modules de gestion de provenance qui collectent les informations relatives aux exécutions (données consommées et produites) permettant d'assurer la reproductibilité d'une expérience. Pour plusieurs raisons, la complexité de la structure du workflow et de ses d'exécutions est en augmentation, rendant la réutilisation de workflows plus difficile. L'objectif global de cette thèse est d'améliorer la réutilisation des workflows en fournissant des stratégies pour réduire la complexité des structures de workflow tout en préservant la provenance. Deux stratégies sont introduites. Tout d'abord, nous introduisons SPFlow un algorithme de réécriture de workflow scientifique préservant la provenance et transformant tout graphe acyclique orienté (DAG) en une structure plus simple, série-parallèle (SP). Ces structures permettent la conception d'algorithmes polynomiaux pour effectuer des opérations complexes sur les workflows (par exemple, leur comparaison) alors que ces mêmes opérations sont associées à des problèmes NP-difficile pour des structures générales de DAG. Deuxièmement, nous proposons une technique capable de réduire la redondance présente dans les workflow en détectant et supprimant des motifs responsables de cette redondance, nommés "anti-patterns". Nous avons conçu l'algorithme DistillFlow capable de transformer un workflow en un workflow sémantiquement équivalent "distillé", possédant une structure plus concise et dans laquelle on retire autant que possible les anti-patterns. Nos solutions (SPFlow et DistillFlow) ont été testées systématiquement sur de grandes collections de workflows réels, en particulier avec le système Taverna. Nos outils sont disponibles à l'adresse: https://www.lri.fr/~chenj/.