- Menu
- Passa a palavra.
Partilha esta página. - Linked in
- Copiar Url
-
2017
Autores
Romano, RA; Pait, F; dos Santos, PL;
Publicação
2017 AMERICAN CONTROL CONFERENCE (ACC)
Abstract
While most physical systems or phenomena occur in continuous-time, identification methods based on discrete-time models are more widespread among practitioners and academic community, possibly due to the discrete-time nature of the data records. There has been a growing interest in estimating continuous-time (CT) models in the last decade. This work develops algorithms to estimate the parameters of multivariable state-space CT models from input-output samples using a method based on the recently developed MOLI-ZOFT approach. The performance of the algorithm is evaluated using real data from an industrial winding process.
2017
Autores
van de Ven, P; O'Brien, H; Henriques, R; Klein, M; Msetfi, R; Nelson, J; Rocha, A; Ruwaard, J; O'Sullivan, D; Riper, H;
Publicação
INTERNET INTERVENTIONS-THE APPLICATION OF INFORMATION TECHNOLOGY IN MENTAL AND BEHAVIOURAL HEALTH
Abstract
In this paper we introduce a new Android library, called ULTEMAT, for the delivery of ecological momentary assessments (EMAs) on mobile devices and we present its use in the MoodBuster app developed in the H2020 E-COMPARED project. We discuss context-aware, or event-based, triggers for the presentation of EMAs and discuss the potential they have to improve the effectiveness of mobile provision of mental health interventions as they allow for the delivery of assessments to the patients when and where these are most appropriate. Following this, we present the abilities of ULTEMAT to use such context-aware triggers to schedule EMAs and we discuss how a similar approach can be used for Ecological Momentary Interventions (EMIs).
2017
Autores
Rodrigues, S; Kaiseler, M; Pimentel, G; Rodrigues, J; Aguiar, A; Queirós, C; Cunha, JPS;
Publicação
Occupational Health Science
Abstract
2017
Autores
Yang F.; Wang J.; Pierce B.L.; Chen L.S.; Aguet F.; Ardlie K.G.; Cummings B.B.; Gelfand E.T.; Getz G.; Hadley K.; Handsaker R.E.; Huang K.H.; Kashin S.; Karczewski K.J.; Lek M.; Li X.; MacArthur D.G.; Nedzel J.L.; Nguyen D.T.; Noble M.S.; Segrè A.V.; Trowbridge C.A.; Tukiainen T.; Abell N.S.; Balliu B.; Barshir R.; Basha O.; Battle A.; Bogu G.K.; Brown A.; Brown C.D.; Castel S.E.; Chiang C.; Conrad D.F.; Cox N.J.; Damani F.N.; Davis J.R.; Delaneau O.; Dermitzakis E.T.; Engelhardt B.E.; Eskin E.; Ferreira P.G.; Frésard L.; Gamazon E.R.; Garrido-Martín D.; Gewirtz A.D.H.; Gliner G.; Gloudemans M.J.; Guigo R.; Hall I.M.; Han B.; He Y.; Hormozdiari F.; Howald C.; Im H.K.; Jo B.; Kang E.Y.; Kim Y.; Kim-Hellmuth S.; Lappalainen T.; Li G.; Li X.; Liu B.; Mangul S.; McCarthy M.I.; McDowell I.C.; Mohammadi P.; Monlong J.; Montgomery S.B.; Muñoz-Aguirre M.; Ndungu A.W.; Nicolae D.L.; Nobel A.B.; Oliva M.; Ongen H.; Palowitch J.J.; Panousis N.; Papasaikas P.; Park Y.S.; Parsana P.; Payne A.J.; Peterson C.B.; Quan J.; Reverter F.; Sabatti C.; Saha A.; Sammeth M.; Scott A.J.; Shabalin A.A.; Sodaei R.; Stephens M.; Stranger B.E.; Strober B.J.; Sul J.H.; Tsang E.K.; Urbut S.; van de Bunt M.; Wang G.; Wen X.; Wright F.A.;
Publicação
Genome Research
Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.
2017
Autores
Saha A.; Kim Y.; Gewirtz A.D.H.; Jo B.; Gao C.; McDowell I.C.; Engelhardt B.E.; Battle A.; Aguet F.; Ardlie K.G.; Cummings B.B.; Gelfand E.T.; Getz G.; Hadley K.; Handsaker R.E.; Huang K.H.; Kashin S.; Karczewski K.J.; Lek M.; Li X.; MacArthur D.G.; Nedzel J.L.; Nguyen D.T.; Noble M.S.; Segrè A.V.; Trowbridge C.A.; Tukiainen T.; Abell N.S.; Balliu B.; Barshir R.; Basha O.; Battle A.; Bogu G.K.; Brown A.; Brown C.D.; Castel S.E.; Chen L.S.; Chiang C.; Conrad D.F.; Cox N.J.; Damani F.N.; Davis J.R.; Delaneau O.; Dermitzakis E.T.; Engelhardt B.E.; Eskin E.; Ferreira P.G.; Frésard L.; Gamazon E.R.; Garrido-Martín D.; Gliner G.; Gloudemans M.J.; Guigo R.; Hall I.M.; Han B.; He Y.; Hormozdiari F.; Howald C.; Im H.K.; Kang E.Y.; Kim-Hellmuth S.; Lappalainen T.; Li G.; Li X.; Liu B.; Mangul S.; McCarthy M.I.; Mohammadi P.; Monlong J.; Montgomery S.B.; Muñoz-Aguirre M.; Ndungu A.W.; Nicolae D.L.; Nobel A.B.; Oliva M.; Ongen H.; Palowitch J.J.; Panousis N.; Papasaikas P.; Park Y.S.; Parsana P.; Payne A.J.; Peterson C.B.; Quan J.; Reverter F.; Sabatti C.; Sammeth M.; Scott A.J.; Shabalin A.A.; Sodaei R.; Stephens M.; Stranger B.E.; Strober B.J.; Sul J.H.; Tsang E.K.; Urbut S.; van de Bunt M.; Wang G.; Wen X.; Wright F.A.;
Publicação
Genome Research
Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
2017
Autores
Aguet, F; Brown, AA; Castel, SE; Davis, JR; He, Y; Jo, B; Mohammadi, P; Park, Y; Parsana, P; Segre, AV; Strober, BJ; Zappala, Z; Cummings, BB; Gelfand, ET; Hadley, K; Huang, KH; Lek, M; Li, X; Nedzel, JL; Nguyen, DY; Noble, MS; Sullivan, TJ; Tukiainen, T; MacArthur, DG; Getz, G; Management, NP; Addington, A; Guan, P; Koester, S; Little, AR; Lockhart, NC; Moore, HM; Rao, A; Struewing, JP; Volpi, S; Collection, B; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; Mestichelli, B; Myer, K; Roe, B; Salvatore, M; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Bridge, J; Foster, BA; Gillard, BM; Karasik, E; Kumar, R; Miklos, M; Moser, MT; Jewell, SD; Montroy, RG; Rohrer, DC; Valley, D; Mash, DC; Davis, DA; Sobin, L; Barcus, ME; Branton, PA; Grp, EMW; Abell, NS; Balliu, B; Delaneau, O; Fresard, L; Gamazon, ER; Garrido Martin, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Han, B; He, AZ; Hormozdiari, F; Li, X; Liu, B; Kang, EY; McDowell, IC; Ongen, H; Palowitch, JJ; Peterson, CB; Quon, G; Ripke, S; Saha, A; Shabalin, AA; Shimko, TC; Sul, JH; Teran, NA; Tsang, EK; Zhang, H; Zhou, YH; Bustamante, CD; Cox, NJ; Guigo, R; Kellis, M; McCarthy, MI; Conrad, DF; Eskin, E; Li, G; Nobel, AB; Sabatti, C; Stranger, BE; Wen, X; Wright, FA; Ardlie, KG; Dermitzakis, ET; Lappalainen, T; Battle, A; Brown, CD; Engelhardt, BE; Montgomery, SB; Aguet, F; Ardlie, KG; Cummings, BB; Gelfand, ET; Getz, G; Hadley, K; Handsaker, RE; Huang, KH; Kashin, S; Karczewski, KJ; Lek, M; Li, X; MacArthur, DG; Nedzel, JL; Nguyen, DT; Noble, MS; Segre, AV; Trowbridge, CA; Tukiainen, T; Abell, NS; Balliu, B; Barshir, R; Basha, O; Battle, A; Bogu, GK; Brown, A; Brown, CD; Castel, SE; Chen, LS; Chiang, C; Conrad, DF; Cox, NJ; Damani, FN; Davis, JR; Delaneau, O; Dermitzakis, ET; Engelhardt, BE; Eskin, E; Ferreira, PG; Fresard, L; Gamazon, ER; Garrido Martin, D; Gewirtz, ADH; Gliner, G; Gloudemans, MJ; Guigo, R; Hall, IM; Han, B; He, Y; Hormozdiari, F; Howald, C; Im, HK; Jo, B; Kang, EY; Kim, Y; Kim Hellmuth, S; Lappalainen, T; Li, G; Li, X; Liu, B; Mangul, S; McCarthy, MI; McDowell, IC; Mohammadi, P; Monlong, J; Montgomery, SB; Munoz Aguirre, M; Ndungu, AW; Nicolae, DL; Nobel, AB; Oliva, M; Ongen, H; Palowitch, JJ; Panousis, N; Papasaikas, P; Park, Y; Parsana, P; Payne, AJ; Peterson, CB; Quan, J; Reverter, F; Sabatti, C; Saha, A; Sammeth, M; Scott, AJ; Shabalin, AA; Sodaei, R; Stephens, M; Stranger, BE; Strober, BJ; Sul, JH; Tsang, EK; Urbut, S; De Bunt, MV; Wang, G; Wen, X; Wright, FA; Xi, HS; Yeger Lotem, E; Zappala, Z; Zaugg, JB; Zhou, YH; Akey, JM; Bates, D; Chan, J; Chen, LS; Claussnitzer, M; Demanelis, K; Diegel, M; Doherty, JA; Feinberg, AP; Fernando, MS; Halow, J; Hansen, KD; Haugen, E; Hickey, PF; Hou, L; Jasmine, F; Jian, R; Jiang, L; Johnson, A; Kaul, R; Kellis, M; Kibriya, MG; Lee, K; Li, JB; Li, Q; Li, X; Lin, J; Lin, S; Linder, S; Linke, C; Liu, Y; Maurano, MT; Molinie, B; Montgomery, SB; Nelson, J; Neri, FJ; Oliva, M; Park, Y; Pierce, BL; Rinaldi, NJ; Rizzardi, LF; Sandstrom, R; Skol, A; Smith, KS; Snyder, MP; Stamatoyannopoulos, J; Stranger, BE; Tang, H; Tsang, EK; Wang, L; Wang, M; Van Wittenberghe, N; Wu, F; Zhang, R; Fund, NC; Nierras, CR; Nci, N; Branton, PA; Carithers, LJ; Guan, P; Moore, HM; Rao, A; Vaught, JB; Nhgri, N; Gould, SE; Lockart, NC; Martin, C; Struewing, JP; Volpi, S; Nimh, N; Addington, AM; Koester, SE; Nida, N; Little, AR; Brigham, LE; Hasz, R; Hunter, M; Johns, C; Johnson, M; Kopen, G; Leinweber, WF; Lonsdale, JT; McDonald, A; Mestichelli, B; Myer, K; Roe, B; Salvatore, M; Shad, S; Thomas, JA; Walters, G; Washington, M; Wheeler, J; Bridge, J; Foster, BA; Gillard, BM; Karasik, E; Kumar, R; Miklos, M; Moser, MT; Jewell, SD; Montroy, RG; Rohrer, DC; Valley, DR; Davis, DA; Mash, DC; Undale, AH; Smith, AM; Tabor, DE; Roche, NV; McLean, JA; Vatanian, N; Robinson, KL; Sobin, L; Barcus, ME; Valentino, KM; Qi, L; Hunter, S; Hariharan, P; Singh, S; Um, KS; Matose, T; Tomaszewski, MM; Study, E; Barker, LK; Mosavel, M; Siminoff, LA; Traino, HM; Flicek, P; Juettemann, T; Ruffier, M; Sheppard, D; Taylor, K; Trevanion, SJ; Zerbino, DR; Craft, B; Goldman, M; Haeussler, M; Kent, WJ; Lee, CM; Paten, B; Rosenbloom, KR; Vivian, J; Zhu, J;
Publicação
NATURE
Abstract
Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.