| 1. |
- Munn-Chernoff, M. A., et al.
(författare)
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Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies
- 2021
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Ingår i: Addiction Biology. - : Wiley. - 1355-6215 .- 1369-1600. ; 26:1
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Tidskriftsartikel (refereegranskat)abstract
- Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r(g)], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from similar to 2400 to similar to 537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r(g) = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r(g) = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r(g) = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r(gs) = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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| 2. |
- Murgas, F., et al.
(författare)
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Two super-Earths at the edge of the habitable zone of the nearby M dwarf TOI-2095
- 2023
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Ingår i: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 677
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Tidskriftsartikel (refereegranskat)abstract
- The main scientific goal of TESS is to find planets smaller than Neptune around stars that are bright enough to allow for further characterization studies. Given our current instrumentation and detection biases, M dwarfs are prime targets in the search for small planets that are in (or near) the habitable zone of their host star. In this work, we use photometric observations and CARMENES radial velocity (RV) measurements to validate a pair of transiting planet candidates found by TESS. The data were fitted simultaneously, using a Bayesian Markov chain Monte Carlo (MCMC) procedure and taking into account the stellar variability present in the photometric and spectroscopic time series. We confirm the planetary origin of the two transiting candidates orbiting around TOI-2095 (LSPM J1902+7525). The star is a nearby M dwarf (d = 41.90 ± 0.03 pc, Teff = 3759 ± 87 K, V = 12.6 mag), with a stellar mass and radius of M∗ = 0.44 ± 0.02 M· and R∗ = 0.44 ± 0.02 R·, respectively. The planetary system is composed of two transiting planets: TOI-2095b, with an orbital period of Pb = 17.66484 ± (7 A - 10- 5) days, and TOI-2095c, with Pc = 28.17232 ± (14 A - 10- 5) days. Both planets have similar sizes with Rb = 1.25 ± 0.07 R· and Rc = 1.33 ± 0.08 R· for planet b and planet c, respectively. Although we did not detect the induced RV variations of any planet with significance, our CARMENES data allow us to set stringent upper limits on the masses of these objects. We find Mb < 4.1 M· for the inner and Mc < 7.4 M· for the outer planet (95% confidence level). These two planets present equilibrium temperatures in the range of 300 350 K and are close to the inner edge of the habitable zone of their star.
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| 3. |
- Ng, Theam Foo, et al.
(författare)
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Automated feature weighting in fuzzy declustering-based vector quantization
- 2010
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Konferensbidrag (refereegranskat)abstract
- Feature weighting plays an important role in improving the performance of clustering technique. We propose an automated feature weighting in fuzzy declustering-based vector quantization (FDVQ), namely AFDVQ algorithm, for enhancing effectiveness and efficiency in classification. The proposed AFDVQ imposes weights on the modified fuzzy c-means (FCM) so that it can automatically calculate feature weights based on their degrees of importance rather than treating them equally. Moreover, the extension of FDVQ and AFDVQ algorithms based on generalized improved fuzzy partitions (GIFP), known as GIFP-FDVQ and GIFP-AFDVQ respectively, are proposed. The experimental results on real data (original and noisy data) and modified data (biased and noisy-biased data) have demonstrated that the proposed algorithms outperformed standard algorithms in classifying clusters especially for biased data.
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| 4. |
- Ng, Theam Foo, et al.
(författare)
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Feature interaction in subspace clustering using the Choquet integral
- 2012
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 45:7, s. 2645-2660
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Tidskriftsartikel (refereegranskat)abstract
- Subspace clustering has recently emerged as a popular approach to removing irrelevant and redundant features during the clustering process. However, most subspace clustering methods do not consider the interaction between the features. This unawareness limits the analysis performance in many pattern recognition problems. In this paper, we propose a novel subspace clustering technique by introducing the feature interaction using the concepts of fuzzy measures and the Choquet integral. This new framework of subspace clustering can provide optimal subsets of interacted features chosen for each cluster, and hence can improve clustering-based pattern recognition tasks. Various experimental results illustrate the effective performance of the proposed method.
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| 5. |
- Ng, Theam Foo, et al.
(författare)
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Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis
- 2013
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Ingår i: Knowledge-Based Systems in Biomedicine and Computational Life Science. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642330148 - 9783642330155 ; , s. 177-213
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Features or attributes play an important role when handling multi-dimensional datasets. Generally, not all the features are needed to find several groups of similar objects in traditional clustering methods because some of the features may not be relevant and also redundant. Hence, the concept of identifying subsets of the features that are relevant to clusters is introduced, instead of using the full set of features. This chapter discusses the use of the prior knowledge of the importance of features and their interaction in constructing both fuzzy measures and signed fuzzy measures for subspace clustering. The Choquet integral, which is known as a useful aggregation operator with respect to fuzzy measure, is used to aggregate the importance and interaction of the features. The concept of fuzzy knowledge-based subspace clustering is applied especially to the analysis of life science data in this chapter.
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| 6. |
- Ng, Theam Foo, et al.
(författare)
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Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes
- 2010
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Konferensbidrag (refereegranskat)abstract
- With the fast development of multi‐dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering‐based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype‐image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ‐HMM) and a well‐known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG‐HMM) will be carried out. The experimental results show that the performances of both FDVQ‐HMM and LBG‐HMM are almost similar. Finally, we have justified the competitiveness of FDVQ‐HMM in classification of cellular phenotype image database by using hypotheses t‐test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome‐wide screening image data.
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