This document describes a new algorithm called GRAph ALigner (GRAAL) for topological network alignment. GRAAL aims to provide a unique global alignment of nodes between two networks based solely on topological similarity, without using other a priori information. It works by first finding a dense core between the networks to seed the alignment, then expanding outwards in spheres of increasing radius to align additional nodes. The alignment score is based on the percentage of edges from the first network that are correctly aligned to edges in the second network. The algorithm is tested on sample networks and achieves an edge correctness of 0.089.
DnaBind is a hybrid algorithm that uses machine learning and template-based methods to predict DNA-binding residues in proteins, showing strong performance. This approach leverages statistical features and structural properties to improve classification accuracy compared to other software. The hybrid method demonstrates effectiveness in reducing the time and cost associated with experimental determination of protein-DNA interactions.
This study evaluated changes in hepatitis B virus (HBV) quasispecies diversity and complexity during lamivudine treatment. 25 patients were treated with lamivudine for 4 weeks. Viral DNA was cloned from patients at baseline and 4 weeks. Patients were classified as responders or non-responders based on HBV DNA and liver enzyme levels. Responders had lower quasispecies complexity and diversity after 4 weeks compared to non-responders, and responders were more likely to be monoclonal while non-responders were polyclonal.
This document presents a new statistical method called smooth isotonic regression (sIR) to calibrate predictive models. sIR improves upon existing calibration methods like logistic regression (LR) and isotonic regression (IR) by using spline curves to provide a smooth, non-parametric calibration curve. The sIR method was shown to outperform LR and IR in calibration based on both simulation data and analysis of a real biological dataset, providing calibrated predicted values that better reflected the actual observed values.
DnaBind is a hybrid algorithm that uses machine learning and template-based methods to predict DNA-binding residues in proteins, showing strong performance. This approach leverages statistical features and structural properties to improve classification accuracy compared to other software. The hybrid method demonstrates effectiveness in reducing the time and cost associated with experimental determination of protein-DNA interactions.
This study evaluated changes in hepatitis B virus (HBV) quasispecies diversity and complexity during lamivudine treatment. 25 patients were treated with lamivudine for 4 weeks. Viral DNA was cloned from patients at baseline and 4 weeks. Patients were classified as responders or non-responders based on HBV DNA and liver enzyme levels. Responders had lower quasispecies complexity and diversity after 4 weeks compared to non-responders, and responders were more likely to be monoclonal while non-responders were polyclonal.
This document presents a new statistical method called smooth isotonic regression (sIR) to calibrate predictive models. sIR improves upon existing calibration methods like logistic regression (LR) and isotonic regression (IR) by using spline curves to provide a smooth, non-parametric calibration curve. The sIR method was shown to outperform LR and IR in calibration based on both simulation data and analysis of a real biological dataset, providing calibrated predicted values that better reflected the actual observed values.
16. topicmodels package CTM
@Sigma # variance-covariance matrix of topics on the logit scale.
These are files corresponding to the (K-1) x (K-1) covariance matrix
between topics. Note that this code implements the logistic normal
where a K-2 Gaussian is mapped to the K-1 simplex. (This is slightly
different from the treatment in the paper, where the K-1 Gaussian is
mapped to the K-1 simplex.)
Correlation Topic Model (CTM)
17. These are files corresponding to the (K-1)
x (K-1) covariance matrix between topics.
Note that this code implements the
logistic normal where a K-2 Gaussian is
mapped to the K-1 simplex. (This is
slightly different from the treatment in
the paper, where the K-1 Gaussian is
mapped to the K-1 simplex.)
晩云Zでおk
18. (K-1) x (K-1) covariance matrix??
トピック方はKだろ?
Note that this code implements the logistic normal where
a K-2 Gaussian is mapped to the K-1 simplex.
La estoy cr┴ptica distribuci┏n de Gauss de K-2 ?Qui└n dijo
que corresponde a la simple de K-1?
(K-2のガウス蛍下がK-1のsimplexに鬉靴討い襪辰
吭龍音苧なんだけど?)
???