Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

Photon and leptons induced processes at the LHC

We study a few basic photon- and lepton-initiated processes at the LHC which can be computed using the recently developed photon and lepton parton densities. First, we consider the production of a massive scalar particle initiated by lepton-antilepton annihilation and photon-photon fusion as representative examples of searches of exotic particles. Then we study lepton-lepton scattering, since this Standard-Model process may be observable at the LHC. We examine these processes at leading and next-to-leading order and, using the POWHEG method, we match our calculations to parton shower programs that implement the required lepton or photon initial-states. We assess the typical size of cross-sections and their uncertainties and discuss the preferred choices for the factorization scale. These processes can also be computed starting directly from the lepto-production hadronic tensor, leading to a result where some collinear-enhanced QED corrections are missing, but all strong corrections are included. Thus, we are in the unique position to perform a comparison of results obtained via the factorization approach to a calculation that does not have strong corrections. This is particularly relevant in the case of lepton-scattering, that is more abundant at lower energies where it is affected by larger strong corrections. We thus compute this process also with the hadronic-tensor method, and compare the results with those obtained with POWHEG. Finally, for some lepton-lepton scattering processes, we compare the size of the signal to the main quark-induced background, which is double Drell-Yan production, and outline a preliminary search strategy to enhance the signal to background ratio.

Bottomonium production and polarization in the NRQCD with $$k_T$$-factorization. III: $$\Upsilon (1S)$$ and $$\chi _b(1P)$$ mesons

The $$\Upsilon (1S)$$ Υ ( 1 S ) meson production and polarization at high energies is studied in the framework of the $$k_T$$ k T -factorization approach. Our consideration is based on the non-relativistic QCD formalism for a bound states formation and off-shell production amplitudes for hard partonic subprocesses. The direct production mechanism, feed-down contributions from radiative $$\chi _b(mP)$$ χ b ( m P ) decays and contributions from $$\Upsilon (3S)$$ Υ ( 3 S ) and $$\Upsilon (2S)$$ Υ ( 2 S ) decays are taken into account. The transverse momentum dependent (TMD) gluon densities in a proton were derived from the Ciafaloni–Catani–Fiorani–Marchesini evolution equation and the Kimber-Martin–Ryskin prescription. Treating the non-perturbative color octet transitions in terms of multipole radiation theory, we extract the corresponding non-perturbative matrix elements for $$\Upsilon (1S)$$ Υ ( 1 S ) and $$\chi _b(1P)$$ χ b ( 1 P ) mesons from a combined fit to transverse momenta distributions measured at various LHC experiments. Then we apply the extracted values to investigate the polarization parameters $$\lambda _\theta $$ λ θ , $$\lambda _\phi $$ λ ϕ and $$\lambda _{\theta \phi }$$ λ θ ϕ , which determine the $$\Upsilon (1S)$$ Υ ( 1 S ) spin density matrix. Our predictions have a reasonably good agreement with the currently available Tevatron and LHC data within the theoretical and experimental uncertainties.

Time-reversal asymmetries and angular distributions in Λb → ΛV

We study the spin correlations to probe time-reversal (T) asymmetries in the decays of Λb→ ΛV (V = ϕ, ρ0, ω, K∗0). The eigenstates of the T-odd operators are obtained along with definite angular momenta. We obtain the T-odd spin correlations from the complex phases among the helicity amplitudes. We give the angular distributions of Λb→ Λ(→ pπ−)V (→ PP′) and show the corresponding spin correlations, where P(′) are the pseudoscalar mesons. Due to the helicity conservation of the s quark in Λ, we deduce that the polarization asymmetries of Λ are close to −1. Since the decay of Λb→ Λϕ in the standard model (SM) is dictated by the single weak phase from the product of CKM elements, $$ {V}_{tb}{V}_{ts}^{\ast } $$ V tb V ts ∗ , the true T and CP asymmetries are suppressed, providing a clean background to test the SM and search for new physics. In the factorization approach, as the helicity amplitudes in the SM share the same complex phase, T-violating effects are absent. Nonetheless, the experimental branching ratio of Br(Λb→ Λϕ) = (5.18 ± 1.29) × 10−6 suggests that the nonfactorizable effects or some new physics play an important role. By parametrizing the nonfactorizable contributions with the effective color number, we calculate the branching ratios and direct CP asymmetries. We also explore the possible T-violating effects from new physics.

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear patterns from massive text data collections.