theophqa

• Sveen, Anita; Stenersen, Jakob Mørkved; Asenso, Theophilus Quachie; Klokkerud, Solveig Knoph; Kranjec, Christian & Eilertsen, Ina [Vis alle 11 forfattere av denne artikkelen] (2026). Exceptional treatment responses and molecular markers among 103 colorectal cancer cell lines. Molecular Biomedicine. 7. doi: 10.1186/s43556-026-00551-z. Fulltekst i vitenarkiv Vis sammendrag

  Patients with colorectal cancer have few molecularly guided treatment options. Cancers with exceptional treatment response are valuable models for discovery of molecular response mechanisms and might reveal new predictive biomarkers to guide treatment selection. However, the rarity of events is a clinical challenge. We performed integrated high-throughput drug sensitivity testing (n = 620 drugs) and molecular analyses of 103 colorectal cancer cell lines to build an in vitro foundation for exceptional treatment responses and their molecular markers. Exceptional responses were scored by a two-way outlier detection approach and were identified in 67 unique cell line-drug pairs (0.13% of all pairs tested). This involved 35 cell lines (34.0%) and 49 drugs (7.9%) representing standard, experimental, and non-oncology drugs of diverse classes. Plausible response mechanisms on the gene, protein, and/or pathway expression levels were identified at baseline in 89.6% of the 67 pairs. Experimental validation confirmed treatment-induced marker suppression in seven of eight selected pairs, including target engagement of kinase inhibitors and downregulation of markers of extended response mechanisms. Recurrent exceptional sensitivity to the same drug in different cell lines involved functionally convergent molecular mechanisms, but rarely precisely the same marker. Most exceptional sensitivities (77.8%) could be predicted with multivariable transcriptomic models. In conclusion, this study provides in vitro support for biomarker-guided prescreening to potentially obtain exceptional treatment response to diverse drugs and therapeutic targets in colorectal cancer. Most exceptional responses have a clear molecular underpinning. The study also provides a large and openly available pharmaco-transcriptomics resource for colorectal cancer cell lines.

• Dalessandro, Matteo; Asenso, Theophilus Quachie & Zucknick, Manuela (2025). Integrating Multiple Data Sources With Interactions in Multi-Omics Using Cooperative Learning. Statistics in Medicine. ISSN 0277-6715. 44(13-14). doi: 10.1002/sim.70148. Fulltekst i vitenarkiv Vis sammendrag

  Modeling with multi-omics data presents multiple challenges such as the high-dimensionality of the problem (p ≫ n), the presence of interactions between features, and the need for integration between multiple data sources. We establish an interaction model that allows for the inclusion of multiple sources of data from the integration of two existing methods, pliable lasso and cooperative learning. The integrated model is tested both on simulation studies and on real multi-omics datasets for predicting labor onset and cancer treatment response. The results show that the model is effective in modeling multi-source data in various scenarios where interactions are present, both in terms of prediction performance and selection of relevant variables.

• Asenso, Theophilus Quachie & Zucknick, Manuela (2025). A novel multi-response regression with overlapping groups and interaction effects motivated by large-scale pharmacogenomic screens. Journal of Applied Statistics. ISSN 0266-4763. s. 1–30. doi: 10.1080/02664763.2025.2567981. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, we consider the regularised multi-response regression problem where there exists some structural relation within the responses and also between the covariates and a set of modifying variables. To handle this problem, we propose MADMMplasso, a novel regularised regression method. This method is able to find covariates and their corresponding interactions, with some joint association with multiple related responses. We allow the interaction term between covariate and modifying variable to be included in a (weak) asymmetrical hierarchical manner by considering whether the corresponding covariate main term is in the model. For parameter estimation, we develop an ADMM algorithm that allows us to implement the overlapping groups in a simple way. The results from the simulations and analysis of a pharmacogenomic screen dataset show that the proposed method has an advantage in handling correlated responses and interaction effects, with respect to prediction and variable selection performance.

• Kwakye, Benjamin Danso; Li, Yongjun; Mohamed, Halima Habuba; Baidoo, Evans & Asenso, Theophilus Quachie (2024). Particle guided metaheuristic algorithm for global optimization and feature selection problems[Formula presented]. Expert Systems With Applications. ISSN 0957-4174. 248. doi: 10.1016/j.eswa.2024.123362. Fulltekst i vitenarkiv
• Chidume, Chidiebere Sunday; Okopi, Solomon Inalegwu; Sesay, Taiwo; Materu, Irene Simon & Asenso, Theophilus Quachie (2022). Three-Tier Computing Platform Optimization: A Deep Reinforcement Learning Approach. Mobile Information Systems. ISSN 1574-017X. 2022. doi: 10.1155/2022/5051496. Fulltekst i vitenarkiv Vis sammendrag

  he increasing number of computing platforms is critical with the increasing trend of delay-sensitive complex applications with enormous power consumption. These computing platforms attach themselves to multiple small base stations and macro base stations to optimize system performance if appropriately allocated. The arrival rate of computing tasks is often stochastic under time-varying wireless channel conditions in the mobile edge computing Internet of things (MEC IoT) network, making it challenging to implement an optimal offloading scheme. The user needs to choose the best computing platforms and base stations to minimize the task completion time and consume less power. In addition, the reliability of our system in terms of the number of computing resources (power, CPU cycles) each computing platform consumes to process the user’s task efficiently needs to be ascertained. This paper implements a computational task offloading scheme to a high-performance processor through a small base station and a collaborative edge device through macro base stations, considering the system’s maximum available processing capacity as one of the optimization constraints. We minimized the latency and energy consumption, which constitute the system’s total cost, by optimizing the computing platform choice, base station’s choice, and resource allocation (computing, communication, power). We use the actor-critic architecture to implement a Markov decision process that depends on deep reinforcement learning (DRL) to solve the model’s problem. Simulation results showed that our proposed scheme achieves significant long-term rewards in latency and energy costs compared with random search, greedy search, deep Q-learning, and the other schemes that implemented either the local computation or edge computation.

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