Investigation of model stacking for drug sensitivity prediction

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types. Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing squared error and inherent bias of random forests in prediction of outliers. The framework is tested on a setup including gene expression, drug target, physical properties and drug response information for a set of drugs and cell lines. Coclusion: The performance of individual and stacked models are compared. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominat eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues

Investigation of model stacking for drug sensitivity prediction

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types. Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing squared error and inherent bias of random forests in prediction of outliers. The framework is tested on a setup including gene expression, drug target, physical properties and drug response information for a set of drugs and cell lines. Coclusion: The performance of individual and stacked models are compared. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominat eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues

Changes in quality of life following hypoglycaemia in adults with type 2 diabetes: a systematic review of longitudinal studies

AIM: To conduct a systematic review of published studies reporting on the longitudinal impacts of hypoglycaemia on quality of life (QoL) in adults with type 2 diabetes. METHOD: Database searches with no restrictions by language or date were conducted in MEDLINE, Cochrane Library, CINAHL and PsycINFO. Studies were included for review if they used a longitudinal design (e.g. cohort studies, randomised controlled trials) and reported on the association between hypoglycaemia and changes over time in patient-reported outcomes related to QoL. RESULTS: In all, 20 longitudinal studies published between 1998 and 2020, representing 50,429 adults with type 2 diabetes, were selected for review. A descriptive synthesis following Synthesis Without Meta-analysis guidelines indicated that self-treated symptomatic hypoglycaemia was followed by impairments in daily functioning along with elevated symptoms of generalised anxiety, diabetes distress and fear of hypoglycaemia. Severe hypoglycaemic events were associated with reduced confidence in diabetes self-management and lower ratings of perceived health over time. Frequent hypoglycaemia was followed by reduced energy levels and diminished emotional well-being. There was insufficient evidence, however, to conclude that hypoglycaemia impacted sleep quality, depressive symptoms, general mood, social support or overall diabetes-specific QoL. CONCLUSIONS: Longitudinal evidence in this review suggests hypoglycaemia is a common occurrence among adults with type 2 diabetes that impacts key facets in the physical and psychological domains of QoL. Nonetheless, additional longitudinal research is needed-in particular, studies targeting diverse forms of hypoglycaemia, more varied facets of QoL and outcomes assessed using hypoglycaemia-specific measures

Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer

Causal discovery is becoming a key part in medical AI research. These methods can enhance healthcare by identifying causal links between biomarkers, demographics, treatments and outcomes. They can aid medical professionals in choosing more impactful treatments and strategies. In parallel, Large Language Models (LLMs) have shown great potential in identifying patterns and generating insights from text data. In this paper we investigate applying LLMs to the problem of determining the directionality of edges in causal discovery. Specifically, we test our approach on a deidentified set of Non Small Cell Lung Cancer(NSCLC) patients that have both electronic health record and genomic panel data. Graphs are validated using Bayesian Dirichlet estimators using tabular data. Our result shows that LLMs can accurately predict the directionality of edges in causal graphs, outperforming existing state-of-the-art methods. These findings suggests that LLMs can play a significant role in advancing causal discovery and help us better understand complex systems

Investigation of translocation, DNA unwinding, and protein displacement by NS3h, the helicase domain from the Hepatitis C virus helicase

Helicases are motor proteins that are involved in DNA and RNA metabolism, replication, recombination, transcription and repair. The motors are powered by ATP binding and hydrolysis. Hepatitis C virus encodes a helicase called non-structural protein (NS3). NS3 possesses protease and helicase activities on its N-terminal and C-terminal domains respectively. The helicase domain of NS3 protein is referred as NS3h. In vitro, NS3h catalyzes RNA and DNA unwinding in a 3’ to -5’ direction. The directionality for unwinding is thought to arise in part from the enzyme's ability to translocate along DNA, but translocation has not been shown explicitly. We examined the DNA translocase activity of NS3h by using single-stranded oligonucleotide substrates containing a fluorescent probe on the 5’ end. NS3h can bind to the ssDNA and in the presence of ATP, move towards the 5’-end. When the enzyme encounters the fluorescent probe, a fluorescence change is observed that allows translocation to be characterized. Under conditions that favor binding of one NS3h per DNA substrate (100 nM NS3h, 200 nM oligonucleotide) we find that NS3h translocates on ssDNA at a rate of 46 ± 5 nt s−1 and that it can move for 230 ± 60 nt before dissociating from the DNA. The translocase activity of some helicases is responsible for displacing proteins that are bound to DNA. We studied protein displacement by using a ssDNA oligonucleotide covalently linked to biotin on the 5’-end. Upon addition of streptavidin, a ‘protein-block’ was placed in the pathway of the helicase. Interestingly, NS3h was unable to displace streptavidin from the end of the oligonucleotide, despite its ability to translocate along the DNA. The DNA unwinding activity of NS3h was examined using a 22 bp duplex DNA substrate under conditions that were identical to those used to study translocation. NS3h exhibited little or no DNA unwinding under single cycle conditions, supporting the conclusion that NS3h is a relatively poor helicase in its monomeric form, as has been reported. In summary, NS3h translocates on ssDNA as a monomer, but the translocase activity does not correspond to comparable DNA unwinding activity or protein-displacement activity under identical conditions

Genomic network analysis of environmental and livestock F-type plasmid populations

F-type plasmids are diverse and of great clinical significance, often carrying genes conferring antimicrobial resistance (AMR) such as extended-spectrum β-lactamases, particularly in Enterobacterales. Organising this plasmid diversity is challenging, and current knowledge is largely based on plasmids from clinical settings. Here, we present a network community analysis of a large survey of F-type plasmids from environmental (influent, effluent, and upstream/downstream waterways surrounding wastewater treatment works) and livestock settings. We use a tractable and scalable methodology to examine the relationship between plasmid metadata and network communities. This reveals how niche (sampling compartment and host genera) partition and shape plasmid diversity. We also perform pangenome-style analyses on network communities. We show that such communities define unique combinations of core genes, with limited overlap. Building plasmid phylogenies based on alignments of these core genes, we demonstrate that plasmid accessory function is closely linked to core gene content. Taken together, our results suggest that stable F-type plasmid backbone structures can persist in environmental settings while allowing dramatic variation in accessory gene content that may be linked to niche adaptation. The association of F-type plasmids with AMR likely reflects their suitability for rapid niche adaptation

Application of transfer learning for cancer drug sensitivity prediction

Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context. Results: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as comapred to the existing approaches. Conclusion: We have comapred the performance of the proposed predictive models with database-specifc individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting senstivity for different anti-cancer compound, particularly the nonlinear mapping model shows the best overall performance

Investigation of model stacking for drug sensitivity prediction

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types. Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing squared error and inherent bias of random forests in prediction of outliers. The framework is tested on a setup including gene expression, drug target, physical properties and drug response information for a set of drugs and cell lines. Coclusion: The performance of individual and stacked models are compared. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominat eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues