Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Molecular biology of breast cancer metastasis: The use of mathematical models to determine relapse and to predict response to chemotherapy in breast cancer

Breast cancer mortality rates have shown only modest improvemen despite the advent of effective chemotherapeutic agents which have been administered to a large percentage of women with breast cancer. In an effort to improve breast cancer treatment strategies, a variety of mathematical models have been developed that describe the natural history of breast cancer and the effects of treatment on the cancer. These models help researchers to develop, quantify, and test various treatment hypotheses quickly and efficiently. The present review discusses several of these models, with a focus on how they have been used to predict the initiation time of metastatic growth, the effect of operative therapy on the growth of metastases, and the optimal administration strategy for chemotherapy

Cell killing and resistance in pre-operative breast cancer chemotherapy

<p>Abstract</p> <p>Background</p> <p>Despite the recent development of technologies giving detailed images of tumours <it>in vivo</it>, direct or indirect ways to measure how many cells are actually killed by a treatment or are resistant to it are still beyond our reach.</p> <p>Methods</p> <p>We designed a simple model of tumour progression during treatment, based on descriptions of the key phenomena of proliferation, quiescence, cell killing and resistance, and giving as output the macroscopically measurable tumour volume and growth fraction. The model was applied to a database of the time course of volumes of breast cancer in patients undergoing pre-operative chemotherapy, for which the initial estimate of proliferating cells by the measure of the percentage of Ki67-positive cells was available.</p> <p>Results</p> <p>The analysis recognises different patterns of response to treatment. In one subgroup of patients the fitting implied drug resistance. In another subgroup there was a shift to higher sensitivity during the therapy. In the subgroup of patients where killing of cycling cells had the highest score, the drugs showed variable efficacy against quiescent cells.</p> <p>Conclusion</p> <p>The approach was feasible, providing items of information not otherwise available. Additional data, particularly sequential Ki67 measures, could be added to the system, potentially reducing uncertainty in estimates of parameter values.</p

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionised our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive data sets, exponential increases in processing power and key algorithmic developments which allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain.In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation where ML is driving progress, explore the future potential roles of ML and discuss the challenges and limitations of these powerful tools

Pseudomonas aeruginosa Population Structure Revisited

At present there are strong indications that Pseudomonas aeruginosa exhibits an epidemic population structure; clinical isolates are indistinguishable from environmental isolates, and they do not exhibit a specific (disease) habitat selection. However, some important issues, such as the worldwide emergence of highly transmissible P. aeruginosa clones among cystic fibrosis (CF) patients and the spread and persistence of multidrug resistant (MDR) strains in hospital wards with high antibiotic pressure, remain contentious. To further investigate the population structure of P. aeruginosa, eight parameters were analyzed and combined for 328 unrelated isolates, collected over the last 125 years from 69 localities in 30 countries on five continents, from diverse clinical (human and animal) and environmental habitats. The analysed parameters were: i) O serotype, ii) Fluorescent Amplified-Fragment Length Polymorphism (FALFP) pattern, nucleotide sequences of outer membrane protein genes, iii) oprI, iv) oprL, v) oprD, vi) pyoverdine receptor gene profile (fpvA type and fpvB prevalence), and prevalence of vii) exoenzyme genes exoS and exoU and viii) group I pilin glycosyltransferase gene tfpO. These traits were combined and analysed using biological data analysis software and visualized in the form of a minimum spanning tree (MST). We revealed a network of relationships between all analyzed parameters and non-congruence between experiments. At the same time we observed several conserved clones, characterized by an almost identical data set. These observations confirm the nonclonal epidemic population structure of P. aeruginosa, a superficially clonal structure with frequent recombinations, in which occasionally highly successful epidemic clones arise. One of these clones is the renown and widespread MDR serotype O12 clone. On the other hand, we found no evidence for a widespread CF transmissible clone. All but one of the 43 analysed CF strains belonged to a ubiquitous P. aeruginosa “core lineage” and typically exhibited the exoS+/exoU− genotype and group B oprL and oprD alleles. This is to our knowledge the first report of an MST analysis conducted on a polyphasic data set

Population Structure of Pseudomonas aeruginosa from Five Mediterranean Countries: Evidence for Frequent Recombination and Epidemic Occurrence of CC235

Several studies in recent years have provided evidence that Pseudomonas aeruginosa has a non-clonal population structure punctuated by highly successful epidemic clones or clonal complexes. The role of recombination in the diversification of P. aeruginosa clones has been suggested, but not yet demonstrated using multi-locus sequence typing (MLST). Isolates of P. aeruginosa from five Mediterranean countries (n = 141) were subjected to pulsed-field gel electrophoresis (PFGE), serotyping and PCR targeting the virulence genes exoS and exoU. The occurrence of multi-resistance (≥3 antipseudomonal drugs) was analyzed with disk diffusion according to EUCAST. MLST was performed on a subset of strains (n = 110) most of them had a distinct PFGE variant. MLST data were analyzed with Bionumerics 6.0, using minimal spanning tree (MST) as well as eBURST. Measurement of clonality was assessed by the standardized index of association (IAS). Evidence of recombination was estimated by ClonalFrame as well as SplitsTree4.0. The MST analysis connected 70 sequence types, among which ST235 was by far the most common. ST235 was very frequently associated with the O11 serotype, and frequently displayed multi-resistance and the virulence genotype exoS−/exoU+. ClonalFrame linked several groups previously identified by eBURST and MST, and provided insight to the evolutionary events occurring in the population; the recombination/mutation ratio was found to be 8.4. A Neighbor-Net analysis based on the concatenated sequences revealed a complex network, providing evidence of frequent recombination. The index of association when all the strains were considered indicated a freely recombining population. P. aeruginosa isolates from the Mediterranean countries display an epidemic population structure, particularly dominated by ST235-O11, which has earlier also been coupled to the spread of ß-lactamases in many countries

Roles of linear ubiquitinylation, a crucial regulator of NF-κB and cell death, in the immune system.

Linear ubiquitinylation, a newly identified post-translational modification, is catalyzed by the linear ubiquitin assembly complex (LUBAC), which is composed of three different subunits, HOIL-1L (heme-oxidized IRP2 ligase 1L), HOIP (HOIL-1 interacting protein), and SHARPIN (SHANK-associated RH domain-interacting protein). LUBAC plays a critical role in the activation of nuclear factor-κB (NF-κB) signaling triggered by a variety of stimuli, including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and pathogen-derived components, and in the protection from cell death. Loss of function of SHARPIN in mice triggers chronic inflammation in multiple organs including the skin, as well as immunodeficiency. In humans, mutations in the gene encoding HOIL-1L cause chronic hyperinflammation and immunodeficiency, which are both associated with decreased levels of LUBAC. The linear ubiquitinylation activity of LUBAC is indispensable for B-cell function in mice, and hyperactivation of LUBAC is associated with oncogenesis in certain forms of B-cell lymphoma. In this review, the current understanding of the biochemistry of LUBAC-mediated linear ubiquitinylation and its involvement in the immune system are discussed