Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

Pilot study on developing a decision support tool for guiding re-administration of chemotherapeutic agent after a serious adverse drug reaction

<p>Abstract</p> <p>Background</p> <p>Currently, there are no standard guidelines for recommending re-administration of a chemotherapeutic drug to a patient after a serious adverse drug reaction (ADR) incident. The decision on whether to rechallenge the patient is based on the experience of the clinician and is highly subjective. Thus the aim of this study is to develop a decision support tool to assist clinicians in this decision making process.</p> <p>Methods</p> <p>The inclusion criteria for patients in this study are: (1) had chemotherapy at National Cancer Centre Singapore between 2004 to 2009, (2) suffered from serious ADRs, and (3) were rechallenged. A total of 46 patients fulfilled the inclusion criteria. A genetic algorithm attribute selection method was used to identify clinical predictors for patients' rechallenge status. A Naïve Bayes model was then developed using 35 patients and externally validated using 11 patients.</p> <p>Results</p> <p>Eight patient attributes (age, chemotherapeutic drug, albumin level, red blood cell level, platelet level, abnormal white blood cell level, abnormal alkaline phosphatase level and abnormal alanine aminotransferase level) were identified as clinical predictors for rechallenge status of patients. The Naïve Bayes model had an AUC of 0.767 and was found to be useful for assisting clinical decision making after clinicians had identified a group of patients for rechallenge. A platform independent version and an online version of the model is available to facilitate independent validation of the model.</p> <p>Conclusion</p> <p>Due to the limited size of the validation set, a more extensive validation of the model is necessary before it can be adopted for routine clinical use. Once validated, the model can be used to assist clinicians in deciding whether to rechallenge patients by determining if their initial assessment of rechallenge status of patients is accurate.</p

The ASY-EOS experiment at GSI: investigating the symmetry energy at supra-saturation densities

The elliptic-flow ratio of neutrons with respect to protons in reactions of neutron rich heavy-ions systems at intermediate energies has been proposed as an observable sensitive to the strength of the symmetry term in the nuclear Equation Of State (EOS) at supra-saturation densities. The recent results obtained from the existing FOPI/LAND data for $^{197}$Au+$^{197}$Au collisions at 400 MeV/nucleon in comparison with the UrQMD model allowed a first estimate of the symmetry term of the EOS but suffer from a considerable statistical uncertainty. In order to obtain an improved data set for Au+Au collisions and to extend the study to other systems, a new experiment was carried out at the GSI laboratory by the ASY-EOS collaboration in May 2011.Comment: Talk given by P. Russotto at the 11th International Conference on Nucleus-Nucleus Collisions (NN2012), San Antonio, Texas, USA, May 27-June 1, 2012. To appear in the NN2012 Proceedings in Journal of Physics: Conference Series (JPCS

Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers

The piRNA-pathway factor FKBP6 is essential for spermatogenesis but dispensable for control of meiotic LINE-1 expression in humans

Infertility affects around 7% of the male population and can be due to severe spermatogenic failure (SPGF), resulting in no or very few sperm in the ejaculate. We initially identified a homozygous frameshift variant in FKBP6 in a man with extreme oligozoospermia. Subsequently, we screened a total of 2,699 men with SPGF and detected rare bi-allelic loss-of-function variants in FKBP6 in five additional persons. All six individuals had no or extremely few sperm in the ejaculate, which were not suitable for medically assisted reproduction. Evaluation of testicular tissue revealed an arrest at the stage of round spermatids. Lack of FKBP6 expression in the testis was confirmed by RT-qPCR and immunofluorescence staining. In mice, Fkbp6 is essential for spermatogenesis and has been described as being involved in piRNA biogenesis and formation of the synaptonemal complex (SC). We did not detect FKBP6 as part of the SC in normal human spermatocytes, but small RNA sequencing revealed that loss of FKBP6 severely impacted piRNA levels, supporting a role for FKBP6 in piRNA biogenesis in humans. In contrast to findings in piRNA-pathway mouse models, we did not detect an increase in LINE-1 expression in men with pathogenic FKBP6 variants. Based on our findings, FKBP6 reaches a "strong" level of evidence for being associated with male infertility according to the ClinGen criteria, making it directly applicable for clinical diagnostics. This will improve patient care by providing a causal diagnosis and will help to predict chances for successful surgical sperm retrieval

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Wittler R, Chauve C. Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes. BMC Bioinformatics. 2011;12(Suppl. 9):S21

Automated systems to identify relevant documents in product risk management

<p>Abstract</p> <p>Background</p> <p>Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems.</p> <p>Results</p> <p>The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system.</p> <p>Conclusions</p> <p>Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency of product risk management can be improved with the use of specific automated systems.</p

A tudor domain protein SPINDLIN1 interacts with the mRNA-binding protein SERBP1 and is involved in mouse oocyte meiotic resumption

Mammalian oocytes are arrested at prophase I of meiosis, and resume meiosis prior to ovulation. Coordination of meiotic arrest and resumption is partly dependent on the post-transcriptional regulation of maternal transcripts. Here, we report that, SPINDLIN1 (SPIN1), a maternal protein containing Tudor-like domains, interacts with a known mRNA-binding protein SERBP1, and is involved in regulating maternal transcripts to control meiotic resumption. Mouse oocytes deficient for Spin1 undergo normal folliculogenesis, but are defective in resuming meiosis. SPIN1, via its Tudor-like domain, forms a ribonucleoprotein complex with SERBP1, and regulating mRNA stability and/or translation. The mRNA for the cAMP-degrading enzyme, PDE3A, is reduced in Spin1 mutant oocytes, possibly contributing to meiotic arrest. Our study demonstrates that Spin1 regulates maternal transcripts post-transcriptionally and is involved in meiotic resumption.Ting Gang Chew, Anne Peaston, Ai Khim Lim, Chanchao Lorthongpanich, Barbara B. Knowles, Davor Solte

Sex and the Single Cell. II. There Is a Time and Place for Sex

In both male and female Drosophila, only a subset of cells have the potential to sexually differentiate, making both males and females mosaics of sexually differentiated and sexually undifferentiated cells