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Modern Applied Statistics With S

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques.

In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

Contact: yvan.saeys@psb.ugent.be

Supplementary information: http://bioinformatics.psb.ugent.be/supplementary_data/yvsae/fsreview

A review of feature selection techniques in bioinformatics

CA : A Cancer Journal for Clinicians

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Oxidative Stress, Inflammation, and Cancer: How Are They Linked?

Placental immune response and its tropism for specific viruses and pathogens affect the outcome of the pregnant woman's susceptibility to and severity of certain infectious diseases. The generalization of pregnancy as a condition of immune suppression or increased risk is misleading and prevents the determination of adequate guidelines for treating pregnant women during pandemics. There is a need to evaluate the interaction of each specific pathogen with the fetal/placental unit and its responses to design the adequate prophylaxis or therapy. The complexity of the immunology of pregnancy and the focus, for many years, on the concept of immunology of pregnancy as an organ transplantation have complicated the field and delayed the development of new guidelines with clinical implications that could help to answer these and other relevant questions. Our challenge as scientists and clinicians interested in the field of reproductive immunology is to evaluate many of the 'classical concepts' to define new approaches for a better understanding of the immunology of pregnancy that will benefit mothers and fetuses in different clinical scenarios.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0897.2010.00836.x

The Immune System in Pregnancy: A Unique Complexity

The concept that pregnancy is associated with immune suppression has created a myth of pregnancy as a state of immunological weakness and, therefore, of increased susceptibility to infectious diseases. A challenging question is whether the maternal immune system is a friend or a foe of pregnancy. In this review, we discuss data associated to the role of the immune system during pregnancy. We propose a new paradigm in terms of the fetal-maternal immune interaction as well as the immunological response of the mother to microorganism. Our challenge is to better understand the immunology of pregnancy in order to deliver the appropriate treatment to patients with pregnancy complications as well as to determine public policies for the protection of pregnant women during pandemics.

/pdf/inflammation-and-pregnancy-the-role-of-the-immune-system-at-40k2f6pag9.pdf

Inflammation and pregnancy: the role of the immune system at the implantation site.

The comparison of the immunological state of pregnancy to an immunosuppressed host-graft model continues to lead research and clinical practice to ill-defined approaches. This Review discusses recent evidence that supports the idea that immunological responses at the receptive maternal-fetal interface are not simply suppressed but are instead highly dynamic. We discuss the crucial role of trophoblast cells in shaping not only the way in which immune cells respond to the invading blastocyst but also how they collectively react to external stimuli. We also discuss the role of the microbiota in promoting a tolerogenic maternal immune system and highlight how subclinical viral infections can disrupt this status quo, leading to pregnancy complications.

The unique immunological and microbial aspects of pregnancy

Motivation: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. Results: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k -nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.

/pdf/comparison-of-statistical-methods-for-classification-of-2998m4gy60.pdf

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

A major burden in the treatment of ovarian cancer is the high percentage of recurrence and chemoresistance. Cancer stem cells (CSCs) provide a reservoir of cells that can self-renew, can maintain the tumor by generating differentiated cells [non-stem cells (non-CSCs)] which make up the bulk of the tumor and may be the primary source of recurrence. We describe the characterization of human ovarian cancer stem cells (OCSCs). These cells have a distinctive genetic profile that confers them with the capacity to recapitulate the original tumor, proliferate with chemotherapy, and promote recurrence. CSC identified in EOC cells isolated form ascites and solid tumors are characterized by: CD44+, MyD88+, constitutive NFkappaB activity and cytokine and chemokine production, high capacity for repair, chemoresistance to conventional chemotherapies, resistance to TNFalpha-mediated apoptosis, capacity to form spheroids in suspension, and the ability to recapitulate in vivo the original tumor. Chemotherapy eliminates the bulk of the tumor but it leaves a core of cancer cells with high capacity for repair and renewal. The molecular properties identified in these cells may explain some of the unique characteristics of CSCs that control self-renewal and drive metastasis. The identification and cloning of human OCSCs can aid in the development of better therapeutic approaches for ovarian cancer patients.

Gil Mor

Papers

The Immune System in Pregnancy: A Unique Complexity

Inflammation and pregnancy: the role of the immune system at the implantation site.

The unique immunological and microbial aspects of pregnancy

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

Molecular phenotyping of human ovarian cancer stem cells unravels the mechanisms for repair and chemoresistance.