scholarly journals Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yize Zhao ◽  
Changgee Chang ◽  
Qi Long
Keyword(s):  
Statistical Learning ◽  
Current Knowledge ◽  
Complex Diseases ◽  
High Dimensional ◽  
Future Research ◽  
Great Promise ◽  
Biological Knowledge ◽  
Precision Oncology ◽  
Omics Data ◽  
Learning Methods

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.

scholarly journals Some recent statistical learning methods for longitudinal high-dimensional data

2013 ◽  
Vol 6 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Shuo Chen ◽  
Edward Grant ◽  
Tong Tong Wu ◽  
F. DuBois Bowman
Keyword(s):  
Statistical Learning ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Methods

scholarly journals Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

2017 ◽  
Author(s):  
Genevieve L. Stein-O’Brien ◽  
Raman Arora ◽  
Aedin C. Culhane ◽  
Alexander V. Favorov ◽  
Lana X. Garmire ◽  
...  
Keyword(s):  
High Throughput ◽  
Matrix Factorization ◽  
Time Course ◽  
High Dimensional Data ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Biological Knowledge ◽  
Omics Data ◽  
Cellular Interactions ◽  
Low Dimensional

AbstractOmics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge—answering questions from high-dimensional data that we have not yet thought to ask.

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

2021 ◽  
Vol 23 ◽  
Author(s):  
Xiong Li ◽  
Yangping Qiu ◽  
Juan Zhou ◽  
Ziruo Xie
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Joint Analysis ◽  
Future Research ◽  
Omics Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Source Data

Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.

Amruthapala (Decalepis arayalpathra (J. Joseph and V. Chandras.) Venter): A Comprehensive Review on Diversity, Therapeutic Uses, and Valorization of Bioactive Constituents

2019 ◽  
Vol 20 (5) ◽  
pp. 376-389 ◽  
Author(s):  
Sonali Mishra ◽  
Nupur Srivastava ◽  
Velusamy Sundaresan ◽  
Karuna Shanker
Keyword(s):  
Secondary Metabolite ◽  
Current Knowledge ◽  
Experimental Studies ◽  
Critical Discussion ◽  
Future Research ◽  
Bioactive Constituents ◽  
Medicinal Uses ◽  
Comprehensive Review ◽  
Therapeutic Uses ◽  
Decalepis Arayalpathra

Background: Decalepis arayalpathra (J. Joseph and V. Chandras.) Venter is used primarily for nutrition besides its therapeutic values. Traditional preparations/formulations from its tuber are used as a vitalizer and blood purifier drink. The folklore medicinal uses cover inflammation, cough, wound healing, antipyretic, and digestive system management. A comprehensive review of the current understanding of the plant is required due to emerging concerns over its safety and efficacy. Objective: The systematic collection of the authentic information from different sources with the critical discussion is summarised in order to address various issues related to botanical identity, therapeutic medicine, nutritional usage, phytochemical, and pharmacological potentials of the D. arayalpathra. Current use of traditional systems of medicine can be used to expand future research opportunities. Materials and Methods: Available scripted information was collected manually, from peered review research papers and international databases viz. Science Direct, Google Scholar, SciFinder, Scopus, etc. The unpublished resources which were not available in database were collected through the classical books of ‘Ayurveda’ and ‘Siddha’ published in regional languages. The information from books, Ph.D. and MSc dissertations, conference papers and government reports were also collected. We thoroughly screened the scripted information of classical books, titles, abstracts, reports, and full-texts of the journals to establish the reliability of the content. Results: Tuber bearing vanilla like signature flavor is due to the presence of 2-hydroxy-4-methoxybenzaldehyde (HMB). Among five other species, Decalepis arayalpathra (DA) has come under the ‘critically endangered’ category, due to over-exploitation for traditional, therapeutic and cool drink use. The experimental studies proved that it possesses gastro-protective, anti-tumor, and antiinflammatory activities. Some efforts were also made to develop better therapeutics by logical modifications in 2-Hydroxy-4-methoxy-benzaldehyde, which is a major secondary metabolite of D. arayalpathra. ‘Amruthapala’ offers the enormous opportunity to develop herbal drink with health benefits like gastro-protective, anti-oxidant and anti-inflammatory actions. Results: The plant has the potential to generate the investigational new lead (IND) based on its major secondary metabolite i.e. 2-Hydroxy-4-methoxy-benzaldehyde. The present mini-review summarizes the current knowledge on Decalepis arayalpathra, covering its phytochemical diversity, biological potentials, strategies for its conservation, and intellectual property rights (IPR) status. Chemical Compounds: 2-hydroxy-4-methoxybenzaldehyde (Pubchem CID: 69600), α-amyrin acetate (Pubchem CID: 293754), Magnificol (Pubchem CID: 44575983), β-sitosterol (Pubchem CID: 222284), 3-hydroxy-p-anisaldehyde (Pubchem CID: 12127), Naringenin (Pubchem CID: 932), Kaempferol (Pubchem CID: 5280863), Aromadendrin (Pubchem CID: 122850), 3-methoxy-1,2-cyclopentanedione (Pubchem CID: 61209), p-anisaldehyde (Pubchem CID: 31244), Menthyl acetate (Pubchem CID: 27867), Benzaldehyde (Pubchem CID: 240), p-cymene (Pubchem CID: 7463), Salicylaldehyde (Pubchem CID: 6998), 10-epi-γ-eudesmol (Pubchem CID: 6430754), α -amyrin (Pubchem CID: 225688), 3-hydroxy-4-methoxy benzaldehyde (Pubchem CID: 12127).

Disparities in Health Between Black and White Americans: Current Knowledge and Directions for Future Research

2018 ◽  
pp. 456-478
Author(s):  
Thomas E. Fuller-Rowell ◽  
David S. Curtis ◽  
Adrienne M. Duke
Keyword(s):  
Health Disparities ◽  
Current Knowledge ◽  
Public Investment ◽  
The United States ◽  
Future Research ◽  
Sociocultural Factors ◽  
White Americans ◽  
Biological Mechanisms ◽  
Black And White ◽  
Racial Ethnic

Conceptual frameworks for racial/ethnic health disparities are abundant, but many have received insufficient empirical attention. As a result, there are substantial gaps in scientific knowledge and a range of untested hypotheses. Particularly lacking is specificity in behavioral and biological mechanisms for such disparities and their underlying social determinants. Alongside lack of political will and public investment, insufficient clarity in mechanisms has stymied efforts to address racial health disparities. Capitalizing on emergent findings from the Midlife in the United States (MIDUS) study and other longitudinal studies of aging, this chapter evaluates research on health disparities between black and white US adults. Attention is given to candidate behavioral and biological mechanisms as precursors to group differences in morbidity and mortality and to environmental and sociocultural factors that may underlie these mechanisms. Future research topics are discussed, emphasizing those that offer promise with respect to illuminating practical solutions to racial/ethnic health disparities.

scholarly journals Protocol for a systematic review on the experience of informal caregivers for people with a moderate to advanced dementia within a domestic home setting

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Charles James ◽  
Catherine Walshe ◽  
Katherine Froggatt
Keyword(s):  
Systematic Review ◽  
Negative Impact ◽  
Current Knowledge ◽  
Informal Caregivers ◽  
Informal Caregiving ◽  
Bibliographic Databases ◽  
Future Research ◽  
Advanced Dementia ◽  
Home Setting ◽  
Search Terms

Abstract Background The knowledge about the experience of informal caregivers who provide care to people with moderate to advanced dementia in a domestic home setting is limited. A consequence of long hours of caregiving in addition to dealing with normal challenges of daily living is their experience of a poor quality of life. Some of their experiences may be described in terms of a feeling of powerlessness to make changes during care provision. This feeling may also suggest an experience of moral distress. The aim of this systematic review is to synthesise qualitative evidence relating to these experiences. Methods This review adopts a narrative synthesis approach. A search will be conducted for studies written in the English language in the bibliographic databases MEDLINE Complete, CINAHL, EMBASE, PsycINFO, Web of Science and Academic Search Complete covering periods from 1984 to present. Included studies will be qualitative or mixed-methods designs. The search terms will be related to dementia and caregivers, and the process will be focused on dementia at the moderate to the advanced stages within the domestic home setting. Reference lists of included papers will also be searched for additional relevant citations. Search terms and strategies will be checked by two independent reviewers. The identification of abstracts and full texts of studies will be done by the author, while the quality and the risk of bias will also be checked by the two independent reviewers. Discussion Psychological distress is cited as an experience reported within informal caregiving. For the caregiver, it is associated with a negative impact on general health. To date, no synthesis exists on the specific experience of informal caregiving for people with moderate to advanced dementia within the domestic home setting. This review considers that variation of accounts contributes to how the informal caregivers’ general experience is explored in future research. This may enable gaps in current knowledge to be highlighted within the wider context of caregiving in the domestic home setting. Systematic review registration This review is registered with PROSPERO (CRD42020183649).

scholarly journals Molecular Mechanisms of Insulin Resistance in Polycystic Ovary Syndrome: Unraveling the Conundrum in Skeletal Muscle?

2019 ◽  
Vol 104 (11) ◽  
pp. 5372-5381 ◽  
Author(s):  
Nigel K Stepto ◽  
Alba Moreno-Asso ◽  
Luke C McIlvenna ◽  
Kirsty A Walters ◽  
Raymond J Rodgers
Keyword(s):  
Insulin Resistance ◽  
Skeletal Muscle ◽  
Polycystic Ovary Syndrome ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Polycystic Ovary ◽  
Evidence Synthesis ◽  
Basic Research ◽  
Future Research ◽  
Ovary Syndrome

Abstract Context Polycystic ovary syndrome (PCOS) is a common endocrine condition affecting 8% to 13% of women across the lifespan. PCOS affects reproductive, metabolic, and mental health, generating a considerable health burden. Advances in treatment of women with PCOS has been hampered by evolving diagnostic criteria and poor recognition by clinicians. This has resulted in limited clinical and basic research. In this study, we provide insights into the current and future research on the metabolic features of PCOS, specifically as they relate to PCOS-specific insulin resistance (IR), that may affect the most metabolically active tissue, skeletal muscle. Current Knowledge PCOS is a highly heritable condition, yet it is phenotypically heterogeneous in both reproductive and metabolic features. Human studies thus far have not identified molecular mechanisms of PCOS-specific IR in skeletal muscle. However, recent research has provided new insights that implicate energy-sensing pathways regulated via epigenomic and resultant transcriptomic changes. Animal models, while in existence, have been underused in exploring molecular mechanisms of IR in PCOS and specifically in skeletal muscle. Future Directions Based on the latest evidence synthesis and technologies, researchers exploring molecular mechanisms of IR in PCOS, specifically in muscle, will likely need to generate new hypothesis to be tested in human and animal studies. Conclusion Investigations to elucidate the molecular mechanisms driving IR in PCOS are in their early stages, yet remarkable advances have been made in skeletal muscle. Overall, investigations have thus far created more questions than answers, which provide new opportunities to study complex endocrine conditions.

Ways that nurse practitioner students self-explain during diagnostic reasoning

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Leah Burt ◽  
Susan Corbridge ◽  
Colleen Corte ◽  
Laurie Quinn ◽  
Lorna Finnegan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Health Care Providers ◽  
Nurse Practitioner ◽  
Descriptive Analysis ◽  
Diagnostic Errors ◽  
Diagnostic Reasoning ◽  
Knowledge Structures ◽  
Future Research ◽  
Biological Knowledge ◽  
Care Providers

Abstract Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

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