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Yuan H, Mancuso CA, Johnson K, Braasch I, Krishnan A. Computational strategies for cross-species knowledge transfer and translational biomedicine. ARXIV 2024:arXiv:2408.08503v1. [PMID: 39184546 PMCID: PMC11343225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.
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Affiliation(s)
- Hao Yuan
- Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Christopher A. Mancuso
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus
| | - Kayla Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
| | - Ingo Braasch
- Department of Integrative Biology; Genetics and Genome Science Program; Ecology, Evolution, and Behavior Program, Michigan State University
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
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2
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Meier TA, Refahi MS, Hearne G, Restifo DS, Munoz-Acuna R, Rosen GL, Woloszynek S. The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain. Curr Pain Headache Rep 2024; 28:769-784. [PMID: 38822995 DOI: 10.1007/s11916-024-01264-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.
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Affiliation(s)
| | - Mohammad S Refahi
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Gavin Hearne
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | | | - Ricardo Munoz-Acuna
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Stephen Woloszynek
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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3
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Wu Z(E, Xu D, Hu PJH, Huang TS. A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients. J Am Med Inform Assoc 2023; 30:846-858. [PMID: 36794643 PMCID: PMC10114116 DOI: 10.1093/jamia/ocad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/26/2022] [Accepted: 01/25/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVE Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value. MATERIALS AND METHODS The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC). RESULTS We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods. DISCUSSION AND CONCLUSION The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.
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Affiliation(s)
- Zejian (Eric) Wu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Da Xu
- Department of Information Systems, College of Business, California State University Long Beach, Long Beach, California, USA
| | - Paul Jen-Hwa Hu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Ting-Shuo Huang
- Department of General Surgery, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
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4
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Yang X, Xu W, Leng D, Wen Y, Wu L, Li R, Huang J, Bo X, He S. Exploring novel disease-disease associations based on multi-view fusion network. Comput Struct Biotechnol J 2023; 21:1807-1819. [PMID: 36923471 PMCID: PMC10009443 DOI: 10.1016/j.csbj.2023.02.038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/02/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.
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Affiliation(s)
- Xiaoxi Yang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.,Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Wenjian Xu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.,Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.,MOE Key Laboratory of Major Diseases in Children, Beijing 100045, China.,Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing 100045, China
| | - Dongjin Leng
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Lianlian Wu
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Huang
- Clinical Medicine Institute, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
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5
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Iwata M, Kosai K, Ono Y, Oki S, Mimori K, Yamanishi Y. Regulome-based characterization of drug activity across the human diseasome. NPJ Syst Biol Appl 2022; 8:44. [DOI: 10.1038/s41540-022-00255-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractDrugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.
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6
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Artificial Intelligence and Cardiovascular Genetics. Life (Basel) 2022; 12:life12020279. [PMID: 35207566 PMCID: PMC8875522 DOI: 10.3390/life12020279] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/26/2022] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
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7
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Wang L, Wu M, Wu Y, Zhang X, Li S, He M, Zhang F, Wang Y, Li J. Prediction of the Disease Causal Genes Based on Heterogeneous Network and Multi-Feature Combination Method. Comput Biol Chem 2022; 97:107639. [DOI: 10.1016/j.compbiolchem.2022.107639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/05/2022] [Accepted: 02/07/2022] [Indexed: 11/30/2022]
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8
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Guo A, Khan YM, Langabeer JR, Foraker RE. Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset. Sci Rep 2021; 11:20969. [PMID: 34697328 PMCID: PMC8547216 DOI: 10.1038/s41598-021-00345-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/22/2021] [Indexed: 11/29/2022] Open
Abstract
Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, USA.
| | - Yosef M Khan
- Health Informatics and Analytics, Centers for Health Metrics and Evaluation, American Heart Association, Dallas, TX, USA
| | - James R Langabeer
- School of Biomedical Informatics, Health Science Center at Houston, The University of Texas, Houston, TX, USA
| | - Randi E Foraker
- Institute for Informatics (I2), Washington University School of Medicine, St. Louis, MO, USA
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA
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9
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Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. ENTROPY 2021; 23:e23080997. [PMID: 34441136 PMCID: PMC8391962 DOI: 10.3390/e23080997] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 02/01/2023]
Abstract
In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered as an implicit probability distribution estimation under the assumption that there exists a true contextual distribution among words. Therefore, we apply information criteria with the aim of selecting the best dimensionality so that the corresponding model can be as close as possible to the true distribution. We examine the following information criteria for the dimensionality selection problem: the Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sequential Normalized Maximum Likelihood (SNML) criterion. SNML is the total codelength required for the sequential encoding of a data sequence on the basis of the minimum description length. The proposed approach is applied to both the original SG model and the SG Negative Sampling model to clarify the idea of using information criteria. Additionally, as the original SNML suffers from computational disadvantages, we introduce novel heuristics for its efficient computation. Moreover, we empirically demonstrate that SNML outperforms both BIC and AIC. In comparison with other evaluation methods for word embedding, the dimensionality selected by SNML is significantly closer to the optimal dimensionality obtained by word analogy or word similarity tasks.
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10
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Applications of Deep Learning in Biomedicine. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11507-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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11
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Zhou F, Gillespie A, Gligorijevic D, Gligorijevic J, Obradovic Z. Use of disease embedding technique to predict the risk of progression to end-stage renal disease. J Biomed Inform 2020; 105:103409. [PMID: 32304869 PMCID: PMC9885429 DOI: 10.1016/j.jbi.2020.103409] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 02/01/2023]
Abstract
The accurate prediction of progression of Chronic Kidney Disease (CKD) to End Stage Renal Disease (ESRD) is of great importance to clinicians and a challenge to researchers as there are many causes and even more comorbidities that are ignored by the traditional prediction models. We examine whether utilizing a novel low-dimensional embedding model disease2disease (D2D) learned from a large-scale electronic health records (EHRs) could well clusters the causes of kidney diseases and comorbidities and further improve prediction of progression of CKD to ESRD compared to traditional risk factors. The study cohort consists of 2,507 hospitalized Stage 3 CKD patients of which 1,375 (54.8%) progressed to ESRD within 3 years. We evaluated the proposed unsupervised learning framework by applying a regularized logistic regression model and a cox proportional hazard model respectively, and compared the accuracies with the ones obtained by four alternative models. The results demonstrate that the learned low-dimensional disease representations from EHRs can capture the relationship between vast arrays of diseases, and can outperform traditional risk factors in a CKD progression prediction model. These results can be used both by clinicians in patient care and researchers to develop new prediction methods.
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Affiliation(s)
- Fang Zhou
- School of Data Science & Engineering, East China Normal University, Shanghai, China
| | - Avrum Gillespie
- Division of Nephrology, Hypertension, and Kidney Transplantation, Department of Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA
| | - Djordje Gligorijevic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA
| | - Jelena Gligorijevic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA
| | - Zoran Obradovic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA
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12
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Ma C, Li Y, Shia B, Ma S. Human disease cost network analysis. Stat Med 2020; 39:1237-1249. [DOI: 10.1002/sim.8472] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 08/30/2019] [Accepted: 12/18/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Chenjin Ma
- School of StatisticsRenmin University of China Haidian China
- Department of BiostatisticsYale University New Haven Connecticut
| | - Yang Li
- School of StatisticsRenmin University of China Haidian China
- Center for Applied StatisticsRenmin University of China Haidian China
| | - BenChang Shia
- School of ManagementTaipei Medical University Taipei Taiwan
| | - Shuangge Ma
- School of StatisticsRenmin University of China Haidian China
- Department of BiostatisticsYale University New Haven Connecticut
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13
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Yang K, Wang R, Liu G, Shu Z, Wang N, Zhang R, Yu J, Chen J, Li X, Zhou X. HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction. IEEE J Biomed Health Inform 2020; 23:1805-1815. [PMID: 31283472 DOI: 10.1109/jbhi.2018.2870728] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease-related (e.g., symptoms) and gene-related (e.g., gene ontology and protein-protein interactions) information to improve the performance of disease gene prediction is still an issue. Here, we develop a heterogeneous disease-gene-related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred). Based on this framework, a low-dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR-based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease-gene network (RW-RDGN), to predict disease genes with high performance. First, to validate the rationality of the framework, we analyze the similarity-based overlap distribution of disease pairs and design an experiment for disease-gene association recovery, the results of which revealed that the LVR of nodes performs well at preserving the local and global network structure of the HDGN. Then, we apply tenfold cross validation and external validation to compare our methods with other well-known disease gene prediction algorithms. The experimental results show that the RW-RDGN performs better than the state-of-the-art algorithm. The prediction results of disease candidate genes are essential for molecular mechanism investigation and experimental validation. The source codes of HerGePred and experimental data are available at https://github.com/yangkuoone/HerGePred.
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14
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Kirk IK, Simon C, Banasik K, Holm PC, Haue AD, Jensen PB, Juhl Jensen L, Rodríguez CL, Pedersen MK, Eriksson R, Andersen HU, Almdal T, Bork-Jensen J, Grarup N, Borch-Johnsen K, Pedersen O, Pociot F, Hansen T, Bergholdt R, Rossing P, Brunak S. Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining. eLife 2019; 8:44941. [PMID: 31818369 PMCID: PMC6904221 DOI: 10.7554/elife.44941] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 11/16/2019] [Indexed: 12/13/2022] Open
Abstract
Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.
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Affiliation(s)
- Isa Kristina Kirk
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christian Simon
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Peter Christoffer Holm
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Amalie Dahl Haue
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Peter Bjødstrup Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,Odense Patient Data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Cristina Leal Rodríguez
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Mette Krogh Pedersen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Robert Eriksson
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Thomas Almdal
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Oluf Pedersen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Pociot
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Department of Clinical Medicine, Herlev-Gentofte Hospital, Herlev, Denmark
| | - Torben Hansen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,Center for Biological Sequence Analysis, Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
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15
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Gligorijevic J, Gligorijevic D, Pavlovski M, Milkovits E, Glass L, Grier K, Vankireddy P, Obradovic Z. Optimizing clinical trials recruitment via deep learning. J Am Med Inform Assoc 2019; 26:1195-1202. [PMID: 31188432 PMCID: PMC7647233 DOI: 10.1093/jamia/ocz064] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 04/11/2019] [Accepted: 04/20/2019] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. MATERIALS AND METHODS To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. RESULTS Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. DISCUSSION The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. CONCLUSION Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.
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Affiliation(s)
- Jelena Gligorijevic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA
| | - Djordje Gligorijevic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA
| | - Martin Pavlovski
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA
- Macedonian Academy of Sciences and Arts, Skopje, Republic of Macedonia
| | | | | | | | | | - Zoran Obradovic
- Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA
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16
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Akiyoshi S, Iwata M, Berenger F, Yamanishi Y. Omics-based Identification of Glycan Structures as Biomarkers for a Variety of Diseases. Mol Inform 2019; 39:e1900112. [PMID: 31622036 DOI: 10.1002/minf.201900112] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022]
Abstract
Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of glycogenes (glycosyltransferases and glycosidases) for 79 different diseases. A biological pathway-based glycogene signature was constructed to identify the alteration in glycan biosynthesis and the associated glycan structures for each disease state. The degradation of N-glycan and keratan sulfate, for example, may promote the growth or metastasis of multiple types of cancer, including endometrial, gastric, and nasopharyngeal. Our results also revealed that commonalities between diseases can be interpreted using glycogene expression patterns, as well as the associated glycan structure patterns at the level of the affected pathway. The proposed method is expected to be useful for understanding the relationships between glycans, glycogenes, and disease and identifying disease-specific glycan biomarkers.
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Affiliation(s)
- Sayaka Akiyoshi
- Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan
| | - Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
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17
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Iwata M, Yuan L, Zhao Q, Tabei Y, Berenger F, Sawada R, Akiyoshi S, Hamano M, Yamanishi Y. Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm. Bioinformatics 2019; 35:i191-i199. [PMID: 31510663 PMCID: PMC6612872 DOI: 10.1093/bioinformatics/btz313] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications. RESULTS Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Longhao Yuan
- Graduate School of Engineering, Saitama Institute of Technology, Fukaya, Saitama, Japan
- RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan
| | - Qibin Zhao
- RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan
- School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China
| | - Yasuo Tabei
- RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan
| | - Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Sayaka Akiyoshi
- Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Momoko Hamano
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- PRESTO Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
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18
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Dozmorov MG. Disease classification: from phenotypic similarity to integrative genomics and beyond. Brief Bioinform 2019; 20:1769-1780. [DOI: 10.1093/bib/bby049] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
Abstract
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA
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Dozmorov MG. Reforming disease classification system-are we there yet? ANNALS OF TRANSLATIONAL MEDICINE 2018; 6:S30. [PMID: 30613605 DOI: 10.21037/atm.2018.09.36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA
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20
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Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15:20170387. [PMID: 29618526 PMCID: PMC5938574 DOI: 10.1098/rsif.2017.0387] [Citation(s) in RCA: 826] [Impact Index Per Article: 137.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 03/07/2018] [Indexed: 11/12/2022] Open
Abstract
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
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Affiliation(s)
- Travers Ching
- Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Daniel S Himmelstein
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brett K Beaulieu-Jones
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Enrico Ferrero
- Computational Biology and Stats, Target Sciences, GlaxoSmithKline, Stevenage, UK
| | | | - Michael Zietz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xie
- Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Benjamin J Lengerich
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Johnny Israeli
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Jack Lanchantin
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Stephen Woloszynek
- Ecological and Evolutionary Signal-processing and Informatics Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Avanti Shrikumar
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL, USA
| | - Evan M Cofer
- Department of Computer Science, Trinity University, San Antonio, TX, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Christopher A Lavender
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Srinivas C Turaga
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - Amr M Alexandari
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - David J Harris
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | | | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Yifan Peng
- National Center for Biotechnology Information and National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Wiley
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Marwin H S Segler
- Institute of Organic Chemistry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University in Saint Louis, St Louis, MO, USA
| | - Austin Huang
- Department of Medicine, Brown University, Providence, RI, USA
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
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Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
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22
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Laubichler MD, Prohaska SJ, Stadler PF. Toward a mechanistic explanation of phenotypic evolution: The need for a theory of theory integration. JOURNAL OF EXPERIMENTAL ZOOLOGY PART B-MOLECULAR AND DEVELOPMENTAL EVOLUTION 2018; 330:5-14. [DOI: 10.1002/jez.b.22785] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Revised: 11/03/2017] [Accepted: 11/15/2017] [Indexed: 01/01/2023]
Affiliation(s)
- Manfred D. Laubichler
- School of Life Sciences; Arizona State University; Tempe Arizona
- Marine Biological Laboratory; Woods Hole; Massachusetts
- Santa Fe Institute; Santa Fe New Mexico
| | - Sonja J. Prohaska
- Santa Fe Institute; Santa Fe New Mexico
- Computational EvoDevo Group; Department of Computer Science; Leipzig Germany
- Interdisciplinary Center of Bioinformatics; University of Leipzig; Leipzig Germany
| | - Peter F. Stadler
- Santa Fe Institute; Santa Fe New Mexico
- Interdisciplinary Center of Bioinformatics; University of Leipzig; Leipzig Germany
- Bioinformatics Group, Department of Computer Science; University of Leipzig; Leipzig Germany
- Max-Planck Institute for Mathematics in the Sciences; Leipzig Germany
- Fraunhofer Institut für Zelltherapie und Immunologie-IZI; Leipzig Germany. Department of Theoretical Chemistry; University of Vienna; Wien Austria. Center for Non-Coding RNA in Technology and Health; University of Copenhagen; Frederiksberg Denmark
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23
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