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Papastergiou T, Azé J, Bringay S, Louet M, Poncelet P, Rosales-Hurtado M, Vo-Hoang Y, Licznar-Fajardo P, Docquier JD, Gavara L. Discovering NDM-1 inhibitors using molecular substructure embeddings representations. J Integr Bioinform 2023; 0:jib-2022-0050. [PMID: 37498676 PMCID: PMC10389050 DOI: 10.1515/jib-2022-0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds.
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Affiliation(s)
- Thomas Papastergiou
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | - Jérôme Azé
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
| | - Sandra Bringay
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
- AMIS, Paul Valery University, 34199 Montpellier, France
| | - Maxime Louet
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | - Pascal Poncelet
- LIRMM, University of Montpellier, CNRS, 34095 Montpellier, France
| | | | - Yen Vo-Hoang
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
| | | | - Jean-Denis Docquier
- Department of Medical Biotechnologies, University of Siena, I-53100 Siena, Italy
| | - Laurent Gavara
- IBMM, CNRS, University of Montpellier, ENSCM, 34293 Montpellier, France
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2
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Moros L, Azé J, Bringay S, Poncelet P, Servajean M, Dunoyer C. Learning to Classify Medical Discharge Summaries According to ICD-9. Stud Health Technol Inform 2023; 302:773-777. [PMID: 37203493 DOI: 10.3233/shti230264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
CONTEXT We present a post-hoc approach to improve the recall of ICD classification. METHOD The proposed method can use any classifier as a backbone and aims to calibrate the number of codes returned per document. We test our approach on a new stratified split of the MIMIC-III dataset. RESULTS When returning 18 codes on average per document we obtain a recall that is 20% better than a classic classification approach.
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Affiliation(s)
- Leonardo Moros
- LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France
| | - Jérôme Azé
- LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France
| | - Sandra Bringay
- LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France
- AMIS, Paul-Valéry University, Montpellier, France
| | - Pascal Poncelet
- LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France
| | - Maximilien Servajean
- LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France
- AMIS, Paul-Valéry University, Montpellier, France
| | - Caroline Dunoyer
- Medical Information Department, CHU Montpellier, Montpellier, France
- IDESP, UMR UA11, INSERM - University of Montpellier, Montpellier, France
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3
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Le Baher H, Azé J, Bringay S, Poncelet P, Rodriguez N, Dunoyer C. Patient Electronic Health Record as Temporal Graphs for Health Monitoring. Stud Health Technol Inform 2023; 302:561-565. [PMID: 37203748 DOI: 10.3233/shti230205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Machine learning methods are becoming increasingly popular to anticipate critical risks in patients under surveillance reducing the burden on caregivers. In this paper, we propose an original modeling that benefits of recent developments in Graph Convolutional Networks: a patient's journey is seen as a graph, where each node is an event and temporal proximities are represented by weighted directed edges. We evaluated this model to predict death at 24 hours on a real dataset and successfully compared our results with the state of the art.
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Affiliation(s)
- Hugo Le Baher
- LIRMM, UMR 5506, Université de Montpellier, CNRS, Montpellier, France
- 5 DEGRÉS, Paris, France
- Département d'Information Médicale, CHU Montpellier, Montpellier, France
| | | | - Sandra Bringay
- LIRMM, UMR 5506, Université de Montpellier, CNRS, Montpellier, France
- AMIS, Université Paul-Valéry, Montpellier, France
| | - Pascal Poncelet
- LIRMM, UMR 5506, Université de Montpellier, CNRS, Montpellier, France
| | - Nancy Rodriguez
- LIRMM, UMR 5506, Université de Montpellier, CNRS, Montpellier, France
| | - Caroline Dunoyer
- Département d'Information Médicale, CHU Montpellier, Montpellier, France
- IDESP, UMR UA11, INSERM - Université de Montpellier, Montpellier, France
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4
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Morgiève M, Yasri D, Genty C, Dubois J, Leboyer M, Vaiva G, Berrouiguet S, Azé J, Courtet P. Acceptability and satisfaction with emma, a smartphone application dedicated to suicide ecological assessment and prevention. Front Psychiatry 2022; 13:952865. [PMID: 36032223 PMCID: PMC9403788 DOI: 10.3389/fpsyt.2022.952865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND As mHealth may contribute to suicide prevention, we developed emma, an application using Ecological Momentary Assessment and Intervention (EMA/EMI). OBJECTIVE This study evaluated emma usage rate and acceptability during the first month and satisfaction after 1 and 6 months of use. METHODS Ninety-nine patients at high risk of suicide used emma for 6 months. The acceptability and usage rate of the EMA and EMI modules were monitored during the first month. Satisfaction was assessed by questions in the monthly EMA (Likert scale from 0 to 10) and the Mobile App Rating Scale (MARS; score: 0-5) completed at month 6. After inclusion, three follow-up visits (months 1, 3, and 6) took place. RESULTS Seventy-five patients completed at least one of the proposed EMAs. Completion rates were lower for the daily than weekly EMAs (60 and 82%, respectively). The daily completion rates varied according to the question position in the questionnaire (lower for the last questions, LRT = 604.26, df = 1, p-value < 0.0001). Completion rates for the daily EMA were higher in patients with suicidal ideation and/or depression than in those without. The most used EMI was the emergency call module (n = 12). Many users said that they would recommend this application (mean satisfaction score of 6.92 ± 2.78) and the MARS score at month 6 was relatively high (overall rating: 3.3 ± 0.87). CONCLUSION Emma can target and involve patients at high risk of suicide. Given the promising users' satisfaction level, emma could rapidly evolve into a complementary tool for suicide prevention.
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Affiliation(s)
- Margot Morgiève
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,ICM - Paris Brain Institute, Hôpital de la Pitié-Salpêtriére, Paris, France.,GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France
| | - Daniel Yasri
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Catherine Genty
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Jonathan Dubois
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Marion Leboyer
- Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France.,Faculté de Médicine, Institut National de la Santé et de la Recherche Médicale, Université Paris-Est Créteil, Créteil, France.,Assistance Publique Hôpitaux de Paris, Pôle de Psychiatrie et Addictologie, Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Guillaume Vaiva
- CHU Lille, Hôpital Fontan, Department of Psychiatry, Lille, France.,Centre National de Resources and Résilience pour les Psychotraumatisme, Université de Lille, Lille, France.,CNRS UMR-9193, SCALab - Sciences Cognitives et Sciences Affectives, Université de Lille, Lille, France
| | - Sofian Berrouiguet
- Laboratoire du Traitement de l'Information Médicale, INSERM UMR1101, CHRU Brest, Brest, France
| | - Jérôme Azé
- LIRMM, CNRS, Univ Montpellier, Montpellier, France
| | - Philippe Courtet
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France
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5
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Morgiève M, Genty C, Azé J, Dubois J, Leboyer M, Vaiva G, Berrouiguet S, Courtet P. A Digital Companion, the Emma App, for Ecological Momentary Assessment and Prevention of Suicide: Quantitative Case Series Study. JMIR Mhealth Uhealth 2020; 8:e15741. [PMID: 33034567 PMCID: PMC7584985 DOI: 10.2196/15741] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 04/08/2020] [Accepted: 06/03/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Many suicide risk factors have been identified, but traditional clinical methods do not allow for the accurate prediction of suicide behaviors. To face this challenge, emma, an app for ecological momentary assessment (EMA), ecological momentary intervention (EMI), and prediction of suicide risk in high-risk patients, was developed. OBJECTIVE The aim of this case report study was to describe how subjects at high risk of suicide use the emma app in real-world conditions. METHODS The Ecological Mental Momentary Assessment (EMMA) study is an ongoing, longitudinal, interventional, multicenter trial in which patients at high risk for suicide are recruited to test emma, an app designed to be used as a self-help tool for suicidal crisis management. Participants undergo clinical assessment at months 0, 1, 3, and 6 after inclusion, mainly to assess and characterize the presence of mental disorders and suicidal thoughts and behaviors. Patient recruitment is still ongoing. Some data from the first 14 participants who already completed the 6-month follow-up were selected for this case report study, which evaluated the following: (1) data collected by emma (ie, responses to EMAs), (2) metadata on emma use, (3) clinical data, and (4) qualitative assessment of the participants' experiences. RESULTS EMA completion rates were extremely heterogeneous with a sharp decrease over time. The completion rates of the weekly EMAs (25%-87%) were higher than those of the daily EMAs (0%-53%). Most patients (10/14, 71%) answered the EMA questionnaires spontaneously. Similarly, the use of the Safety Plan Modules was very heterogeneous (2-75 times). Specifically, 11 patients out of 14 (79%) used the Call Module (1-29 times), which was designed by our team to help them get in touch with health care professionals and/or relatives during a crisis. The diversity of patient profiles and use of the EMA and EMI modules proposed by emma were highlighted by three case reports. CONCLUSIONS These preliminary results indicate that patients have different clinical and digital profiles and needs that require a highly scalable, interactive, and customizable app. They also suggest that it is possible and acceptable to collect longitudinal, fine-grained, contextualized data (ie, EMA) and to offer personalized intervention (ie, EMI) in real time to people at high risk of suicide. To become a complementary tool for suicide prevention, emma should be integrated into existing emergency procedures. TRIAL REGISTRATION ClinicalTrials.gov NCT03410381; https://clinicaltrials.gov/ct2/show/NCT03410381.
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Affiliation(s)
- Margot Morgiève
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France.,ICM - Brain and Spine Institute, hôpital de la Pitié-Salpêtrière, Paris, France.,GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France
| | - Catherine Genty
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France
| | - Jérôme Azé
- LIRMM, UMR 5506, Montpellier University/CNRS, Montpellier, France
| | - Jonathan Dubois
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France
| | - Marion Leboyer
- Fondation Fondamental, hôpital Albert-Chenevier, Créteil, France
| | - Guillaume Vaiva
- GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France.,CHU Lille, Hôpital Fontan, Department of Psychiatry, Lille, France.,Centre National de Ressources & Résilience pour les psychotraumatismes, Lille, France.,Université de Lille, CNRS UMR-9193, SCALab - Sciences Cognitives et Sciences Affectives, Lille, France
| | - Sofian Berrouiguet
- GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France.,EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France.,IMT Atlantique, Lab-STICC, Brest, France
| | - Philippe Courtet
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, INSERM, Univ Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France.,Fondation Fondamental, hôpital Albert-Chenevier, Créteil, France
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6
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Abstract
PURPOSE OF REVIEW We reviewed how scholars recently addressed the complex relationship that binds distress, affective disorders, and suicidal behaviors on the one hand and social networking on the other. We considered the latest machine learning performances in detecting affective-related outcomes from social media data, and reviewed understandings of how, why, and with what consequences distressed individuals use social network sites. Finally, we examined how these insights may concretely instantiate on the individual level with a qualitative case series. RECENT FINDINGS Machine learning classifiers are progressively stabilizing with moderate to high performances in detecting affective-related diagnosis, symptoms, and risks from social media linguistic markers. Qualitatively, such markers appear to translate ambivalent and socially constrained motivations such as self-disclosure, passive support seeking, and connectedness reinforcement. Binding data science and psychosocial research appears as the unique condition to ground a translational web-clinic for treating and preventing affective-related issues on social media.
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Affiliation(s)
- Charles-Edouard Notredame
- Psychiatry Department, CHU Lille, 2 rue André Verhaeghe, F-59000, Lille, France. .,SCALab, CNRS UMR9193, F-59000, Lille, France. .,Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France. .,Papageno Program, Lille, France.
| | - M Morgiève
- Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France.,Papageno Program, Lille, France.,Centre de Recherche Médecine, Sciences, Santé, Santé Mentale, Société (CERMES3), UMR CNRS 8211-Unité Inserm 988-EHESS-Université Paris Descartes, 75006, Paris, France.,Hôpital de la Pitié-Salpêtrière, ICM - Brain and Spine Institute, 47-83, boulevard de l'hôpital, 75013, Paris, France
| | - F Morel
- Psychiatry Department, CHU Lille, 2 rue André Verhaeghe, F-59000, Lille, France
| | - S Berrouiguet
- Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France.,Centre Hospitalier Régional Universitaire de Brest à Bohars, Pôle de psychiatrie, 29820, Bohars, France
| | - J Azé
- LIRMM, UMR 5506, Montpellier University/CNRS, 860 rue de St Priest, 34095, Montpellier Cedex 5, France
| | - G Vaiva
- Psychiatry Department, CHU Lille, 2 rue André Verhaeghe, F-59000, Lille, France.,SCALab, CNRS UMR9193, F-59000, Lille, France.,Groupement d'Étude et de Prévention du Suicide, Saint-Benoît, France
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7
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Abstract
More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated. Detecting posts written by medical experts facilitates the quick access to posts that have more chances of being correct and informative. The main objective of this work is to learn classification models that can be used to detect posts written by medical experts in any health forum discussions. Two French health forums have been used to discover the best features and methods for this text categorization task. The obtained results confirm that models learned on appropriate websites may be used efficiently on other websites (more than 98% of F1-measure has been obtained using a Random Forest classifier). A study of misclassified posts highlights the participation of medical experts in forum discussions even if their role is not explicitly indicated.
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Affiliation(s)
| | | | - Sandra Bringay
- Université Montpellier (UM), France; Université Paul-Valéry Montpellier 3 (UM3), France
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8
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Lopez-Castroman J, Moulahi B, Azé J, Bringay S, Deninotti J, Guillaume S, Baca-Garcia E. Mining social networks to improve suicide prevention: A scoping review. J Neurosci Res 2019; 98:616-625. [PMID: 30809836 DOI: 10.1002/jnr.24404] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 12/03/2018] [Accepted: 02/07/2019] [Indexed: 12/18/2022]
Abstract
Attention about the risks of online social networks (SNs) has been called upon reports describing their use to express emotional distress and suicidal ideation or plans. On the Internet, cyberbullying, suicide pacts, Internet addiction, and "extreme" communities seem to increase suicidal behavior (SB). In this study, the scientific literature about SBs and SNs was narratively reviewed. Some authors focus on detecting at-risk populations through data mining, identification of risks factors, and web activity patterns. Others describe prevention practices on the Internet, such as websites, screening, and applications. Targeted interventions through SNs are also contemplated when suicidal ideation is present. Multiple predictive models should be defined, implemented, tested, and combined in order to deal with the risk of SB through an effective decision support system. This endeavor might require a reorganization of care for SNs users presenting suicidal ideation.
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Affiliation(s)
- Jorge Lopez-Castroman
- INSERM U888, La Colombière Hospital, Montpellier, France.,Department of Adult Psychiatry, CHRU Nimes, Nimes, France.,Departments of Psychiatry, Media and Internet, and Telecommunication and Networks, University of Montpellier UM, Montpellier, France
| | - Bilel Moulahi
- Departments of Psychiatry, Media and Internet, and Telecommunication and Networks, University of Montpellier UM, Montpellier, France.,LIRMM UMR 5506, Montpellier, France
| | - Jérôme Azé
- Departments of Psychiatry, Media and Internet, and Telecommunication and Networks, University of Montpellier UM, Montpellier, France.,LIRMM UMR 5506, Montpellier, France
| | - Sandra Bringay
- Departments of Psychiatry, Media and Internet, and Telecommunication and Networks, University of Montpellier UM, Montpellier, France.,LIRMM UMR 5506, Montpellier, France.,Department of Applied Mathematics and Informatics, Paul-Valery University, Montpellier, France
| | | | - Sebastien Guillaume
- INSERM U888, La Colombière Hospital, Montpellier, France.,Departments of Psychiatry, Media and Internet, and Telecommunication and Networks, University of Montpellier UM, Montpellier, France.,Department of Emergency Psychiatry and Post-Acute Care, Montpellier University Hospital, Montpellier, France
| | - Enrique Baca-Garcia
- Department of Psychiatry, Fundacion Jimenez Diaz University Hospital, Madrid, Spain.,Department of Psychiatry, University Hospital Rey Juan Carlos, Mostoles, Spain.,Department of Psychiatry, General Hospital of Villalba, Madrid, Spain.,Department of Psychiatry, University Hospital Infanta Elena, Valdemoro, Spain.,Department of Psychiatry, Madrid Autonomous University, Madrid, Spain.,CIBERSAM (Centro de Investigacion en Salud Mental), Carlos III Institute of Health, Madrid, Spain.,Universidad Catolica del Maule, Talca, Chile
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9
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Pinaire J, Azé J, Bringay S, Poncelet P, Genolini C, Landais P. PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction. Stud Health Technol Inform 2018; 247:391-395. [PMID: 29677989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.
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Affiliation(s)
| | - Jérôme Azé
- LIRMM, UMR 5506, Montpellier University, Montpellier, France
| | - Sandra Bringay
- LIRMM, UMR 5506, Montpellier University, Montpellier, France
| | - Pascal Poncelet
- LIRMM, UMR 5506, Montpellier University, Montpellier, France
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10
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Pinaire J, Azé J, Bringay S, Landais P. Patient healthcare trajectory. An essential monitoring tool: a systematic review. Health Inf Sci Syst 2017; 5:1. [PMID: 28413630 PMCID: PMC5390363 DOI: 10.1007/s13755-017-0020-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 03/29/2017] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves. METHODS We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions. RESULTS Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%). CONCLUSION This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.
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Affiliation(s)
- Jessica Pinaire
- Biostatistics, Epidemiology and Public Health Department, Nîmes University Hospital, Place R Debré, 30 029 Nîmes, France
- UPRES EA 2415, Clinical Research University Institute, 641 av du Doyen Gaston Giraud, 34 093 Montpellier, France
- LIRMM, UMR 5506, Montpellier University, 860 rue de Saint Priest – Bât 5, 34 095 Montpellier Cedex 5, France
| | - Jérôme Azé
- LIRMM, UMR 5506, Montpellier University, 860 rue de Saint Priest – Bât 5, 34 095 Montpellier Cedex 5, France
| | - Sandra Bringay
- LIRMM, UMR 5506, Montpellier University, 860 rue de Saint Priest – Bât 5, 34 095 Montpellier Cedex 5, France
- AMIS, Paul Valéry University, Montpellier, France
| | - Paul Landais
- Biostatistics, Epidemiology and Public Health Department, Nîmes University Hospital, Place R Debré, 30 029 Nîmes, France
- UPRES EA 2415, Clinical Research University Institute, 641 av du Doyen Gaston Giraud, 34 093 Montpellier, France
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11
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Lenoir P, Moulahi B, Azé J, Bringay S, Mercier G, Carbonnel F. Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 #SmearForSmear Campaign. J Med Internet Res 2017; 19:e344. [PMID: 29038096 PMCID: PMC5662788 DOI: 10.2196/jmir.8421] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 09/05/2017] [Accepted: 09/06/2017] [Indexed: 02/06/2023] Open
Abstract
Background Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz. Objective The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign. Methods We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users’ status. Verbatims were thematically and then statistically analyzed. Results A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P<.001), female gender (OR 3.752, 95% CI 2.133-6.598, P<.001), and people who live in the United Kingdom (OR 2.097, 95% CI 1.447-3.038, P<.001). Nonsensitizing tweets were statistically more posted by a nonhealth or nonmedia company (OR 0.558, 95% CI 0.383-0.814, P<.001). Conclusions This study demonstrates that the success of a public health campaign using a social media platform depends on its ability to get its targets involved. It also suggests the need to use social marketing to help its dissemination. The clinical impact of this Twitter campaign to increase cervical cancer screening is yet to be evaluated.
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Affiliation(s)
- Philippe Lenoir
- Department of General Practice, Montpellier University, Montpellier, France
| | - Bilel Moulahi
- Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, UMR 5506, Montpellier University/Centre National de la Recherche Scientifique, Montpellier, France
| | - Jérôme Azé
- Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, UMR 5506, Montpellier University/Centre National de la Recherche Scientifique, Montpellier, France
| | - Sandra Bringay
- Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, UMR 5506, Montpellier University/Centre National de la Recherche Scientifique, Montpellier, France.,Application des Mathématiques, Informatique et Statistique Group, Paul Valéry University, Montpellier, France
| | - Gregoire Mercier
- Public Health Department, Montpellier University Hospital, Montpellier, France.,Centre d'Etudes Politiques de l'Europe Latine UMR 5112, Montpellier University/Centre National de la Recherche Scientifique, Montpellier, France.,Centre d'Evaluation des programmes de Prévention Santé Platform, Paul Valéry University Montpellier 3, Montpellier University, Montpellier, France
| | - François Carbonnel
- Department of General Practice, Montpellier University, Montpellier, France.,Centre d'Evaluation des programmes de Prévention Santé Platform, Paul Valéry University Montpellier 3, Montpellier University, Montpellier, France.,Avicenne Multiprofessional Health Center, Cabestany, France.,Institut du Cancer Montpellier, Montpellier, France.,Epsylon EA4556, Paul Valéry University Montpellier 3, Montpellier University, Montpellier, France
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12
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Azé J, Sola C, Zhang J, Lafosse-Marin F, Yasmin M, Siddiqui R, Kremer K, van Soolingen D, Refrégier G. Genomics and Machine Learning for Taxonomy Consensus: The Mycobacterium tuberculosis Complex Paradigm. PLoS One 2015; 10:e0130912. [PMID: 26154264 PMCID: PMC4496040 DOI: 10.1371/journal.pone.0130912] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 05/25/2015] [Indexed: 11/18/2022] Open
Abstract
Infra-species taxonomy is a prerequisite to compare features such as virulence in different pathogen lineages. Mycobacterium tuberculosis complex taxonomy has rapidly evolved in the last 20 years through intensive clinical isolation, advances in sequencing and in the description of fast-evolving loci (CRISPR and MIRU-VNTR). On-line tools to describe new isolates have been set up based on known diversity either on CRISPRs (also known as spoligotypes) or on MIRU-VNTR profiles. The underlying taxonomies are largely concordant but use different names and offer different depths. The objectives of this study were 1) to explicit the consensus that exists between the alternative taxonomies, and 2) to provide an on-line tool to ease classification of new isolates. Genotyping (24-VNTR, 43-spacers spoligotypes, IS6110-RFLP) was undertaken for 3,454 clinical isolates from the Netherlands (2004-2008). The resulting database was enlarged with African isolates to include most human tuberculosis diversity. Assignations were obtained using TB-Lineage, MIRU-VNTRPlus, SITVITWEB and an algorithm from Borile et al. By identifying the recurrent concordances between the alternative taxonomies, we proposed a consensus including 22 sublineages. Original and consensus assignations of the all isolates from the database were subsequently implemented into an ensemble learning approach based on Machine Learning tool Weka to derive a classification scheme. All assignations were reproduced with very good sensibilities and specificities. When applied to independent datasets, it was able to suggest new sublineages such as pseudo-Beijing. This Lineage Prediction tool, efficient on 15-MIRU, 24-VNTR and spoligotype data is available on the web interface “TBminer.” Another section of this website helps summarizing key molecular epidemiological data, easing tuberculosis surveillance. Altogether, we successfully used Machine Learning on a large dataset to set up and make available the first consensual taxonomy for human Mycobacterium tuberculosis complex. Additional developments using SNPs will help stabilizing it.
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Affiliation(s)
- Jérôme Azé
- LIRMM UM CNRS, UMR 5506, 860 rue de St Priest, 34095 Montpellier cedex 5, France
| | - Christophe Sola
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, rue Gregor Mendel, Bât 400, 91405 Orsay cedex, France
| | - Jian Zhang
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, rue Gregor Mendel, Bât 400, 91405 Orsay cedex, France
| | - Florian Lafosse-Marin
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, rue Gregor Mendel, Bât 400, 91405 Orsay cedex, France
| | - Memona Yasmin
- Pakistan Institute for Engineering and Applied Sciences (PIEAS), Lehtrar Road, Nilore, Islamabad, Pakistan
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box # 577, Jhang Road, Faisalabad, Pakistan
| | - Rubina Siddiqui
- Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), P.O. Box # 577, Jhang Road, Faisalabad, Pakistan
| | - Kristin Kremer
- National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands
| | - Dick van Soolingen
- National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands
- Department of Pulmonary Diseases and Department of Microbiology, Radbout University Nijmegen Medical Centre, University Lung Centre Dekkerswald, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Guislaine Refrégier
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, rue Gregor Mendel, Bât 400, 91405 Orsay cedex, France
- * E-mail:
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13
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Bourquard T, Landomiel F, Reiter E, Crépieux P, Ritchie DW, Azé J, Poupon A. Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex. Sci Rep 2015; 5:10760. [PMID: 26030356 PMCID: PMC4649906 DOI: 10.1038/srep10760] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Accepted: 01/26/2015] [Indexed: 12/22/2022] Open
Abstract
β-arrestins serve as signaling scaffolds downstream of G protein-coupled receptors, and thus play a crucial role in a plethora of cellular processes. Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions.
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Affiliation(s)
- Thomas Bourquard
- 1] BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France; CNRS, UMR7247, F-37380 Nouzilly, France; Université François Rabelais, 37041 Tours, France; IFCE, Nouzilly, F-37380 France [2] INRIA Nancy, 615 Rue du Jardin Botanique, Villers-lès-Nancy, 54600 France
| | - Flavie Landomiel
- BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France; CNRS, UMR7247, F-37380 Nouzilly, France; Université François Rabelais, 37041 Tours, France; IFCE, Nouzilly, F-37380 France
| | - Eric Reiter
- BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France; CNRS, UMR7247, F-37380 Nouzilly, France; Université François Rabelais, 37041 Tours, France; IFCE, Nouzilly, F-37380 France
| | - Pascale Crépieux
- BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France; CNRS, UMR7247, F-37380 Nouzilly, France; Université François Rabelais, 37041 Tours, France; IFCE, Nouzilly, F-37380 France
| | - David W Ritchie
- INRIA Nancy, 615 Rue du Jardin Botanique, Villers-lès-Nancy, 54600 France
| | - Jérôme Azé
- Bioinformatics group - AMIB INRIA - Laboratoire de Recherche en Informatique, Université Paris-Sud, Orsay, 91405 France
| | - Anne Poupon
- BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France; CNRS, UMR7247, F-37380 Nouzilly, France; Université François Rabelais, 37041 Tours, France; IFCE, Nouzilly, F-37380 France
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14
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Abdaoui A, Azé J, Bringay S, Poncelet P. Assisting e-patients in an Ask the Doctor Service. Stud Health Technol Inform 2015; 210:572-576. [PMID: 25991213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Ask the doctor services are personalized forums allowing patients to ask questions directly to doctors. Usually, patients must choose the most appropriate category for their question among lots of categories to be redirected to the most relevant physician. However, manual selection is tedious and error prone activity. In this work we propose to assist the patients in this task by recommending a short list of most appropriate categories.
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Affiliation(s)
- Amine Abdaoui
- LIRMM, 860 St Priest Street, 34095 Montpellier, France
| | - Jérôme Azé
- LIRMM, 860 St Priest Street, 34095 Montpellier, France
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15
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Abdaoui A, Azé J, Bringay S, Poncelet P. E-Patient Reputation in Health Forums. Stud Health Technol Inform 2015; 216:137-141. [PMID: 26262026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Online health forums are increasingly used by patients to get information and help related to their health. However, information reliability in these forums is unfortunately not always guaranteed. Obviously, consequences of self-diagnosis may be severe on the patient's health if measures are taken without consulting a doctor. Many works on trust issues related to social media have been proposed, but most of them mainly focus only on the structure part of the social network (number of posts, number of likes, etc.). In the case of online health forums, a lot of trust and distrust is expressed inside the posted messages and cannot be inferred by only considering the structure. In this study, we rather suggest inferring the user's trustworthiness from the replies he receives in the forum. The proposed method is divided into three main steps: First, the recipient(s) of each post must be identified. Next, the trust or distrust expressed in these posts is evaluated. Finally, the user's reputation is computed by aggregating all the posts he received. Conducted experiments using a manually annotated corpus are encouraging.
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Affiliation(s)
- Amine Abdaoui
- LIRMM UM2 CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France
| | - Jérôme Azé
- LIRMM UM2 CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France
| | - Sandra Bringay
- LIRMM UM2 CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France
| | - Pascal Poncelet
- LIRMM UM2 CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France
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16
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Guilhot-Gaudeffroy A, Froidevaux C, Azé J, Bernauer J. Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities. PLoS One 2014; 9:e108928. [PMID: 25268579 PMCID: PMC4182525 DOI: 10.1371/journal.pone.0108928] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 09/05/2014] [Indexed: 12/03/2022] Open
Abstract
Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.
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Affiliation(s)
- Adrien Guilhot-Gaudeffroy
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
- Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
| | - Christine Froidevaux
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
| | - Jérôme Azé
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
- Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS UMR 5506, Université Montpellier 2, Montpellier, France
| | - Julie Bernauer
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
- * E-mail:
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17
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Fleishman SJ, Whitehead TA, Strauch EM, Corn JE, Qin S, Zhou HX, Mitchell JC, Demerdash ON, Takeda-Shitaka M, Terashi G, Moal IH, Li X, Bates PA, Zacharias M, Park H, Ko JS, Lee H, Seok C, Bourquard T, Bernauer J, Poupon A, Azé J, Soner S, Ovali ŞK, Ozbek P, Ben Tal N, Haliloglu T, Hwang H, Vreven T, Pierce BG, Weng Z, Pérez-Cano L, Pons C, Fernández-Recio J, Jiang F, Yang F, Gong X, Cao L, Xu X, Liu B, Wang P, Li C, Wang C, Robert CH, Guharoy M, Liu S, Huang Y, Li L, Guo D, Chen Y, Xiao Y, London N, Itzhaki Z, Schueler-Furman O, Inbar Y, Patapov V, Cohen M, Schreiber G, Tsuchiya Y, Kanamori E, Standley DM, Nakamura H, Kinoshita K, Driggers CM, Hall RG, Morgan JL, Hsu VL, Zhan J, Yang Y, Zhou Y, Kastritis PL, Bonvin AM, Zhang W, Camacho CJ, Kilambi KP, Sircar A, Gray JJ, Ohue M, Uchikoga N, Matsuzaki Y, Ishida T, Akiyama Y, Khashan R, Bush S, Fouches D, Tropsha A, Esquivel-Rodríguez J, Kihara D, Stranges PB, Jacak R, Kuhlman B, Huang SY, Zou X, Wodak SJ, Janin J, Baker D. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J Mol Biol 2011; 414:289-302. [PMID: 22001016 PMCID: PMC3839241 DOI: 10.1016/j.jmb.2011.09.031] [Citation(s) in RCA: 119] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2011] [Revised: 09/08/2011] [Accepted: 09/16/2011] [Indexed: 11/26/2022]
Abstract
The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.
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Affiliation(s)
- Sarel J Fleishman
- Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Timothy A Whitehead
- Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Eva-Maria Strauch
- Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Jacob E Corn
- Department of Biochemistry, University of Washington, Seattle, Washington 98195
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Julie C. Mitchell
- Departments of Mathematics and Biochemistry, University of Wisconsin USA
| | - Omar N.A Demerdash
- Biophysics and Medical Sciences Training Programs, University of Wisconsin USA
| | | | - Genki Terashi
- School of Pharmacy, Kitasato University, Tokyo, Japan
| | - Iain H. Moal
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, UK
| | - Xiaofan Li
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, UK
| | - Martin Zacharias
- Physics Department, Technical University Munich, 85748 Garching, Germany
| | - Hahnbeom Park
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Jun-su Ko
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Thomas Bourquard
- INRIA AMIB, Bioinformatics group, Laboratoire de Recherche en Informatique, Université Paris-Sud, 91405 Orsay, France
- INRIA AMIB, Bioinformatics group, Laboratoire d'Informatique (LIX), École Polytechnique, 91128 Palaiseau, France
- INRIA Nancy/Laboratoire Lorrain de Recherche en Informatique et ses Applications, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy, France
| | - Julie Bernauer
- INRIA AMIB, Bioinformatics group, Laboratoire d'Informatique (LIX), École Polytechnique, 91128 Palaiseau, France
| | - Anne Poupon
- BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des Comportements, 37380 Nouzilly, France; CNRS, UMR6175, 37380 Nouzilly, France; Université Francois Rabelais, 37041 Tours, France
| | - Jérôme Azé
- INRIA AMIB, Bioinformatics group, Laboratoire d'Informatique (LIX), École Polytechnique, 91128 Palaiseau, France
| | - Seren Soner
- Polymer Research Center and Chemical Engineering Department, Bogazici University, Bebek - Istanbul, Turkey
| | - Şefik Kerem Ovali
- Polymer Research Center and Chemical Engineering Department, Bogazici University, Bebek - Istanbul, Turkey
| | - Pemra Ozbek
- Polymer Research Center and Chemical Engineering Department, Bogazici University, Bebek - Istanbul, Turkey
| | - Nir Ben Tal
- Department of Biochemistry and Molecular Biology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
| | - Türkan Haliloglu
- Polymer Research Center and Chemical Engineering Department, Bogazici University, Bebek - Istanbul, Turkey
| | - Howook Hwang
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Brian G. Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Laura Pérez-Cano
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, 08034 Barcelona, Spain
| | - Carles Pons
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Jordi Girona 29, 08034 Barcelona, Spain
| | - Fan Jiang
- Institute of Physics, Chinese Academy of Sciences, China
| | - Feng Yang
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Xinqi Gong
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Libin Cao
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Xianjin Xu
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Bin Liu
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Panwen Wang
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Chunhua Li
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Cunxin Wang
- College of Life Science and Bioengineering, Beijing University of Technology, 100124, China
| | - Charles H. Robert
- Laboratoire de Biochimie Théorique CNRS-UPR 9080, Institut de Biologie Physico-Chimique (IBPC), Paris, FRANCE
| | - Mainak Guharoy
- Laboratoire de Biochimie Théorique CNRS-UPR 9080, Institut de Biologie Physico-Chimique (IBPC), Paris, FRANCE
| | - Shiyong Liu
- Department of Physics, Huazhong University of Science and Technology, China
| | - Yangyu Huang
- Department of Physics, Huazhong University of Science and Technology, China
| | - Lin Li
- Department of Physics, Huazhong University of Science and Technology, China
| | - Dachuan Guo
- Department of Physics, Huazhong University of Science and Technology, China
| | - Ying Chen
- Department of Physics, Huazhong University of Science and Technology, China
| | - Yi Xiao
- Department of Physics, Huazhong University of Science and Technology, China
| | - Nir London
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University, POB 12272, Jerusalem, 91120 Israel
| | - Zohar Itzhaki
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University, POB 12272, Jerusalem, 91120 Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University, POB 12272, Jerusalem, 91120 Israel
| | - Yuval Inbar
- Department of Biological Chemistry, Weizmann Institute of Science, Israel
| | - Vladimir Patapov
- Department of Biological Chemistry, Weizmann Institute of Science, Israel
| | - Mati Cohen
- Department of Biological Chemistry, Weizmann Institute of Science, Israel
| | - Gideon Schreiber
- Department of Biological Chemistry, Weizmann Institute of Science, Israel
| | - Yuko Tsuchiya
- Institute for Protein Research, Osaka University, Japan
| | | | - Daron M. Standley
- Systems Immunology Lab, WPI Immunology Frontier Research Center (IFReC), Osaka University,3-1 Yamadaoka, Suita, Osaka 565-0871, Japan
| | | | - Kengo Kinoshita
- Graduate School of Information Sciences, Tohoku University, Japan
| | - Camden M. Driggers
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, OR, USA
| | - Robert G. Hall
- Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR, USA
| | - Jessica L. Morgan
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, OR, USA
| | - Victor L. Hsu
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, OR, USA
| | - Jian Zhan
- Indiana University School of Informatics, Indiana University Purdue University at Indianapolus, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
| | - Yuedong Yang
- Indiana University School of Informatics, Indiana University Purdue University at Indianapolus, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
| | - Yaoqi Zhou
- Indiana University School of Informatics, Indiana University Purdue University at Indianapolus, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine
| | - Panagiotis L. Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, The Netherlands
| | - Alexandre M.J.J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, The Netherlands
| | - Weiyi Zhang
- Department of Computational and Systems Biology, University of Pittsburgh, US
| | - Carlos J. Camacho
- Department of Computational and Systems Biology, University of Pittsburgh, US
| | - Krishna P. Kilambi
- Department of Chemical & Biomolecular Engineering and the Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Aroop Sircar
- Department of Chemical & Biomolecular Engineering and the Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey J. Gray
- Department of Chemical & Biomolecular Engineering and the Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Masahito Ohue
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan
| | - Nobuyuki Uchikoga
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan
| | - Yuri Matsuzaki
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan
| | - Takashi Ishida
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan
| | - Yutaka Akiyama
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan
| | - Raed Khashan
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599-7360
| | - Stephen Bush
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599-7360
| | - Denis Fouches
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599-7360
| | - Alexander Tropsha
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599-7360
| | - Juan Esquivel-Rodríguez
- Department of Computer Science, Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907
| | - Daisuke Kihara
- Department of Computer Science, Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907
| | - P Benjamin Stranges
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599-7260
| | - Ron Jacak
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599-7260
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599-7260
| | - Sheng-You Huang
- Department of Physics, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri-Columbia, Columbia, MO 65211
| | - Xiaoqin Zou
- Department of Physics, Department of Biochemistry, Dalton Cardiovascular Research Center, Informatics Institute, University of Missouri-Columbia, Columbia, MO 65211
| | - Shoshana J Wodak
- Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
- Department of Biochemistry, University of Toronto, Toronto Ontario M5S 1A8, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8
| | - Joel Janin
- IBBMC UMR 8619, Bat. 430, Université Paris-Sud 91405-Orsay, France
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington 98195
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
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18
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Azé J, Gentils L, Toffano-Nioche C, Loux V, Gibrat JF, Bessières P, Rouveirol C, Poupon A, Froidevaux C. Towards a semi-automatic functional annotation tool based on decision-tree techniques. BMC Proc 2008; 2 Suppl 4:S3. [PMID: 19091050 PMCID: PMC2654970 DOI: 10.1186/1753-6561-2-s4-s3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Due to the continuous improvements of high throughput technologies and experimental procedures, the number of sequenced genomes is increasing exponentially. Ultimately, the task of annotating these data relies on the expertise of biologists. The necessity for annotation to be supervised by human experts is the rate limiting step of the data analysis. To face the deluge of new genomic data, the need for automating, as much as possible, the annotation process becomes critical. Results We consider annotation of a protein with terms of the functional hierarchy that has been used to annotate Bacillus subtilis and propose a set of rules that predict classes in terms of elements of the functional hierarchy, i.e., a class is a node or a leaf of the hierarchy tree. The rules are obtained through two decision-trees techniques: first-order decision-trees and multilabel attribute-value decision-trees, by using as training data the proteins from two lactic bacteria: Lactobacillus sakei and Lactobacillus bulgaricus. We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures. Results obtained for the two approaches on both genomes are comparable and show a good precision together with a high prediction rate. Using combined approaches increases the recall and the prediction rate. Conclusion The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators. This first study is a crucial step towards designing a semi-automatic functional annotation tool.
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Affiliation(s)
- Jérôme Azé
- LRI - CNRS UMR 8623 - University Paris-Sud 11, F-91405 Orsay Cedex, France
| | - Lucie Gentils
- LRI - CNRS UMR 8623 - University Paris-Sud 11, F-91405 Orsay Cedex, France
| | | | - Valentin Loux
- INRA, Unité Mathématique, Informatique et Génome UR1077, F-78352 Jouy-en-Josas, France
| | - Jean-François Gibrat
- INRA, Unité Mathématique, Informatique et Génome UR1077, F-78352 Jouy-en-Josas, France
| | - Philippe Bessières
- INRA, Unité Mathématique, Informatique et Génome UR1077, F-78352 Jouy-en-Josas, France
| | - Céline Rouveirol
- LIPN - UMR CNRS 7030 - Institut Galilée - University Paris-Nord, F-93430 Villetaneuse, France
| | - Anne Poupon
- IBBMC - CNRS UMR 8619 - University Paris-Sud 11, F-91405 Orsay Cedex, France
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19
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Abstract
MOTIVATION Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them. RESULTS To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions. AVAILABILITY http://genomics.eu.org/spip/-Bioinformatics-tools-
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Affiliation(s)
- J Bernauer
- Yeast Structural Genomics, IBBMC UMR CNRS 8619, Bâtiment 430, Université Paris-Sud, 91405 Orsay, France
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20
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Abstract
We describe protein-protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein-protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Voronoï cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.
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Affiliation(s)
- Julie Bernauer
- Yeast Structural Genomics Laboratory, IBBMC UMR CNRS 8619, Bâtiment 430, Université Paris-Sud, 91405-Orsay, France
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