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Vischer A, Rosania J, Socrates T, Blaschke C, Proenca M, Bonnier G, Proust YM, Ferrario D, Eckstein J, Lemay M, Burkard T. Pilot study of comparability of a smartphone blood pressure monitoring algorithm to conventional cuff-based blood pressure measurements. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.2350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background/Introduction
There is a growing market for smartphone applications (apps), offering medical assessments such as blood pressure measurements (BPM). These apps have the potential to improve blood pressure (BP) control by making BPM broadly and easily accessible. Yet, to be suitable for clinical and diagnostic purposes, BPM measured with smartphone apps need to be comparable to conventional BPM.
Purpose
We sought to compare a novel photoplethysmographic BPM algorithm used in a smartphone app to conventional cuff-based BPM.
Methods
We included consecutive patients with an indication for ambulatory BPM. Office blood pressure measurements (OBPM) were taken with an oscillometric cuff-based device (Welch Allyn SureBP). The algorithm of the smartphone app detects the pulse wave in finger capillaries using the phone's camera and estimates BP based on the form of the pulse wave. Before estimating a BP value, the algorithm performs a quality assessment to automatically reject recordings with insufficient quality. On the first day (D1), we took 6 OBPM alternating with 5 smartphone BPM (TestBP). On the second day (D2), 4 OBPM alternating with 3 TestBP were measured. TestBP calibrated based on the first OBPM of D1. Each TestBP was then compared to its RefBP (defined as mean of the previous and following OBPM).
Results
50 patients were included in the study, resulting in 50 TestBP values on D1 and 33 on D2. There was no difference at the 5% significance level between the TestBP and RefBP distributions on both days, and for both systolic and diastolic pressures. The mean ± standard deviation (SD) of the differences between TestBP and RefBP was 0.7±9.4 / 1.0±4.5 mmHg on D1 and 2.6±8.2 / 1.3±4.1 mmHg on D2 for systolic/diastolic values, respectively. The number of TestBP measurements within 5, 10 and 15 mmHg from RefBP are shown in Table 1. Bland-Altman plots depicting the agreement between TestBP and RefBP are shown in Figure 1.
Conclusion
This smartphone algorithm shows comparable values to oscillometric cuff-based especially diastolic values. Its differences between TestBP – RefBP have a good stability 1 day after calibration. Before clinical use, this algorithm needs to undergo formal validation against a reference BP method accepted by international standards (auscultatory or invasive methods).
Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): Centre Suisse d'Electronique et de Microtechnique Table 1Figure 1
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Affiliation(s)
- A Vischer
- University Hospital Basel, ESH Hypertension Centre of Excellence, Medical Outpatient Department, Basel, Switzerland
| | - J Rosania
- University Hospital Basel, ESH Hypertension Centre of Excellence, Medical Outpatient Department, Basel, Switzerland
| | - T Socrates
- University Hospital Basel, ESH Hypertension Centre of Excellence, Medical Outpatient Department, Basel, Switzerland
| | - C Blaschke
- University Hospital Basel, ESH Hypertension Centre of Excellence, Medical Outpatient Department, Basel, Switzerland
| | - M Proenca
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - G Bonnier
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - Y.-M Proust
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - D Ferrario
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - J Eckstein
- University Hospital Basel, Department of Internal Medicine, Basel, Switzerland
| | - M Lemay
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - T Burkard
- University Hospital Basel, ESH Hypertension Centre of Excellence, Medical Outpatient Department, Basel, Switzerland
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Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes. Diabetes Care 2019; 42:192-199. [PMID: 30061316 PMCID: PMC6341285 DOI: 10.2337/dc18-0288] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 06/28/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings. RESEARCH DESIGN AND METHODS Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A], and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years. RESULTS At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P < 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody-positive, 13% for single autoantibody-positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody-positive, 12% for single autoantibody-positive, and 0.5% for initially autoantibody-negative subjects. CONCLUSIONS Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody-positive identical twins and multiple autoantibody-positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.
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Affiliation(s)
- Taylor M. Triolo
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Alexandra Fouts
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Laura Pyle
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Liping Yu
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Peter A. Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Andrea K. Steck
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO
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Espinoza O, Frank E, Liu J, Perry J, Pyle R, Rigby A, Riley K, Soto A, Gitelman S, Adi S, Anderson M, Berhel A, Breen K, Fraser K, Gerard-Gonzalez A, Jossan P, Lustig R, Moassesfar S, Mugg A, Ng D, Prahalod P, Rangel-Lugo M, Sanda S, Tarkoff J, Torok C, Wesch R, Aslan I, Buchanan J, Cordier J, Hamilton C, Hawkins L, Ho T, Jain A, Ko K, Lee T, Phelps S, Rosenthal S, Sahakitrungruang T, Stehl L, Taylor L, Wertz M, Wong J, Philipson L, Briars R, Devine N, Littlejohn E, Grant T, Gottlieb P, Klingensmith G, Steck A, Alkanani A, Bautista K, Bedoy R, Blau A, Burke B, Cory L, Dang M, Fitzgerald-Miller L, Fouts A, Gage V, Garg S, Gesauldo P, Gutin R, Hayes C, Hoffman M, Ketchum K, Logsden-Sackett N, Maahs D, Messer L, Meyers L, Michels A, Peacock S, Rewers M, Rodriguez P, Sepulbeda F, Sippl R, Steck A, Taki I, Tran BK, Tran T, Wadwa RP, Zeitler P, Barker J, Barry S, Birks L, Bomsburger L, Bookert T, Briggs L, Burdick P, Cabrera R, Chase P, Cobry E, Conley A, Cook G, Daniels J, DiDomenico D, Eckert J, Ehler A, Eisenbarth G, Fain P, Fiallo-Scharer R, Frank N, Goettle H, Haarhues M, Harris S, Horton L, Hutton J, Jeffrrey J, Jenison R, Jones K, Kastelic W, King MA, Lehr D, Lungaro J, Mason K, Maurer H, Nguyen L, Proto A, Realsen J, Schmitt K, Schwartz M, Skovgaard S, Smith J, Vanderwel B, Voelmle M, Wagner R, Wallace A, Walravens P, Weiner L, Westerhoff B, Westfall E, Widmer K, Wright H, Schatz D, Abraham A, Atkinson M, Cintron M, Clare-Salzler M, Ferguson J, Haller M, Hosford J, Mancini D, Rohrs H, Silverstein J, Thomas J, Winter W, Cole G, Cook R, Coy R, Hicks E, Lewis N, Marks J, Pugliese A, Blaschke C, Matheson D, Sanders-Branca N, Sosenko J, Arazo L, Arce R, Cisneros M, Sabbag S, Moran A, Gibson C, Fife B, Hering B, Kwong C, Leschyshyn J, Nathan B, Pappenfus B, Street A, Boes MA, Eck SP, Finney L, Fischer TA, Martin A, Muzamhindo CJ, Rhodes M, Smith J, Wagner J, Wood B, Becker D, Delallo K, Diaz A, Elnyczky B, Libman I, Pasek B, Riley K, Trucco M, Copemen B, Gwynn D, Toledo F, Rodriguez H, Bollepalli S, Diamond F, Eyth E, Henson D, Lenz A, Shulman D, Raskin P, Adhikari S, Dickson B, Dunnigan E, Lingvay I, Pruneda L, Ramos-Roman M, Raskin P, Rhee C, Richard J, Siegelman M, Sturges D, Sumpter K, White P, Alford M, Arthur J, Aviles-Santa ML, Cordova E, Davis R, Fernandez S, Fordan S, Hardin T, Jacobs A, Kaloyanova P, Lukacova-Zib I, Mirfakhraee S, Mohan A, Noto H, Smith O, Torres N, Wherrett D, Balmer D, Eisel L, Kovalakovska R, Mehan M, Sultan F, Ahenkorah B, Cevallos J, Razack N, Ricci MJ, Rhode A, Srikandarajah M, Steger R, Russell WE, Black M, Brendle F, Brown A, Moore D, Pittel E, Robertson A, Shannon A, Thomas JW, Herold K, Feldman L, Sherwin R, Tamborlane W, Weinzimer S, Toppari J, Kallio T, Kärkkäinen M, Mäntymäki E, Niininen T, Nurmi B, Rajala P, Romo M, Suomenrinne S, Näntö-Salonen K, Simell O, Simell T, Bosi E, Battaglia M, Bianconi E, Bonfanti R, Grogan P, Laurenzi A, Martinenghi S, Meschi F, Pastore M, Falqui L, Muscato MT, Viscardi M, Castleden H, Farthing N, Loud S, Matthews C, McGhee J, Morgan A, Pollitt J, Elliot-Jones R, Wheaton C, Knip M, Siljander H, Suomalainen H, Colman P, Healy F, Mesfin S, Redl L, Wentworth J, Willis J, Farley M, Harrison L, Perry C, Williams F, Mayo A, Paxton J, Thompson V, Volin L, Fenton C, Carr L, Lemon E, Swank M, Luidens M, Salgam M, Sharma V, Schade D, King C, Carano R, Heiden J, Means N, Holman L, Thomas I, Madrigal D, Muth T, Martin C, Plunkett C, Ramm C, Auchus R, Lane W, Avots E, Buford M, Hale C, Hoyle J, Lane B, Muir A, Shuler S, Raviele N, Ivie E, Jenkins M, Lindsley K, Hansen I, Fadoju D, Felner E, Bode B, Hosey R, Sax J, Jefferies C, Mannering S, Prentis R, She J, Stachura M, Hopkins D, Williams J, Steed L, Asatapova E, Nunez S, Knight S, Dixon P, Ching J, Donner T, Longnecker S, Abel K, Arcara K, Blackman S, Clark L, Cooke D, Plotnick L, Levin P, Bromberger L, Klein K, Sadurska K, Allen C, Michaud D, Snodgrass H, Burghen G, Chatha S, Clark C, Silverberg J, Wittmer C, Gardner J, LeBoeuf C, Bell P, McGlore O, Tennet H, Alba N, Carroll M, Baert L, Beaton H, Cordell E, Haynes A, Reed C, Lichter K, McCarthy P, McCarthy S, Monchamp T, Roach J, Manies S, Gunville F, Marosok L, Nelson T, Ackerman K, Rudolph J, Stewart M, McCormick K, May S, Falls T, Barrett T, Dale K, Makusha L, McTernana C, Penny-Thomas K, Sullivan K, Narendran P, Robbie J, Smith D, Christensen R, Koehler B, Royal C, Arthur T, Houser H, Renaldi J, Watsen S, Wu P, Lyons L, House B, Yu J, Holt H, Nation M, Vickers C, Watling R, Heptulla R, Trast J, Agarwal C, Newell D, Katikaneni R, Gardner C, Del Rio A, Logan A, Collier H, Rishton C, Whalley G, Ali A, Ramtoola S, Quattrin T, Mastrandea L, House A, Ecker M, Huang C, Gougeon C, Ho J, Pacuad D, Dunger D, May J, O’Brien C, Acerini C, Salgin B, Thankamony A, Williams R, Buse J, Fuller G, Duclos M, Tricome J, Brown H, Pittard D, Bowlby D, Blue A, Headley T, Bendre S, Lewis K, Sutphin K, Soloranzo C, Puskaric J, Madison H, Rincon M, Carlucci M, Shridharani R, Rusk B, Tessman E, Huffman D, Abrams H, Biederman B, Jones M, Leathers V, Brickman W, Petrie P, Zimmerman D, Howard J, Miller L, Alemzadeh R, Mihailescu D, Melgozza-Walker R, Abdulla N, Boucher-Berry C, Ize-Ludlow D, Levy R, Swenson Brousell C, Scott R, Heenan H, Lunt H, Kendall D, Willis J, Darlow B, Crimmins N, Edler D, Weis T, Schultz C, Rogers D, Latham D, Mawhorter C, Switzer C, Spencer W, Konstantnopoulus P, Broder S, Klein J, Bachrach B, Gardner M, Eichelberger D, Knight L, Szadek L, Welnick G, Thompson B, Hoffman R, Revell A, Cherko J, Carter K, Gilson E, Haines J, Arthur G, Bowen B, Zipf W, Graves P, Lozano R, Seiple D, Spicer K, Chang A, Fregosi J, Harbinson J, Paulson C, Stalters S, Wright P, Zlock D, Freeth A, Victory J, Maheshwari H, Maheshwari A, Holmstrom T, Bueno J, Arguello R, Ahern J, Noreika L, Watson V, Hourse S, Breyer P, Kissel C, Nicholson Y, Pfeifer M, Almazan S, Bajaj J, Quinn M, Funk K, McCance J, Moreno E, Veintimilla R, Wells A, Cook J, Trunnel S, Transue D, Surhigh J, Bezzaire D, Moltz K, Zacharski E, Henske J, Desai S, Frizelis K, Khan F, Sjoberg R, Allen K, Manning P, Hendry G, Taylor B, Jones S, Couch R, Danchak R, Lieberman D, Strader W, Bencomo M, Bailey T, Bedolla L, Roldan C, Moudiotis C, Vaidya B, Anning C, Bunce S, Estcourt S, Folland E, Gordon E, Harrill C, Ireland J, Piper J, Scaife L, Sutton K, Wilkins S, Costelloe M, Palmer J, Casas L, Miller C, Burgard M, Erickson C, Hallanger-Johnson J, Clark P, Taylor W, Galgani J, Banerjee S, Banda C, McEowen D, Kinman R, Lafferty A, Gillett S, Nolan C, Pathak M, Sondrol L, Hjelle T, Hafner S, Kotrba J, Hendrickson R, Cemeroglu A, Symington T, Daniel M, Appiagyei-Dankah Y, Postellon D, Racine M, Kleis L, Barnes K, Godwin S, McCullough H, Shaheen K, Buck G, Noel L, Warren M, Weber S, Parker S, Gillespie I, Nelson B, Frost C, Amrhein J, Moreland E, Hayes A, Peggram J, Aisenberg J, Riordan M, Zasa J, Cummings E, Scott K, Pinto T, Mokashi A, McAssey K, Helden E, Hammond P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk. Diabetes Care 2018; 41:1887-1894. [PMID: 30002199 PMCID: PMC6105323 DOI: 10.2337/dc18-0087] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 06/06/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
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Affiliation(s)
- Maria J. Redondo
- Texas Children’s Hospital, Baylor College of Medicine, Houston, TX
| | | | - Andrea K. Steck
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Seth Sharp
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | - John M. Wentworth
- Walter and Eliza Hall Institute of Medical Research and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | | | | | | | | | - Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
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Blaschke C, Valencia A. Can bibliographic pointers for known biological data be found automatically? Protein interactions as a case study. Comp Funct Genomics 2010; 2:196-206. [PMID: 18628915 PMCID: PMC2447212 DOI: 10.1002/cfg.91] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2001] [Accepted: 06/25/2001] [Indexed: 11/11/2022] Open
Abstract
The Dictionary of Interacting Proteins (DIP) (Xenarios et al., 2000) is a large repository
of protein interactions: its March 2000 release included 2379 protein pairs whose
interactions have been detected by experimental methods. Even if many of these
correspond to poorly characterized proteins, the result of massive yeast two-hybrid
screenings, as many as 851 correspond to interactions detected using direct biochemical
methods. We used information retrieval technology to search automatically for sentences in
Medline abstracts that support these 851 DIP interactions. Surprisingly, we found
correspondence between DIP protein pairs and Medline sentences describing their
interactions in only 30% of the cases. This low coverage has interesting consequences
regarding the quality of annotations (references) introduced in the database and the
limitations of the application of information extraction (IE) technology to Molecular
Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the
lack of standard protein names are difficulties of considerably more importance than the
limitations of the IE methodology employed. A positive finding is the capacity of the IE
system to identify new relations between proteins, even in a set of proteins previously
characterized by human experts. These identifications are made with a considerable degree
of precision. This is, to our knowledge, the first large scale assessment of IE capacity to detect
previously known interactions: we thus propose the use of the DIP data set as a biological
reference to benchmark IE systems.
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Affiliation(s)
- C Blaschke
- Protein Design Group, National Centre for Biotechnology, CNB-CSIC, Cantoblanco, Madrid E-28049, Spain
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6
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Blaschke C, Hoffmann R, Oliveros JC, Valencia A. Extracting information automatically from biological literature. Comp Funct Genomics 2010; 2:310-3. [PMID: 18629239 PMCID: PMC2448400 DOI: 10.1002/cfg.102] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2001] [Accepted: 07/27/2001] [Indexed: 11/13/2022] Open
Affiliation(s)
- C Blaschke
- Protein Design Group, National Center for Biotechnology, CNB-CSIC, Cantoblanco, Madrid E-28049, Spain
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7
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Blaschke C, Valencia A. The potential use of SUISEKI as a protein interaction discovery tool. Genome Inform 2002; 12:123-34. [PMID: 11791231] [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] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Relevant information about protein interactions is stored in textual sources. This sources are commonly used not only as archives of what is already known but also as information for generating new knowledge, particularly to pose hypothesis about new possible interactions that can be inferred from the existing ones. This task is the more creative part of scientific work in experimental systems. We present a large-scale analysis for the prediction of new interactions based on the interaction network for the ones already known and detected automatically in the literature. During the last few years it has became clear that part of the information about protein interactions could be extracted with automatic tools, even if these tools are still far from perfect and key problems such as detection of protein names are not completely solved. We have developed a integrated automatic approach, called SUISEKI (System for Information Extraction on Interactions), able to extract protein interactions from collections of Medline abstracts. Previous experiments with the system have shown that it is able to extract almost 70% of the interactions present in relatively large text corpus, with an accuracy of approximately 80% (for the best defined interactions) that makes the system usable in real scenarios, both at the level of extraction of protein names and at the level of extracting interaction between them. With the analysis of the interaction map of Saccharomyces cerevisiae we show that interactions published in the years 2000/2001 frequently correspond to proteins or genes that were already very close in the interaction network deduced from the literature published before these years and that they are often connected to the same proteins. That is, discoveries are commonly done among highly connected entities. Some biologically relevant examples illustrate how interactions described in the year 2000 could have been proposed as reasonable working hypothesis with the information previously available in the automatically extracted network of interactions.
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Affiliation(s)
- C Blaschke
- Protein Design Group, CNB/CSIC, Campus Universidad Autonoma, 28049 Madrid, Spain.
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8
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Oliveros JC, Blaschke C, Herrero J, Dopazo J, Valencia A. Expression profiles and biological function. Genome Inform Ser Workshop Genome Inform 2002; 11:106-17. [PMID: 11700592] [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] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Expression arrays facilitate the monitoring of changes in expression patterns of large collections of genes. It is generally expected that genes with similar expression patterns would correspond to proteins of common biological function. We assess this common assumption by comparing levels of similarity of expression patterns and statistical significance of biological terms that describe the corresponding protein functions. Terms are automatically obtained by mining large collections of Medline abstracts. We propose that the combined use of the tools for expression profiles clustering and automatic function retrieval, can be useful tools for the detection of biologically relevant associations between genes in complex gene expression experiments. The results obtained using publicly available experimental data show how, in general, an increase in the similarity of the expression patterns is accompanied by an enhancement of the amount of specific functional information or, in other words, how the selected terms became more specific following an increase in the specificity of the expression patterns. Particularly interesting are the discrepancies from this general trend, i.e. groups of genes with similar expression patterns but very little in common at the functional level. In these cases the similarity of their expression profiles becomes the first link between previously unrelated genes.
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Affiliation(s)
- J C Oliveros
- Protein Design Group, Centro Nacional de Biotecnolog í a (CNB-CSIC), Campus de Cantoblanco, 28049 Madrid, Spain.
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9
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Blaschke C, Oliveros JC, Valencia A. Mining functional information associated with expression arrays. Funct Integr Genomics 2001; 1:256-68. [PMID: 11793245 DOI: 10.1007/s101420000036] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2000] [Accepted: 11/24/2000] [Indexed: 10/27/2022]
Abstract
Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments.
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Affiliation(s)
- C Blaschke
- Protein Design Group, National Center for Biotechnology, CNB-CSIC, Cantoblanco, Madrid 28049, Spain
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10
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Blaschke C, Andrade MA, Ouzounis C, Valencia A. Automatic extraction of biological information from scientific text: protein-protein interactions. Proc Int Conf Intell Syst Mol Biol 2000:60-7. [PMID: 10786287] [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] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We describe the basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts. By restricting the problem domain and imposing a number of strong assumptions which include pre-specified protein names and a limited set of verbs that represent actions, we show that it is possible to perform accurate information extraction. The performance of the system is evaluated with different cases of real-world interaction networks, including the Drosophila cell cycle control. The results obtained computationally are in good agreement with current biological knowledge and demonstrate the feasibility of developing a fully automated system able to describe networks of protein interactions with sufficient accuracy.
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Affiliation(s)
- C Blaschke
- Protein Design Group, CNB-CSIC, Madrid, Spain
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