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Meta- analysis on blood transcriptomic studies identifies consistently coexpressed protein- protein interaction modules as robust markers of human aging

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AGING CELL 2014年第2期13卷 216-225页

作者： van den Akker, Erik B. Passtoors, Willemijn M. Jansen, Rick van Zwet, Erik W. Goeman, Jelle J. Hulsman, Marc Emilsson, Valur Perola, Markus Willemsen, Gonneke Penninx, Brenda W. J. H. Heijmans, Bas T. Maier, Andrea B. Boomsma, Dorret I. Kok, Joost N. Slagboom, Pieternella E. Reinders, Marcel J. T. Beekman, Marian Leiden Univ Med Ctr Dept Mol Epidemiol NL-2300 RC Leiden Netherlands Delft Univ Technol Delft Bioinformat Lab NL-2600 GA Delft Netherlands Vrije Univ Amsterdam Med Ctr Dept Psychiat NL-1081 HL Amsterdam Netherlands EMGO Inst Hlth & Care Res NL-1081 BT Amsterdam Netherlands Leiden Univ Med Ctr Dept Med Stat NL-2300 RC Leiden Netherlands Iceland Heart Assoc IS-201 Kopavogur Iceland Natl Inst Hlth & Welf Helsinki 00271 Finland Vrije Univ Amsterdam Dept Biol Psychol NL-1081 BT Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Internal Med Sect Gerontol & Geriatr NL-1007 MB Amsterdam Netherlands Leiden Univ Leiden Inst Adv Comp Sci Dept Algorithms NL-2333 CA Leiden Netherlands Leiden Univ Med Ctr Netherlands Consortium Hlth Ageing NL-2300 RC Leiden Netherlands

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein-protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P=3.5x10(-38)) enriched for T-cell activation' marking age-associated shifts in lymphocyte blood cell counts (R-2=0.603;P=1.9x10(-10)). Adjusting the analysis in the compendium for the T-cell activation' module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for Translational elongation', Cytolysis' and DNA metabolic process'. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.

关键词： aging blood transcriptomics meta-analysis network-based analysis protein-protein interactions

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Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images

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PATTERN RECOGNITION 2019年 86卷 188-200页

作者： Hou, Le Vu Nguyen Kanevsky, Ariel B. Samaras, Dimitris Kurc, Tahsin M. Zhao, Tianhao Gupta, Rajarsi R. Gao, Yi Chen, Wenjin Foran, David Saltz, Joel H. SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA Univ Montreal Montreal Inst Learning Algorithms Montreal PQ Canada SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Oak Ridge TN USA SUNY Stony Brook Dept Pathol Med Ctr Stony Brook NY 11794 USA Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China Rutgers State Univ Ctr Biomed Imaging & Informat New Brunswick NJ USA Rutgers State Univ Rutgers Canc Inst New Jersey New Brunswick NJ USA Rutgers Robert Wood Johnson Med Sch Div Med Informat Piscataway Township NJ USA Stony Brook Univ Hosp Canc Ctr Stony Brook NY USA

We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully-supervised annotation cost. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Pathology image analysis Convolutional neural network Unsupervised learning Semi-supervised learning

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A Novel Sequence-Based Method of Predicting Protein DNA-Binding Residues, Using a Machine Learning Approach

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MOLECULES AND CELLS 2010年第2期30卷 99-105页

作者： Cai, Yudong He, ZhiSong Shi, Xiaohe Kong, Xiangying Gu, Lei Xie, Lu Shanghai Univ Inst Syst Biol Shanghai 200244 Peoples R China Fudan Univ Ctr Computat Syst Biol Shanghai 200433 Peoples R China Zhejiang Univ Dept Bioinformat Coll Life Sci Hangzhou 310058 Zhejiang Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Hlth Sci Beijing 100864 Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp State Key Lab Med Genom Shanghai 200025 Peoples R China Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat Aachen Germany Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China

Protein-DNA interactions play an essential role in transcriptional regulation, DNA repair, and many vital biological processes. The mechanism of protein-DNA binding, however, remains unclear. For the study of many diseases, researchers must improve their understanding of the amino acid motifs that recognize DNA. Because identifying these motifs experimentally is expensive and time-consuming, it is necessary to devise an approach for computational prediction. Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues. To make these predictions, we used the properties of the micro-environment of each amino acid from the AAIndex as well as conservation scores. Testing by the cross-validation method, we obtained an overall accuracy of 94.89%. Our method shows that the amino acid micro-environment is important for DNA binding, and that it is possible to identify the protein-DNA binding sites with it.

关键词： bioinformatics data mining machine learning mRMR protein-DNA interaction

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A Boolean Logical model for Reprogramming of Testes -derived male Germline Stem Cells into Germline pluripotent stem cells

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compUTER METHODS AND PROGRAMS IN BIOmedICINE 2020年 192卷 105473-105473页

作者： Guttula, Praveen Kumar Monteiro, Pedro T. Gupta, Mukesh Kumar Natl Inst Technol Dept Biotechnol & Med Engn Gene Manipulat Lab Rourkela 769008 India Univ Lisbon Inst Super Tecn Dept Comp Sci & Engn Av Rovisco Pais P-1049001 Lisbon Portugal INESC ID SW Algorithms & Tools Constraint Solving Grp R Alves Redol 9 P-1000029 Lisbon Portugal

Background and Objective Male germline stem (GS) cells are responsible for the maintenance of spermatogenesis throughout the adult life of males. Upon appropriate in vitro culture conditions, these GS cells can undergo reprogramming to become germline pluripotent stem (GPS) cells with the loss of spermatogenic potential. In recent years, voluminous data of gene transcripts in GS and GPS cells have become available. However, the mechanism of reprogramming of GS cells into GPS cells remains elusive. This study was designed to develop a Boolean logical model of gene regulatory network (GRN) that might be involved in the reprogramming of GS cells into GPS *** The gene expression profile of GS and GPS cells (GSE ID: GSE11274 and GSE74151) were analyzed using R Bioconductor to identify differentially expressed genes (DEGs) and were functionally annotated with DAVID server. Potential pluripotent genes among the DEGs were then predicted using a combination of machine learning [Support Vector Machine (SVM)] and BLAST search. Protein isoforms were identified by pattern matching with UniProt database with in-house scripts written in C++. Both linear and non-linear interaction maps were generated using the STRING server. CellNet is used to study the relationship of GRNs between the GS and GPS cells. Finally, the GRNs involving all the genes from integrated methods and literature was constructed and qualitative modelling for reprogramming of GS to GPS cells were done by considering the discrete, asynchronous, multivalued logical formalism using the GINsim modeling and simulation *** Through the use of machine learning and logical modeling, the present study identified 3585 DEGs and 221 novel pluripotent genes including Tet1, Cdh1, Tfap2c, Etv4, Etv5, Prdm14, and Prdm10 in GPS cells. Pathway analysis revealed that important signaling pathways such as core pluripotency network, PI3K-Akt, WNT, GDNF and BMP4 signalling pathways were important for the reprogramming

关键词： Germline stem cells reprogramming logical modeling Gene regulatory network

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PDB_REDO: automated re-refinement of X-ray structure models in the PDB

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2009年第3期42卷 376-384页

作者： Joosten, Robbie P. Salzemann, Jean Bloch, Vincent Stockinger, Heinz Berglund, Ann-Charlott Blanchet, Christophe Bongcam-Rudloff, Erik Combet, Christophe Da Costa, Ana L. Deleage, Gilbert Diarena, Matteo Fabbretti, Roberto Fettahi, Geraldine Flegel, Volker Gisel, Andreas Kasam, Vinod Kervinen, Timo Korpelainen, Eija Mattila, Kimmo Pagni, Marco Reichstadt, Matthieu Breton, Vincent Tickle, Ian J. Vriend, Gert Radboud Univ Nijmegen Ctr Mol & Biomol Informat NCMLS Med Ctr NL-6525 ED Nijmegen Netherlands Univ Blaise Pascal CNRS IN2P3 Phys Corpusculaire Lab Clermont Ferrand France Swiss Inst Bioinformat Vital IT Grp Lausanne Switzerland Uppsala Univ SLV Uppsala Sweden Univ Lyon 1 IBCP CNRS IFR Biosci Lyon Gerland 128 F-69365 Lyon France CNR Inst Biomed Technol Bari I-70126 Bari Italy Fraunhofer Inst Algorithms & Sci Comp St Augustin Germany Finnish IT Ctr Sci CSC Espoo Finland Astex Therapeut Ltd Cambridge England

Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://***/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

关键词： X-ray crystallography refinement structure validation Protein Data Bank grid computing

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Towards a Low-Cost Mobile Subcutaneous Vein Detection Solution Using Near-Infrared Spectroscopy

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SCIENTIFIC WORLD JOURNAL 2014年第1期2014卷 365902-365902页

作者： Juric, Simon Flis, Vojko Debevc, Matjaz Holzinger, Andreas Zalik, Borut Adv ICT Res Grp AIRG Maribor 2000 Slovenia Univ Maribor Fac Elect Engn & Comp Sci Lab Geometr Modelling & Multimedia Algorithms Maribor 2000 Slovenia Univ Med Ctr Maribor Dept Vasc Surg Maribor 2000 Slovenia Univ Maribor Fac Elect Engn & Comp Sci Inst Media Commun Maribor 2000 Slovenia Med Univ Graz Inst Med Informat Stat & Documentat A-8036 Graz Austria Graz Univ Technol Inst Informat Syst & Comp Media A-8010 Graz Austria

Excessive venipunctures are both time- and resource-consuming events, which cause anxiety, pain, and distress in patients, or can lead to severe harmful injuries. We propose a low-cost mobile health solution for subcutaneous vein detection using near-infrared spectroscopy, along with an assessment of the current state of the art in this field. The first objective of this study was to get a deeper overview of the research topic, through the initial team discussions and a detailed literature review (using both academic and grey literature). The second objective, that is, identifying the commercial systems employing near-infrared spectroscopy, was conducted using the Pubmed database. The goal of the third objective was to identify and evaluate (using the IEEE Xplore database) the research efforts in the field of low-cost near-infrared imaging in general, as a basis for the conceptual model of the upcoming prototype. Although the reviewed commercial devices have demonstrated usefulness and value for peripheral veins visualization, other evaluated clinical outcomes are less conclusive. Previous studies regarding low-cost near-infrared systems demonstrated the general feasibility of developing cost-effective vein detection systems;however, their limitations are restricting their applicability to clinical practice. Finally, based on the current findings, we outline the future research direction.

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Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

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DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019年第2019期2019卷 baz068-baz068页

作者： Hoyt, Charles Tapley Domingo-Fernandez, Daniel Aldisi, Rana Xu, Lingling Kolpeja, Kristian Spalek, Sandra Wollert, Esther Bachman, John Gyori, Benjamin M. Greene, Patrick Hofmann-Apitius, Martin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Germany Rhein Freidrich Wilhelms Univ Bonn Bonn Aachen Int Ctr Informat Technol D-53115 Bonn Germany Harvard Med Sch Lab Syst Pharmacol 200 Longwood Ave Boston MA 02115 USA

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from medLINE abstracts and Pubmed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://***/bel-enrichment/bel-enrichment. Database URL: https://github. com/bel-enrichment/results

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Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery

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SCIENTIFIC DATA 2019年第1期6卷 1页

作者： Gsaxner, Christina Wallner, Juergen Chen, Xiaojun Zemann, Wolfgang Egger, Jan Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 6-1 A-8036 Graz Austria Comp Algorithms Med Lab Graz Austria Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16c-2 A-8010 Graz Austria Shanghai Jiao Tong Univ Sch Mech Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China

medical augmented reality (AR) is an increasingly important topic in many medical fields. AR enables x-ray vision to see through real world objects. In medicine, this offers pre-, intra- or post-interventional visualization of "hidden" structures. In contrast to a classical monitor view, AR applications provide visualization not only on but also in relation to the patient. However, research and development of medical AR applications is challenging, because of unique patient-specific anatomies and pathologies. Working with several patients during the development for weeks or even months is not feasible. One alternative are commercial patient phantoms, which are very expensive. Hence, this data set provides a unique collection of head and neck cancer patient PET-CT scans with corresponding 3D models, provided as stereolitography (STL) files. The 3D models are optimized for effective 3D printing at low cost. This data can be used in the development and evaluation of AR applications for head and neck surgery.

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Automatic skull defect restoration and cranial implant generation for cranioplasty

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medICAL IMAGE ANALYSIS 2021年 73卷 102171-102171页

作者： Li, Jianning von Campe, Gord Pepe, Antonio Gsaxner, Christina Wang, Enpeng Chen, Xiaojun Zefferer, Ulrike Toedtling, Martin Krall, Marcell Deutschmann, Hannes Schaefer, Ute Schmalstieg, Dieter Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16 A-8010 Graz Austria Med Univ Graz Dept Neurosurg Auenbruggerpl 29 Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg A-8036 Graz Austria Comp Algorithms Med Lab Graz Austria Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Med Univ Graz Dept Radiol Auenbruggerpl 9 A-8036 Graz Austria BioTechMed Graz Mozartgasse 12-2 A-8010 Graz Austria Med Univ Graz Dept Neurosurg Res Unit Expt Neurotraumatol Auenbruggerpl 22 A-8036 Graz Austria

A fast and fully automatic design of 3D printed patient-specific cranial implants is highly desired in cranioplasty the process to restore a defect on the skull. We formulate skull defect restoration as a 3D volumetric shape completion task, where a partial skull volume is completed automatically. The difference between the completed skull and the partial skull is the restored defect;in other words, the implant that can be used in cranioplasty. To fulfill the task of volumetric shape completion, a fully data-driven approach is proposed. Supervised skull shape learning is performed on a database containing 167 high resolution healthy skulls. In these skulls, synthetic defects are injected to create training and evaluation data pairs. We propose a patch-based training scheme tailored for dealing with high-resolution and spatially sparse data, which overcomes the disadvantages of conventional patch-based training methods in high-resolution volumetric shape completion tasks. In particular, the conventional patch-based training is applied to images of high resolution and proves to be effective in tasks such as segmentation. However, we demonstrate the limitations of conventional patch-based training for shape completion tasks, where the overall shape distribution of the target has to be learnt, since it cannot be captured efficiently by a sub-volume cropped from the target. Additionally, the standard dense implementation of a convolutional neural network tends to perform poorly on sparse data, such as the skull, which has a low voxel occupancy rate. Our proposed training scheme encourages a convolutional neural network to learn from the high-resolution and spatially sparse data. In our study, we show that our deep learning models, trained on healthy skulls with synthetic defects, can be transferred directly to craniotomy skulls with real defects of greater irregularity, and the results show promise for clinical use. Project page: https://***/Jianningli/MIA . (c) 2

关键词： Cranioplasty Craniotomy Cranial implant design Deep learning Shape completion

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Synthetic skull bone defects for automatic patient-specific craniofacial implant design

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SCIENTIFIC DATA 2021年第1期8卷 36页

作者： Li, Jianning Gsaxner, Christina Pepe, Antonio Morais, Ana Alves, Victor von Campe, Gord Wallner, Juergen Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16c-2 A-8010 Graz Austria Comp Algorithms Med Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 6-1 A-8036 Graz Austria Univ Minho Sch Engn Dept Informat Braga Portugal Univ Minho Algoritmi Ctr Braga Portugal Med Univ Graz Dept Neurosurg Auenbruggerpl 29 A-8036 Graz Austria

Patient-specific craniofacial implants are used to repair skull bone defects after trauma or surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is usually time-consuming and expensive. Recent advances in additive manufacturing made the in-hospital or in-operation-room fabrication of personalized implants feasible. However, the implants are still manufactured by external companies. To facilitate an optimized workflow, fast and automatic implant manufacturing is highly desirable. Data-driven approaches, such as deep learning, show currently great potential towards automatic implant design. However, a considerable amount of data is needed to train such algorithms, which is, especially in the medical domain, often a bottleneck. Therefore, we present CT-imaging data of the craniofacial complex from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs and 240 corresponding implants. Based on this work, automatic implant design and manufacturing processes can be trained. Additionally, the data of this work build a solid base for researchers to work on automatic cranial implant designs. Image Acquisition Matrix Size center dot Image Slice Thickness center dot craniofacial regionimaging technique center dot computed tomography Sample Characteristic - Organism Machine-accessible metadata file describing the reported data: https://***/10.6084/***.13265225

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