多学科交叉新秀：古生物信息学的兴起与发展

刘建妮; 冯筠; 李忠虎; 韩健; 颜建强; 王惠亚; 黄康; 黄康俊; 姜博; 温超; 张敏; 章勇勤; 张卫国; 沈妍

doi:10.16152/j.cnki.xdxbzr.2023-06-002

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多学科交叉新秀：古生物信息学的兴起与发展

西北大学学报110周年刊庆 | 更新时间：2024-01-03

- 多学科交叉新秀：古生物信息学的兴起与发展
- A new domain of multidisciplinary research: The rise and development of Paleo-bioinformatic
- 西北大学学报（自然科学版） 2023年53卷第6期页码：886-899
- 作者机构：
  
  1.西北大学　地质学系/大陆动力学国家重点实验室，陕西省早期生命与环境重点实验室，西安市古生物信息学重点实验室，陕西　西安　710069
  2.西北大学　信息科学与技术学院/西安市古生物信息学重点实验室，陕西　西安　710069
  3.西北大学　生命科学学院/西安市古生物信息学重点实验室，陕西　西安　710069
  4.西北大学　数学学院/西安市古生物信息学重点实验室，陕西　西安　710069
  5.西北大学　艺术学院/西安市古生物信息学重点实验室，陕西　西安　710069
- 作者简介：
  
  [ "刘建妮，西北大学地质学系教授，博士生导师，洪堡学者，国家重点研发计划首席科学家。中组部万人计划“中青年科技领军人才”，教育部“长江学者”特聘教授。获得“中国青年女科学家奖”“全国三八红旗手”等荣誉称号。", "主要从事早期生命起源及其与环境的协同演化研究，系列发现和研究在国内外尚属首次，在相关领域具有引领作用。先后主持科技部“973计划”首批青年科学家专题项目、国家自然科学基金“重点基金”项目等多项课题。发表论文83篇(其中SCI论文65篇)，第一作者和/或通讯作者SCI论文31篇，包括Nature封面论文、Science及PNAS等国际重要期刊。系列研究和发现得到了国际同行的正面引用和评价，多项研究成果被国内外多家科研媒体和机构做亮点报道。荣获多个奖项，如国家自然科学奖二等奖，陕西省科学技术奖一等奖，中国高校十大科技进展，全国科普工作先进工作者等。" ]
- 基金信息：
  
  国家自然科学基金重点项目(42130206);国家自然科学基金重大项目(41890845);国家自然科学基金委创新群体项目(41621003);高等学校学科创新引智计划(D17063);中国科学院战略先导专项(XDB26000000);陕西省“三秦学者”创新团队支持计划;陕西省科技创新团队(2019TD-012);西安市古生物信息学重点实验室建设经费
- DOI：10.16152/j.cnki.xdxbzr.2023-06-002
  中图分类号：
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刘建妮, 冯筠, 李忠虎, 等. 多学科交叉新秀：古生物信息学的兴起与发展[J]. 西北大学学报（自然科学版）, 2023,53(6):886-899.

LIU Jianni, FENG Jun, LI Zhonghu, et al. A new domain of multidisciplinary research: The rise and development of Paleo-bioinformatic[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):886-899.
刘建妮, 冯筠, 李忠虎, 等. 多学科交叉新秀：古生物信息学的兴起与发展[J]. 西北大学学报（自然科学版）, 2023,53(6):886-899. DOI： 10.16152/j.cnki.xdxbzr.2023-06-002.

LIU Jianni, FENG Jun, LI Zhonghu, et al. A new domain of multidisciplinary research: The rise and development of Paleo-bioinformatic[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):886-899. DOI： 10.16152/j.cnki.xdxbzr.2023-06-002.

摘要

古生物学的研究对象为“死”的化石，如何将死的化石以“活”的形式展示给大众历来是古生物学研究的难点与热点之一。古生物信息学是西北大学地质学系刘建妮教授首次提出的全新概念，它是古生物学、信息学、数学、生物学及艺术等多学科的交叉融合所产生的新学科，主要致力于挖掘古生物之间或者古生物与现生生物之间的谱系演化关系，对古生物进行三维检索、三维重建及艺术展示等研究方向。该文介绍了该团队近年来在古生物化石资源电子化平台、古生物谱系分析和古生物化石图像检索及三维重建的研究成果，展示了古生物信息可视化技术及其相关科普产品研发。最后展望了古生物信息学的发展前景，指出它必将是21世纪自然科学的核心领域之一。

Abstract

The research object of paleontology is " dead" fossils. How to display dead fossils to the public in the " living" form has always been one of the difficulties and hotspots in paleontology research. Paleo-bioinformaticsis a new concept which was proposed by Prof. Jianni Liu in 2012. Paleo-bioinformatics is a multidisciplinary cross-fertilization of paleontology, informatics, mathematics, biology and art, which not only strives to scientifically and aesthetically display the evolutionary relationship between fossils or between fossils and living organisms, but also helps to restore and 3D reconstruct of fossils based on their characters. This paper introduces the development history and construction results of the electronic platform for paleontological fossil resources, sorts out the research content of palaeontological pedigree analysis methods, expounds the research results of paleontological fossil images retrieval and three-dimensional reconstruction, and demonstrates the research and development of paleontological information visualization technology and related popular science products. Finally, the development prospect of Paleo-bioinformatics is forecasted, which will be one of the core fields of natural science in the 21st century.

关键词

古生物信息学古生物化石资源平台古生物谱系分析化石图像检索化石三维重建

Keywords

paleo-bioinformaticspalaeontological fossil resources platformpalaeontological pedigree analysisfossil image retrievalthree-dimensional reconstruction of fossils

references

NEI M, KUMAR S. Molecular Evolution and Phylogenetics[M]. New York: Oxford University Press, 2000.

黄原. 分子系统发生学[M]. 北京: 科学出版社, 2012.

SWOFFORD D L. PAUP*.Phylogenetic analysis using parsimony (* and other methods)[M]. Sunderland, MA: Sinauer Associates, 2002.

GOLOBOFF P A, FARRIS J S, NIXON K C. TNT, a free program for phylogenetic analysis[J]. Cladistics, 2008, 24(5): 774-786.

HUELSENBECK J P, RONQUIST F. MRBAYES: Bayesian inference of phylogenetic trees[J]. Bioinformatics, 2001, 17(8): 754-755.

LIU Y Y, SLOTINE J J, BARABÁSI A L. Reply[J]. Nature, 2011, 478(7369): E4-E5.

SAITOU N, NEI M. The neighbor-joining method: a new method for reconstructing phylogenetic trees[J]. Molecular Biology and Evolution, 1987, 4(4): 406-425.

TORRES A, GOLOBOFF P A, CATALANO S A. Parsimony analysis of phylogenomic datasets (I): Scripts and guidelines for using TNT (Tree Analysis using New Technology)[J]. Cladistics, 2022, 38(1): 103-125.

RANNALA B, YANG Z H. Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference[J]. Journal of Molecular Evolution, 1996, 43(3): 304-311.

POHLE A, KRÖGER B, WARNOCK R C M, et al. Early cephalopod evolution clarified through Bayesian phylogenetic inference[J]. BMC Biology, 2022, 20(1): 1-30.

KOCH N M, GARWOOD R J, PARRY L A. Fossils improve phylogenetic analyses of morphological characters[J]. Proceedings Biology Science, 2021, 288(1950): 20210044.

DEBASTIANI V J, BASTAZINI V A G, PILLAR V D. Using phylogenetic information to impute missing functional trait values in ecological databases[J]. Ecological Informatics, 2021, 63: 101315.

TALAVERA G, LUKHTANOV V, PIERCE N E, et al. DNA barcodes combined with multilocus data of representative taxa can generate reliable higher-level phylogenies[J]. Systematic Biology, 2022, 71(2): 382-395.

GRAYBEAL A. Is it better to add taxa or characters to a difficult phylogenetic problem? [J]. Systematic Biology, 1998, 47(1): 9-17.

BROWER A V Z, SCHUH R T. Biological systematics: principles and applications[M]. Ithaca, NY: Cornell University Press, 2021.

WIENS J J. Missing data and the design of phylogenetic analyses[J]. Journal of Biomedical Informatics, 2006, 39(1): 34-42.

ARBOUR J H, BROWN C M. Incomplete specimens in geometric morphometric analyses[J]. Methods in Ecology and Evolution, 2014, 5(1): 16-26.

WIENS D J. Does adding characters with missing data increase or decrease phylogenetic accuracy？[J]. Systematic Biology, 1998, 47(4): 625-640.

刘蒙. 基于自编码器与蒙特卡洛树的系统发育树构建方法研究[D]. 西安: 西北大学, 2021.

ZWICKL D J, HILLIS Z D M. Increased taxon sampling greatly reduces phylogenetic error[J]. Systematic Biology, 2002, 51(4): 588-598.

ABERER A J, KROMPASS D, STAMATAKIS A. Pruning rogue taxa improves phylogenetic accuracy: An efficient algorithm and webservice[J]. Systematic Biology, 2013, 62(1): 162-166.

CISMONDI F, FIALHO A S, VIEIRA S M, et al. Missing data in medical databases: Impute, delete or classify？[J]. Artificial Intelligence in Medicine, 2013, 58(1): 63-72.

JANSSEN K J M, DONDERS A R T, JrHARRELL F E, et al. Missing covariate data in medical research: to impute is better than to ignore[J]. Journal of Clinical Epidemiology, 2010, 63(7): 721-727.

申丹丹. 基于层次推断和简约聚类的古生物进化树构建方法研究[D]. 西安: 西北大学, 2019.

BRAZEAU M D, GUILLERME T, SMITH M R. An algorithm for morphological phylogenetic analysis with inapplicable data[J]. Systematic Biology, 2019, 68(4): 619-631.

侯刚. 基于古生物三维模型的化石图像检索技术研究[D]. 西安: 西北大学, 2021.

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

刘曦阳. 图像识别技术在古生物化石图像上的应用[D]. 长春: 吉林大学, 2018.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.New York: ACM, 2012: 10971105.

HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016IEEE IEEE Conference on Computer Vision and Pattern Recognition CVPR. Las Vegas, NV, USA: IEEE, 2016: 770-778.

MARCHANT R, TETARD M, PRATIWI A, et al. Automated analysis of foraminifera fossil records by image classification using a convolutional neural network[J]. Journal of Micropalaeontology, 2020, 39(2): 183-202.

SNELL J, SWERSKY K, ZEMEL R. Prototypical networks for few-shot learning[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York: ACM, 2017: 4080-4090.

刘海艳, 陆映峰. 化石标本的虚拟及现实三维重建研究[J]. 科技风, 2018(6): 63.

董为, 张觉非. 化石标本的虚拟与现实三维重建[M]. 北京: 海洋出版社, 2010: 139-150.

PENTECOST A, EDWARDS H G M. Raman spectroscopy and light microscopy of a modern and sub-fossil microstromatolite: Rivularia haematites (cyanobacteria, Nostocales)[J]. International Journal of Astrobiology, 2002, 1(4): 357-363.

PAN J Y, HAN X G, CHEN W K, et al. Deep mesh reconstruction from single RGB images via topology modification networks[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV). Seoul, Korea(South): IEEE, 2020: 9963-9972.

YUNIARTI A, ARIFINAZ , SUCIATI N. A 3D template-basedpoint generation network for 3D reconstruction from single images[J]. In Applied Soft Computing, 2021, 111: 107749.

ROSAS A, BASTIR M. Thin-plate spline analysis of allometry and sexual dimorphism in the humancraniofacial complex[J]. American Journal of Physical Anthropology, 2002, 117(3): 236-245.

BAZEN A M, GEREZ S H. Fingerprint matching by thin-plate spline modelling of elastic deformations[J]. Pattern Recognition, 2003, 36(8): 1859-1867.

WOOD S N. Thin plate regression splines[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2003, 65(1): 95-114.

KOBBELT L, CAMPAGNA S, VORSATZ J, et al. Interactive multi-resolution modeling on arbitrarymeshes[C]//Proceedings of the 25th annual conference on Computer graphics and interactive techniques.New York: ACM, 1998: 105-114.

SORKINE O, COHEN-OR D, LIPMAN Y, et al. Laplacian surface editing[C]//Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing. New York: ACM, 2004: 175-184.

ZHOU K, HUANG J, SNYDER J, et al. Large mesh deformation using the volumetric graph laplacian[J]. ACM Transactions on Graphics, 2005, 24(3): 496-503.

SORKINE O, ALEXA M. As-rigid-As-possible surface modeling[C]//Proceedings of the fifth Eurographics Symposium on Geometry Processing. Barcelona, Spain New York: ACM, 2007: 109-116.

LEVI Z, GOTSMAN C. Smooth rotation enhanced As-rigid-As-possible mesh animation[J]. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(2): 264-277.

BOTSCH M, SORKINE O. On linear variational surface deformation methods[J]. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(1): 213-230.

POPA T, JULIUS D, SHEFFER A. Material aware mesh deformations[C]//SIGGRAPH’05: ACM SIGGRAPH 2005 Posters. New York: ACM, 2005: 5-es.

ANGUELOV D, SRINIVASAN P, KOLLER D, et al. SCAPE: Shape completion and animation of people[J]. ACM Transactions on Graphics, 24(3): 408-416.

HASLER N, STOLL C, SUNKEL M, et al. A statistical model of human pose and bodyshape[J]. Computer Graphics Forum, 2009, 28(2): 337-346.

CASHMAN T J, FITZGIBBON A W. What shape are dolphins? building 3D morphable models from 2D images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 232-244.

NTOUSKOS V, SANZARI M, CAFARO B, et al. Component-wise modeling of articulated objects[C]//2015 IEEE International Conference on Computer Vision(ICCV). Santiago, Chile: IEEE, 2016: 2327-2335.

KANAZAWA A, KOVALSKY S, BASRI R, et al. Learning 3D deformation of animals from 2D images[J]. Computer Graphics Forum, 2016, 35(2): 365-374.

ZUFFI S, KANAZAWA A, JACOBS D W, et al. 3D menagerie: Modeling the 3D shape and pose of animals[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, HI, USA: IEEE, 2017: 5524-5532.

ZUFFI S, KANAZAWA A, BLACK M J. Lions and tigers and bears: Capturing non-rigid, 3d, articulated shape from images[C]//2018 IEEE/CVF conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 3955-3963.

ZHANG J Y, PEPOSE S, JOO H, et al. Perceiving 3D human-object spatial arrangements from a single image in the wild[M]//Computer Vision-ECCV 2020.Cham: Springer International Publishing, 2020: 34-51.

ZUBIC N, LIÖ P. An effective loss function for generating 3D models from single 2D image without rendering[M]//IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing, 2021: 309-322.

MONNIER T, FISHER M, EFROS A A, et al. Share with thy neighbors: Single-view reconstruction by cross-instance consistency[M]//Lectures Notes in Computer Science.Cham: Springer Nature Switzerland, 2022: 285-303.

周宇航. 基于化石图像显著性特征和关键点检测的三叶虫三维重建算法研究[D]. 西安: 西北大学, 2023.

LIU J N, STEINER M, DUNLOP J A, et al. An armoured Cambrian lobopodian from China with arthropod-like appendages[J]. Nature, 2011, 470(733): 526-530.

HE K Y, LIU J N, HAN J, et al. Comment on ”Ultrastructure reveals ancestral vertebrate pharyngeal skeleton in yunnanozoans”[J]. Science, 2023, 381(6656): eade9707.

KIHM J H, SMITH F W, KIM S, et al. Cambrian lobopodians shed light on the origin of the tardigrade body plan[J]. PNAS, 2023, 120(28): e2211251120.

LIU J N, LEROSEY-AUBRIL R, STEINER M, et al. Origin of raptorial feeding in juvenile euarthropods revealed by a Cambrian radiodontan[J]. National Science Review, 2018, 5(6): 863-869.

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