职　　务：
学　　历： 计算机科学和昆虫学双博士
电　　话： +86
传　　真： +86
电子邮件： ma@mail.kiz.ac.cn
通讯地址： 云南省昆明市教场东路32号 650223
其他主页：

简　　历

马占山，研究员，博导，计算生物与医学生态学学科负责人。美国爱达荷大学计算机科学(2008年)和昆虫学(1997年)双博士; 计算机科学和计算生物学研究科学家。具有在美国硅谷等地近10年的涵盖电子、网络、软件、信息安全领域的计算机高级工程师经历, 并曾担任人类微生物菌群研究计划(Human Microbiome Project)主要研发科学家; 受邀为英国
“Faculty of 1000 Biology & Medicine”撰写学术评论 (2008-2016)。2011年分别入选云南省高端科技人才和海外高层次人才; 2015 年入选云岭产业领军人才。在计算机科学、工程数学、计算智能与人工智能、昆虫学、生态学、生物信息学、人类微生物群系、宏基因组等领域的重要期刊发表论文近百篇；合作研发出大型二、三代基因测序软件、超大规模进化树构建软件（DBG2OLC，SPARC，SparseAssembler, HPTree)，并申请10多项发明专利。 在计算科学领域（非生物学领域）论文可见DBLP计算机科学数据库和MathSciNet 数学网数据库：http://dblp.uni-trier.de/pers/hd/m/Ma:Zhanshan_=Sam=
http://genealogy.math.ndsu.nodak.edu/id.php?id=132255


研究方向

【1】基因大数据科学、三代基因测序软件技术。合作发布如下大型基因测序软件技术:
DBG2OLC, SPARC, SparseAssembler, 10x-assisted-3GS Hybrid Assembly.
【2】人类微生物群系（菌群）医学生态学理论和方法。
主编《生物信息学：计算技术与软件导论》（科学出版社，该书获国家科技出版基金奖励资助），并在 ISME Journal, Ecological Monographs, Molecular Ecology, Science Translational Medicine 等发表论文近50篇。
【3】网络安全可靠性、可存活性理论、进化博弈论、计算智能、人工智能。
编著：《Reliability, Survivability and Resilience: A Unified Theoretic Approach with
Survival Analysis, Dynamic Hybrid Fault Models and Extended Evolutionary Game Theory》
(in Press) Springer. 并在IEEE Translations on Reliability 等期刊和 IEEE / ACM 主办国际会议发表论文30余篇。
软件技术：
DBG2OLC, SPARC, SparseAssembler, 10x-assisted-3GS Hybrid Assembly,
[1] DBG2OLC [三代基因测序组装软件: An ultra efficient de Novo genome assembler for the 3rd generation sequencing technologies (PacBio & Oxford Nanopore)]. Available at:
https://sites.google.com/site/dbg2olc/
https://sourceforge.net/projects/dbg2olc/
[2] Sparc [三代基因测序纠错软件: A sparsity-based consensus algorithm for long erroneous 3rdGS sequencing reads]. Available at:
https://sourceforge.net/projects/sparc-consensus/
https://github.com/yechengxi/Sparc
[3] SparseAssembler [二代基因测序组装软件: Sparse k-mer Graph for Memory Efficient de novo Genome Assembly). The core algorithm (Sparse k-mer) was used in BGI’s SoapDenovo-II, the updated version of BGI’s flagship software SoapDenovo]. Available at:
https://sites.google.com/site/sparseassembler/
https://sourceforge.net/projects/sparseassembler/
[4] 10x-assisted-3GS Hybrid Assembly:
Ma ZS et al. (2018) Hybrid assembly of ultra-long Nanopore reads augmented with 10×-genomics contigs: Demonstrated with a human genome. Genomics, vol. 110, https://doi.org/10.1016/j.ygeno.2018.12.013
[5] HPTree: Reconstructing evolutionary trees in parallel for massive sequences. BMC System Biology.
http://lab.malab.cn/soft/HPtree/contact.html

承担科研项目

(1)国家自然科学基金(信息科学部)：生态“舞台”与进化计算“演出”(2012－2015年)
(2)国家自然科学基金(管理科学部)：网络系统可存活性理论的扩展及实证研究(2015－2018年)
(3)国家自科学基金(生命科学部)：基于宏遗传技术的生物多样性检测法(2012－2015年)
(4)云南省引进高端科技人才项目: 面向计算生物信息学的GP-GPU计算技术（2012-2016）
(5)海外高层次人才引进计划 (2012-2015年)
(6)云岭产业技术领军人才基金（2015-2019年）
(7) 国际合作项目：基因大数据信息技术及其应用（2017-2019年）
(8) 国家自科学基金(生命科学部): 菌群多样性与疾病关系的生态机制及病因研究 （2020-2023）

专家类别

研究员；云南省高端科技人才；海外高层次人才；云岭产业技术领军人才
社会任职

Faculty 1000 of Biology and Medicine, UK (2008-2016)
PeerJ Computer Science, USA (Current)
Bullard Fellow, Harvard University, USA (2021-)

获奖及荣誉


代表论著

1. Ma ZS (2020) Predicting the Outbreak Risks and Inflection Points of COVID‐19 Pandemic with Classic Ecological Theories. Advanced Science, https://doi.org/10.1002/advs.
2. Ma ZS (2020) Critical network structures and medical ecology mechanisms underlying human microbiome-associated diseases. iScience. doi: 10.1016/j.isci.2020.101195.
3. Ma ZS (2020) Testing the Anna Karenina Principle in human microbiome-associated diseases. iScience, 23(4):101007.
4. Ma ZS (2020) Heterogeneity-disease relationship in the human microbiome associated diseases. FEMS Microbiology Ecology, Volume 96, fiaa093. doi:10.1093/femsec/fiaa093.
5. Ma ZS & Taylor RAJ (2020) Human reproductive system microbiomes exhibited significantly different heterogeneity scaling with gut microbiome, but the intra-system scaling is invariant. Oikos, 129 (6): 903-911.
6.Ma ZS, Li LW, Zhang YP (2020) Defining individual-level genetic diversity and similarity profiles.Scientific Reports, 10(1): 5805.
7. Li WD & Ma ZS (2020) A theoretic approach to the mechanism of gut microbiome translocation in SIV-infected Asian macaques. FEMS Microbiology Ecology, Volume 96, Issue 8, August 2020, fiaa134, https://doi.org/10.1093/femsec/fiaa134
8. Li WD & Ma ZS (2020) Dominance network analysis of the healthy human vaginal microbiome not dominated by Lactobacillus species. Computational and Structural Biotechnology Journal.
9.Li WD, Ma ZS (2020) FBA Ecological Guild: Trio of Firmicutes-Bacteroidetes Alliance against actinobacteria in human oral microbiome.Scientific Reports, 10(1): 287.
10. Li WD & Ma ZS (2020) Population-level diversity-disease relationship (DDR) in the human microbiome associated diseases. Frontiers in Microbiology. (in Revision)
11. Li WD, Sun Y, et al (2020) Ecological and network analyses reveal four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC). (Under Review)
12. Li LW, Li WD, Zou Q, Ma ZS (2020) Network analysis of the hot spring microbiome sketches possible niche differentiations among ecological guilds.Ecological Modelling, vol. 431. doi: 10.1016/j.ecolmodel.2020.109147.
13.Li LW, Ma ZS (2020) Species sorting and neutral theory analyses reveal archaeal and bacterial communities are assembled differently in hot springs.Frontiers in Bioengineering and Biotechnology, 8. doi: 10.3389/fbioe.2020.00464.
14 Luo J, Chai J, et al. (2020) From asymmetrical to balanced genomic diversification during rediploidization: Subgenomic evolution in allotetraploid fish. Science Advances. DOI: 10.1126/sciadv.aaz7677
15. Zhang Y & Zhang Z et al. (2020) Selective loss of 5hmC promotes neurodegeneration in the mouse model of Alzheimer's disease. The FASEB Journal, https://doi.org/10.1096/fj.R
16. Sun Y, Li LW et al (2020) Does ulcerative colitis influence the spatial heterogeneity of the intestinal mucosal microbiome? Evolutionary Bioinformatics
17. Li LW, Ma ZS (2020) Modeling the microbiome assembly of breast tissues with tumors via neutral, near neutral and niche-neutral hybrid models. (Under Review)
18. Chen HJ, Yi B et al. (2020) Diversity scaling of human digestive tract (DT) microbiomes: the intra-DT and inter-individual patterns. BMC Microbiology (Under Review)
19. Ma ZS (2020) Niche-neutral theoretic approach to mechanisms underlying biodiversity and biogeography of human microbiomes. Evolutionary Applications.
20.Ma ZS (2020) Estimating the optimum coverage and quality of amplicon sequencing with Taylor's power law extensions.Frontiers in Bioengineering and Biotechnology, 8:372.
21. Ma ZS (2020) Assessing and interpreting the metagenome heterogeneity with power law.Frontiers in Microbiology, 11: 648.
22. Ma ZS & AM Ellison (2020) Risks and etiology of bacterial vaginosis revealed by species dominance network analysis. (Preprint) https://www.medrxiv.org/content/10.1101/2020.05.23.**v1
23. Ma ZS & AM Ellison (2020) Towards a unifying diversity-area relationship (DAR) of species- and gene-diversity. (Preprint) https://www.biorxiv.org/content/10.1101/2020.05.16.099861v1
24. Ma ZS (2020) Towards unifying microbiome ecology of metagenomic genes and microbial taxa: critical ecological processes and network structures. Molecular Ecology Resources (Under Review)
25. Ma ZS, Li WD, Shi P (2020) Microbiome-diversity—host-phylogeny relationship in animal gastrointestinal tract microbiome. Open Biology (In Revision)
26. Ma ZS, Li WD (2019) How man and woman are different in their microbiome: Ecological and network analyses of the microgenderome. Advanced Science, 6(23): **.
27. Ma ZS, Li LW, Gotelli NJ (2019) Diversity-disease relationships and shared species analyses for human microbiome-associated diseases. The ISME Journal, 13: 1911–1919.
28. Ma ZS, Ellison AM (2019) Dominance network analysis provides a new framework for studying the diversity-stability relationship. Ecological Monographs. 89(2), DOI: 10.1002/ecm.1358.
29. Li LW & Ma ZS (2019) Comparative power law analysis for the spatial heterogeneity scaling of the hot-spring and human microbiomes. Molecular Ecology, 28(11): 2932-2943.
30. Ma ZS (2019) A new DTAR (diversity–time–area relationship) model demonstrated with the indoor microbiome. Journal of Biogeography, 46(1). DOI: 10.1111/jbi.13636
31. Ma ZS, Li LW, Ye CX, Peng MS, Zhang YP (2019) Hybrid assembly of ultra-long Nanopore reads augmented with 10×-genomics contigs: Demonstrated with a human genome. Genomics, 111(6): 1896-1901.
32. Li LW, Ma ZS (2019) Global microbiome diversity scaling in hot springs with DAR (diversity-area relationship) profiles. Frontiers in Microbiology, vol. 10, article 118.
33. Li WD, Ma ZS (2019) Diversity scaling of human vaginal microbial communities. Zoological Research. 40(6): 587-594.
34. Ma ZS & AM Ellison (2018) A unified concept of dominance applicable at both community and species scale. Ecosphere, https://doi.org/10.1002/ecs2.2477.
35. Ma ZS (2018) Extending species-area relationships (SAR) to diversity-area relationships (DAR), Ecology and Evolution, 8(20): 10023-10038.
36. Ma ZS (2018) Diversity time-period and diversity-time-area relationships exemplified by the human microbiome. Scientific Reports, 8(1): 7214.
37. Ma ZS (2018) Sketching the human microbiome biogeography with DAR (diversity-area relationship) profiles. Microbial Ecology, vol. 76, https://doi.org/10.1007/s00248-018-1245-6
38. Ma ZS, Li LW, Li W (2018) Assessing and interpreting the within-Body biogeography of human microbiome diversity. Frontiers in Microbiology, 9:1619.
39. Ma ZS, Li LW (2018) Measuring metagenome diversity and similarity with Hill numbers. Molecular Ecology Resources, 18(6): 1339-1355.
40. Li W, Yuan Y, et al. (2018) A cross-scale neutral theory approach to the influence of obesity on community assembly of human gut microbiome. Frontiers in Microbiology, 9: 2320.
41. Sun Y, Li LW, et al. (2018) The gut microbiota heterogeneity and assembly changes associated with the IBD. Scientific Reports, 9(1): 440.
42. Ma ZS, Li LW (2018) Semen microbiome biogeography: an analysis based on a Chinese population study. Frontiers in Microbiology, vol. 9: article 3333.
43. Ma ZS, Ye DD (2017) Trios—promising in silico biomarkers for differentiating the effect of disease on the human microbiome network. Scientific Reports, 7(1): 13259.
44. Ma ZS (2017) The P/N (Positive-to-Negative Links) ratio in complex networks—a promising in silico biomarker for detecting changes occurring in the human microbiome. Microbial Ecology, 75(4): 1063-1073.
45. Ma ZS, Li L (2017) Quantifying the human vaginal community state types (CSTs) with the species specificity index. Peer J, 2017, 5: e3366.
46. Chen HJ, Peng S, et al. (2017) Oral microbial community assembly under the influence of periodontitis. PLoS One, 12(8): e**.
47. Dai L, Kou H, et al. (2017) Does colorectal cancer significantly influence the assembly of gut microbial communities? Peer J, 5(8): e3383. DOI:10.7717/peerj.3383.
48. Wei L, Xing PW, et al. (2017) CPPred-RF: A Sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. Journal of Proteome Research, 16(5): 2044‐2053.
49. Zou Q, Wan S, Zeng X, Ma ZS (2017) Reconstructing evolutionary trees in parallel for massive sequences. BMC Systems Biology, 11(S6): 100. DOI: 10.1186/s12918-017-0476-3.
50. Ma ZS (2017) Measuring microbiome diversity and similarity with Hill Numbers. Chapter 8, in “Metagenomics” https://doi.org/10.1016/B978-0-08-102268-9.00008-2, Elsevier
51. Ye CX & ZS Ma (2016) Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads. PeerJ. 4:e2016. DOI 10.7717/peerj.2016https://peerj.com/articles/2016/
52. Ye CX, C Hill, J Ruan, ZS Ma (2016) DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci Rep, 6: 31900.
53. Li LW & Ma ZS (2016) Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets. Scientific Reports, 6:31448.
54. Ma ZS, Li LW, et al. (2016) Integrated network-diversity analyses suggest suppressive effect of Hodgkin’s lymphoma and slightly relieving effect of chemotherapy on human milk microbiome. Scientific Reports, 6:28048.
55. Wang Y, Wang R, et al. (2016) Sex Ratio Elasticity Influences the Selection of Sex Ratio Strategy. Scientific Reports, 6(1):39807.
56. Ma ZS, et al. (2016) A Brief Review on the Ecological Network Analysis with Applications in the Emerging Medical Ecology. pp 7-41, In “Hydrocarbon and Lipid Microbiology Protocols, Springer Protocols Handbooks”. Editors: McGenity T. et al. Springer.
57. Ma ZS, et al. (2015) Network analysis suggests a potentially ‘evil’ alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Scientific Reports, 5:8275.
58. Ma ZS (2015) Power law analysis of the human microbiome. Molecular Ecology, 24(21):5428‐5445.
59. Ma ZS (2015) Towards computational models of animal cognition, an introduction for computer scientists. Cognitive Systems Research, 33:42-69.
60. Ma ZS (2015) Towards computational models of animal communication, an introduction for computer scientists. Cognitive Systems Research, 33:70-99.　　
61. Ye CX, Hill C, Ruan J, Ma ZS (2014) DBG2OLC: efficient assembly of large genomes using the compressed overlap graph. Paper: http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1410.2801 Software: http://sites.google.com/site/dbg2olc/
62. Li H, Ye DD, et al. (2014) Soil bacterial communities of different natural forest types in China. Plant and Soil, 383.
63. Guan Q & Ma ZS (2014) Ecological analysis of the human milk microbiome. Chinese Science Bulletin, 59(22): 2205-2212. http://www.eurekalert.org/pub_releases/2014-09/scp-ahb090214.php
64. Ma ZS , Yang L, Neilson RP, Hess A, Millar R (2014) A survivability-centered research agenda for cloud computing supported emergency response and management systems. 17pp. The 35th IEEE-AIAA Aerospace Conference (Aerospace 2014), Big Sky, Montana, USA, March 7-15th, doi: 10.1109/AERO.2014.**
65. Zhang ZG, Geng JW, et al. (2014) Spatial heterogeneity and co-occurrence patterns of human mucosal associated intestinal microbiota. The ISME Journal, 8(4): 881‐893.
66. Ma ZS (2013) Stochastic Populations, Power Law, and Fitness Aggregation in Genetic Algorithms. Fundamenta Informaticae, vol. 122, pp173-206. 　　
67. Ma ZS (2013) First passage time and first passage percolation models for analyzing network resilience and effective strategies in strategic information warfare research. I. J. Information and Computer Security, 5(4): 334-358.
68. Ma ZS (2012) Chaotic populations in Genetic Algorithms. Applied Soft Computing, 12(8): 2409-2424.
69. Ma ZS (2012) A unified definition for reliability, survivability and resilience inspired by the handicap principle and ecological stability. I. J. of Critical Infrastructures, 8(2): 242-272.
70. Ma ZS (2012) A note on extending Taylor’s power law for characterizing human microbial communities: inspiration from comparative studies on the distribution patterns of insects and galaxies, and as a case study for medical ecology and personalized medicine. http://adsabs.harvard.edu/abs/2012arXiv1205.3504M　　
71. Ye CX, Ma ZS, et al. (2012) Exploiting sparseness in de novo genome assembly. BMC Bioinformatics, 13(Suppl 6):S1.
72. Gajer P, Brotman RM, et al. (2012) Temporal Dynamics of the Human Vaginal Microbiota. Sci Transl Med., 4(132):132ra52.
73. Ma ZS, et al. (2012) A Bird's Eye View of Microbial Community Dynamics. In “Microbial Ecological Theory: Current Perspectives.” Editors: LA Ogilvie and PR Hirsch. Caister Academic Press.
74. Ma ZS & Krings AW. (2011) Dynamic hybrid fault modeling and extended evolutionary Game theory for reliability, survivability and fault tolerance analyses.IEEE Transactions on Reliability, 60(1):180-196. 　
75. Ma ZS (2011) Ecological ‘theater’ for evolutionary computing ‘play’: some insights from population ecology and evolutionary ecology. I. Journal of Bio-Inspired Computing, 4(1):31-55. 　
76. Ma ZS (2011) Frailty modeling for risk analysis in network security and survivability. I. J. Computer and Information Security, 4:276-294.
77. Ma ZS, et al. (2011) Insect navigation and communication in flight and migration: a potential model for joining and collision avoidance in MAVs (Micro-Aerial Vehicle) and mobile robots fleet control. Proc. of the 32nd IEEE-AIAA Aerospace Conference. 14pp, Big Sky, Montana, USA.
78. Ye CX, Cannon C, Ma ZS*, Yu DW, Pop M* (2011) SparseAssembler2: Sparse k-mer Graph for Memory Efficient Genome Assembly. http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1108.3556
79. Ma ZS & AW Krings et al. (2011) Has the cyber warfare threat been overstated? A cheap talk game-theoretic perspective. The 7th Cyberspace Sciences and Information Intelligence Research Workshop, 7th CSIIRW11. October 14-16, 2011. Oak Ridge National Lab, Oak Ridge, USA. 　
80. Ma ZS, et al. (2011) Caring about trees in the forest: incorporating frailty in risk analysis for personalized medicine. Personalized Medicine, 8(6): 681-688 　
81. Ma ZS (2010) Is Strategic Information Warfare Really Asymmetric?—a New Perspective from the Handicap Principle. Journal of Information Warfare, 9(3): 51-61. 　
82. Ma ZS, et al. (2010) Logics in Animal Cognition: Are They Important to Brain Computer Interfaces (BCI) and Future Space Missions? Proc. 31st IEEE-AIAA Aerospace Conference 2010, 8pp. Big Sky, Montana, USA. 　
83. Ma ZS (2010) An integrated approach to network intrusion detection with block clustering analysis, generalised logistic regression and linear discriminant analysis. I. J. Information and Computer Security. Vol. 4(1):76-97
84. Ma ZS, FT Sheldon, AW Krings (2010) The handicap principle, strategic information warfare and the paradox of asymmetry. The 6th Cyberspace Sciences and Information Intelligence Research Workshop, 6th CSIIRW10, Oak Ridge National Lab, Oak Ridge, USA. 　
85. Ma ZS (2010) A New Extended Evolutionary Game Theory Approach to Strategic Information Warfare Research. J. of Information Warfare, vol. 8(2).
86. Ma ZS & AW Krings (2009) Is Chaos Theory Relevant to Reliability and Survivability? IEEE-AC paper #1697, 10pp, The 30^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14^th, 2009.
87. Ma ZS (2009) Dragonfly Preying on Flying Insects, Rendezvous Search Games, and Rendezvous and Docking in Space Explorations. IEEE-AC paper #1698, 8pp, The 30^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14^th, 2009.
88. Ma ZS (2009) Cognitive Ecology and Social Learning Inspired Machine Learning: with Particular Reference to the Evolving of Resilient Airborne Networks (AN). IEEE-AC paper #1706, 14pp, The 30^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14^th, 2009.
89. Ma ZS, AW Krings, RE Hiromoto (2009) Dragonfly as a model for UAV/MAV Flight and Communication Controls. IEEE-AC paper #1718, 8pp, The 30^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14^th, 2009.
90. Ma ZS & AW Krings (2009) Insect sensory systems inspired computing and communications. Ad Hoc Networks 7(4): 742-755.
91. Ma ZS, AW Krings et al. (2009) The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking. Springer Lecture Notes in Computer Science, vol. 5854, pp458-468
92. Ma ZS (2009) Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature.Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 195-205.
93. Ma ZS (2009) Towards an Extended Evolutionary Game Theory with Survival Analysis and Agreement Algorithms for Modeling Uncertainty, Vulnerability, and Deception. Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 608-618.
94. Ma ZS, AW. Krings, FT. Sheldon (2009) An outline of the three-layer survivability analysis architecture for strategic information warfare research. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 28
95. Ma ZS, AW Krings, RC Millar (2009) Introduction of first passage time (FPT) analysis for software reliability and network security. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 63
96. Krings AW & Ma ZS (2009) Surviving Attacks and Intrusions: What can we Learn from Fault Models. HICSS (Hawaii International Conference on System Sciences) 2009: 1-8. https://hicss.hawaii.edu/
97. Ma ZS (2011) Did we miss some evidence of chaos in laboratory insect populations? Population Ecology, 53:405–412.
98. Ma ZS (2010) Survival Analysis Approach to Life Table Analysis and Hypothesis Testing for Russian Wheat Aphid Populations. Bulletin of Entomological Research. vol. 100(3): 315-324
99. Ma ZS (2009) A new modelling approach to insect reproduction with same-shape reproduction distribution and rate summation: with particular reference to Russian wheat aphid. Bulletin of Entomological Research, vol. 99(5): 445-455
100. Ma ZS, Bechinski EJ (2008) Life tables and demographic statistics of Russian wheat aphid under different temperatures and host plant stages. European Journal of Entomology, vol. 106:205–210
101. Ma, Z.S. & Bechinski, E.J. (2008) Accelerated failure time modeling of the development and survival of Russian wheat aphid, Diuraphis noxia (Mordvilko). Population Ecology 51(4), 543–548
102. Ma ZS & EJ Bechinski (2008) A survival-analysis-based simulation model for Russian wheat aphid population dynamics. Ecological Modelling, vol. 216(3):323-332
103. Ma ZS & EJ Bechinski (2008) Developmental and Phenological Modeling of Russian Wheat Aphid (Hemiptera: Aphididae). Annals of the Entomological Society of America, Vol. 101(2):351-361
104. Ma ZS, AW Krings, RE Hiromoto (2008) Insect Sensory Systems Inspired Communications and Computing (II): An Engineering Perspective. Funchal, Maderia, Prtugal, Jan 28-31, 2008, IEEE BIOSIGNALS, 292-297
105. Ma ZS & AW Krings (2008) Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs). ACM-IEEE MSWiM 2008, Vancouver, Canada, 100-108.
106. Ma ZS & AW Krings (2008) Spatial Distribution Patterns, Power Law, and the Agent-based Directed Diffusion Sensor Networks. 2008 IEEE PerCom (IEEE Pervasive Computing), Hong Kong, March 17-21, 2008: 596-601.
107. Ma ZS & AW Krings (2008) Dynamic populations in genetic algorithms. The 23rd Annual ACM Symposium on Applied Computing, March 16-20, 2008, Fortaleza, Ceara, Brazil. ACM-SAC 2008: 1807-1811
108. Ma ZS & AW Krings (2008) Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems. Paper #1650, 20pp. The 29^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8^th, 2008.
109. Ma ZS & AW Krings (2008) Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM). Paper #1618, 21pp. The 29^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8^th, 2008.
110. Ma ZS & AW Krings (2008) Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM). Paper #1626, 20pp. The 29^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8^th, 2008.
111. Ma ZS & AW Krings (2008) Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling. Paper #1634, 21pp. The 29^th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8^th, 2008.
112. Krings, AW & Ma ZS (2006) Fault-Models in Wireless Communication: Towards Survivable Ad Hoc Networks. 2006 MILCOM, DOI: 10.1109/MILCOM.2006.302349, 2006 IEEE Military Communications Conference. 3-25 October 2006, Washington, DC.

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口腔抵御机会性病原放线菌感染的潜在机制
https://www.newsbreak.com/news/0NpsEXBq/fba-ecological-guild-trio-of-firmicutes-bacteroidetes-alliance-against-actinobacteria-in-human-oral-microbiome
http://digitalpaper.stdaily.com/http_www.kjrb.com/kjrb/html/2020-02/05/content_439486.htm
http://www.chinanews.com/sh/2020/01-16/**.shtml
支撑“红皇后假设”的人类生殖系统共生菌群证据
http://news.sciencenet.cn/htmlnews/2020/3/436533.shtm
https://m.chinanews.com/wap/detail/zw/jk/2020/03-02/**.shtml
http://www.cas.ac.cn/syky/202003/t**_**.shtml
菌群医学生态学：用生态之眼看医学
http://news.sciencenet.cn/sbhtmlnews/2018/10/340330.shtm
中美合作Sparc新软件弥补三代基因测序“硬伤”
http://news.sciencenet.cn/htmlnews/2016/7/350187.shtm
人体菌群多样性与疾病关系的“1/3猜想”提出（新华网, 基金委， ABC， NBC等报道）
http://www.xinhuanet.com/2019-03/25/c_.htm
http://www.nsfc.gov.cn/csc/20340/20343/36702/index.html
https://www.abc6.com/story/**/study-reveals-little-evidence-for-a-consistent-diversity-disease-relationship-ddr-in-human-microbiome-associated-diseases
https://www.nbc29.com/story/**/study-reveals-little-evidence-for-a-consistent-diversity-disease-relationship-ddr-in-human-microbiome-associated-diseases
预测“暗” 生物多样性有了新数学模型（科技日报，新华网, 基金委报道）
http://digitalpaper.stdaily.com/http_www.kjrb.com/kjrb/html/2019-07/04/content_424963.htm?div=-1
http://www.xinhuanet.com//science/2019-07/04/c_.htm
http://www.nsfc.gov.cn/csc/20340/20289/40461/index.html
http://news.sciencenet.cn/htmlnews/2019/7/428054.shtm?id=428054
研究发现全球温泉有超60000种微生物 (新华社报道、基金委等)
http://www.xinhuanet.com/2019-03/12/c_.htm
http://tech.gmw.cn/2019-03/13/content_**.htm
http://www.chinanews.com/gn/2019/03-12/**.shtml
https://www.afpbb.com/articles/-/**?act=all&pid=
http://www.nsfc.gov.cn/csc/20340/20289/36227/index.html
研究揭示温泉菌群空间分布的特征参数
https://molecularecologyblog.com/2019/07/19/summary-from-the-authors-comparative-power-law-analysis-for-the-spatial-heterogeneity-scaling-of-the-hot%E2%80%90spring-microbiomes-by-lianwei-li-and-sam-ma/
精准诊断一类疾病的核心使能技术
http://news.sciencenet.cn/htmlnews/2017/12/395917.shtm
http://www.cnr.cn/yn/ynkx/**/t**_.shtml
三代基因测序软件
https://www.infoholicresearch.com/market-updates/hybrid-genome-assembly-technology
http://digitalpaper.stdaily.com/http_www.kjrb.com/kjrb/html/2019-01/18/content_413307.htm?div=-1
http://tech.chinadaily.com.cn/a/201901/18/WS5c412af1a**bdc491e.html
https://www.pr.com/press-release/776250
http://www.digitaljournal.com/pr/**
http://news.sciencenet.cn/htmlnews/2016/7/350187.shtm
http://stock.591hx.com/article/2016-06-17/s.shtml
http://guba.eastmoney.com/news,cjpl,.html
http://news.hongzhoukan.com/16/0701/hl221824.html
http://finance.qq.com/a/**/016790.htm
http://news.cnstock.com/industry/sid_zxk/201608/**.htm
http://stock.hexun.com/2016-06-23/.html
http://finance.ifeng.com/a/**/**_0.shtml
http://news.sciencenet.cn/htmlnews/2016/6/348686.shtm
http://news.sciencenet.cn/htmlnews/2016/7/350187.shtm



研究团队

研究团队
姓名（学位；早期背景；目前研究方向）
李连伟（博士生；生物科学；生物信息学)
李文迪（博士生；生物医学；医学生态学、深度学习应用）
陈红菊（博士生；数学；进化博弈论）
MD Motiur Rahman (博士生；概率统计；深度学习)
叶承曦（博士；计算机科学、人工智能；客座）
肖琬蒙 （硕士生：林学；生物信息学）
杨旭（硕士生：昆虫学；医学生物信息学）
乔玉婷 （硕士生：生物信息学；生物信息学）
刘慧敏 （联合培养：计算数学；人工智能）
肖朔潼(实习生;动物医学)
　　
客座教授
美国哈佛大学 Aaron Ellison 教授　
Prof. Aaron Ellison
Senior Research Fellow
Fellow of the Ecological Society of America
Harvard University, USA
http://harvardforest.fas.harvard.edu/aaron-ellison
Recipient of CAS PIFI (President’s International Fellowship Initiative)