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Exploring the intersection of mechanobiology and artificial intelligence

Exploring the intersection of mechanobiology and artificial intelligence
  • Dufort, C. C., Paszek, M. J. & Weaver, V. M. Balancing forces: architectural control of mechanotransduction. Nat. Rev. Mol. Cell Biol. 12, 308–319 (2011).

    Article 
    MATH 

    Google Scholar 

  • Mecham, R. P. Overview of Extracellular Matrix. Curr. Protoc. Cell Biol. 57, 10.1.1–10.1.16 (2012).

    Article 
    MATH 

    Google Scholar 

  • Mouw, J. K., Ou, G. & Weaver, V. M. Extracellular matrix assembly: a multiscale deconstruction. Nat. Rev. Mol. Cell Biol. 15, 771–785 (2014).

    Article 

    Google Scholar 

  • Kanchanawong, P. & Calderwood, D. A. Organization, dynamics and mechanoregulation of integrin-mediated cell–ECM adhesions. Nat. Rev. Mol. Cell Biol. 24, 142–161 (2023).

    Article 
    MATH 

    Google Scholar 

  • Jain, K. et al. Immobile Integrin Signaling Transit and Relay Nodes Organize Mechanosignaling through Force-Dependent Phosphorylation in Focal Adhesions. ACS Nano 19, 2070–2088 (2025).

  • Stashko, C. et al. A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer. Nat. Commun. 14, 1–16 (2023).

    Article 
    MATH 

    Google Scholar 

  • Christiansen, E. M. et al. In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell 173, 792–803.e19 (2018).

    Article 
    MATH 

    Google Scholar 

  • Schmitt, M. S. et al. Machine learning interpretable models of cell mechanics from protein images. Cell 187, 481–494.e24 (2024).

    Article 
    MATH 

    Google Scholar 

  • Frantz, C., Stewart, K. M. & Weaver, V. M. The extracellular matrix at a glance. J. Cell Sci. 123, 4195–4200 (2010).

    Article 
    MATH 

    Google Scholar 

  • Saraswathibhatla, A., Indana, D. & Chaudhuri, O. Cell–extracellular matrix mechanotransduction in 3D. Nat. Rev. Mol. Cell Biol. 24, 495–516 (2023).

    Article 

    Google Scholar 

  • Petridou, N. I., Spiró, Z. & Heisenberg, C. P. Multiscale force sensing in development. Nat. Cell Biol. 19, 581–588 (2017).

    Article 

    Google Scholar 

  • Roca-Cusachs, P., Conte, V. & Trepat, X. Quantifying forces in cell biology. Nat. Cell Biol. 19, 742–751 (2017).

    Article 
    MATH 

    Google Scholar 

  • De Belly, H., Paluch, E. K. & Chalut, K. J. Interplay between mechanics and signalling in regulating cell fate. Nat. Rev. Mol. Cell Biol. 23, 465–480 (2022).

    Article 

    Google Scholar 

  • Hytönen, V. P. & Wehrle-Haller, B. Mechanosensing in cell–matrix adhesions – Converting tension into chemical signals. Exp. Cell Res.343, 35–41 (2016).

    Article 

    Google Scholar 

  • Smith, M. L. et al. Force-Induced Unfolding of Fibronectin in the Extracellular Matrix of Living Cells. PLoS Biol. 5, e268 (2007).

    Article 
    MATH 

    Google Scholar 

  • Saini, K., Cho, S., Dooling, L. J. & Discher, D. E. Tension in fibrils suppresses their enzymatic degradation – A molecular mechanism for ‘use it or lose it’. Matrix Biol. 85–86, 34–46 (2020).

    Article 

    Google Scholar 

  • Kubow, K. E. et al. Mechanical forces regulate the interactions of fibronectin and collagen I in extracellular matrix. Nat. Commun. 6, 1–11 (2015).

    Article 

    Google Scholar 

  • Elosegui-Artola, A. et al. Rigidity sensing and adaptation through regulation of integrin types. Nat. Mater. 13, 631–637 (2014).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Guimarães, C. F., Gasperini, L., Marques, A. P. & Reis, R. L. The stiffness of living tissues and its implications for tissue engineering. Nat. Rev. Mater. 5, 351–370 (2020).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

    Article 
    ADS 

    Google Scholar 

  • Nia, H. T. et al. Solid stress and elastic energy as measures of tumour mechanopathology. Nat. Biomed. Eng. 1, 1–11 (2016).

    Article 
    MATH 

    Google Scholar 

  • Tse, J. M. et al. Mechanical compression drives cancer cells toward invasive phenotype. Proc. Natl Acad. Sci. USA 109, 911–916 (2012).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Fernandez-Sanchez, M. E. et al. Mechanical induction of the tumorigenic β-catenin pathway by tumour growth pressure. Nature 523, 92–95 (2015).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Levental, K. R. et al. Matrix Crosslinking Forces Tumor Progression by Enhancing Integrin Signaling. Cell 139, 891–906 (2009).

    Article 
    MATH 

    Google Scholar 

  • Chaudhuri, O. et al. Extracellular matrix stiffness and composition jointly regulate the induction of malignant phenotypes in mammary epithelium. Nat. Mater. 13, 970–978 (2014).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Mai, Z., Lin, Y., Lin, P., Zhao, X. & Cui, L. Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy. Cell Death Dis. 15, 1–16 (2024).

    Article 
    MATH 

    Google Scholar 

  • Tamiello, C. et al. Soft substrates normalize nuclear morphology and prevent nuclear rupture in fibroblasts from a laminopathy patient with compound heterozygous LMNA mutations. Nucleus 4, 61 (2013).

    Article 

    Google Scholar 

  • Swift, J. et al. Nuclear lamin-A scales with tissue stiffness and enhances matrix-directed differentiation. Science (1979) 341, 1240104 (2013).

  • Jetta, D., Gottlieb, P. A., Verma, D., Sachs, F. & Hua, S. Z. Shear stress-induced nuclear shrinkage through activation of Piezo1 channels in epithelial cells. J. Cell Sci. 132, jcs226076 (2019).

  • Chistiakov, D. A., Orekhov, A. N. & Bobryshev, Y. V. Effects of shear stress on endothelial cells: go with the flow. Acta Physiol. (Oxf.) 219, 382–408 (2017).

    Article 
    MATH 

    Google Scholar 

  • Roux, E., Bougaran, P., Dufourcq, P. & Couffinhal, T. Fluid Shear Stress Sensing by the Endothelial Layer. Front Physiol. 11, 533349 (2020).

    Article 

    Google Scholar 

  • Pahakis, M. Y., Kosky, J. R., Dull, R. O. & Tarbell, J. M. The role of endothelial glycocalyx components in mechanotransduction of fluid shear stress. Biochem Biophys. Res. Commun. 355, 228–233 (2007).

    Article 

    Google Scholar 

  • Girard, P. R. & Nerem, R. M. Shear stress modulates endothelial cell morphology and F-actin organization through the regulation of focal adhesion-associated proteins. J. Cell Physiol. 163, 179–193 (1995).

    Article 
    MATH 

    Google Scholar 

  • Steward, R., Tambe, D., Corey Hardin, C., Krishnan, R. & Fredberg, J. J. Fluid shear, intercellular stress, and endothelial cell alignment. Am. J. Physiol. Cell Physiol. 308, C657 (2015).

    Article 

    Google Scholar 

  • Sumpio, B. E. & Banes, A. J. Response of porcine aortic smooth muscle cells to cyclic tensional deformation in culture. J. Surg. Res 44, 696–701 (1988).

    Article 

    Google Scholar 

  • Dang, C. V. & Semenza, G. L. Oncogenic alterations of metabolism. Trends Biochem Sci. 24, 68–72 (1999).

    Article 
    MATH 

    Google Scholar 

  • Cukierman, E., Pankov, R., Stevens, D. R. & Yamada, K. M. Taking cell-matrix adhesions to the third dimension. Science (1979) 294, 1708–1712 (2001).

    Google Scholar 

  • Yamada, K. M., Doyle, A. D. & Lu, J. Cell–3D matrix interactions: recent advances and opportunities. Trends Cell Biol. 32, 883–895 (2022).

    Article 
    MATH 

    Google Scholar 

  • Kai, F. et al. ECM dimensionality tunes actin tension to modulate endoplasmic reticulum function and spheroid phenotypes of mammary epithelial cells. EMBO J. 41, 109205 (2022).

    Article 

    Google Scholar 

  • Erler, J. T. & Weaver, V. M. Three-dimensional context regulation of metastasis. Clin. Exp. Metastasis 26, 35 (2009).

    Article 
    MATH 

    Google Scholar 

  • Loessner, D. et al. Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells. Biomaterials 31, 8494–8506 (2010).

    Article 
    MATH 

    Google Scholar 

  • Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J. & Werb, Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 11, 1–19 (2020).

    Article 

    Google Scholar 

  • Huang, J. et al. Extracellular matrix and its therapeutic potential for cancer treatment. Signal Transduct. Target. Ther. 6, 1–24 (2021).

    MATH 

    Google Scholar 

  • Kechagia, J. Z., Ivaska, J. & Roca-Cusachs, P. Integrins as biomechanical sensors of the microenvironment. Nat. Rev. Mol. Cell Biol. 20, 457–473 (2019).

    Article 

    Google Scholar 

  • Benham-Pyle, B. W., Pruitt, B. L. & Nelson, W. J. Mechanical strain induces E-cadherin-dependent Yap1 and β-catenin activation to drive cell cycle entry. Science (1979) 348, 1024–1027 (2015).

    Google Scholar 

  • Coste, B. et al. Piezo1 and Piezo2 are essential components of distinct mechanically activated cation channels. Science (1979) 330, 55–60 (2010).

    MATH 

    Google Scholar 

  • Groves, J. T. & Kuriyan, J. Molecular mechanisms in signal transduction at the membrane. Nat. Struct. Mol. Biol. 17, 659–665 (2010).

    Article 
    MATH 

    Google Scholar 

  • Yang, B. et al. Stopping transformed cancer cell growth by rigidity sensing. Nat. Mater. 19, 239 (2020).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Ghassemi, S. et al. Cells test substrate rigidity by local contractions on submicrometer pillars. Proc. Natl. Acad. Sci. USA 109, 5328–5333 (2012).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Jain, K. et al. Intrinsic self-organization of integrin nanoclusters within focal adhesions is required for cellular mechanotransduction. bioRxiv 2023.11.20.567975 (2023).

  • Oria, R. et al. Force loading explains spatial sensing of ligands by cells. Nature 552, 219–224 (2017).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Giannone, G. & Sheetz, M. P. Substrate rigidity and force define form through tyrosine phosphatase and kinase pathways. Trends Cell Biol. 16, 213–223 (2006).

    Article 
    MATH 

    Google Scholar 

  • Elosegui-Artola, A. et al. Force Triggers YAP Nuclear Entry by Regulating Transport across Nuclear Pores. Cell 171, 1397–1410.e14 (2017).

    Article 
    MATH 

    Google Scholar 

  • Sathe, A. R., Shivashankar, G. V. & Sheetz, M. P. Nuclear transport of paxillin depends on focal adhesion dynamics and FAT domains. J. Cell Sci. 129, 1981–1988 (2016).

    Article 

    Google Scholar 

  • Wei, S. C. et al. Matrix stiffness drives epithelial–mesenchymal transition and tumour metastasis through a TWIST1–G3BP2 mechanotransduction pathway. Nat. Cell Biol. 17, 678–688 (2015).

    Article 
    MATH 

    Google Scholar 

  • Vining, K. H. & Mooney, D. J. Mechanical forces direct stem cell behaviour in development and regeneration. Nat. Rev. Mol. Cell Biol. 18, 728–742 (2017).

    Article 
    MATH 

    Google Scholar 

  • Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix Elasticity Directs Stem Cell Lineage Specification. Cell 126, 677–689 (2006).

    Article 

    Google Scholar 

  • Qin, R. et al. Tumor Suppressor DAPK1 Catalyzes Adhesion Assembly on Rigid but Anoikis on Soft Matrices. Front Cell Dev. Biol. 10, 959521 (2022).

    Article 

    Google Scholar 

  • Von Erlach, T. C. et al. Cell-geometry-dependent changes in plasma membrane order direct stem cell signalling and fate. Nat. Mater. 17, 237–242 (2018).

    Article 
    ADS 

    Google Scholar 

  • Théry, M., Pépin, A., Dressaire, E., Chen, Y. & Bornens, M. Cell distribution of stress fibres in response to the geometry of the adhesive environment. Cell Motil. Cytoskeleton 63, 341–355 (2006).

    Article 

    Google Scholar 

  • Paul, C. D., Mistriotis, P. & Konstantopoulos, K. Cancer cell motility: lessons from migration in confined spaces. Nat. Rev. Cancer 17, 131–140 (2016).

    Article 

    Google Scholar 

  • Miron-Mendoza, M., Seemann, J. & Grinnell, F. The differential regulation of cell motile activity through matrix stiffness and porosity in three dimensional collagen matrices. Biomaterials 31, 6425–6435 (2010).

    Article 
    MATH 

    Google Scholar 

  • Hiraki, H. L. et al. Fiber density and matrix stiffness modulate distinct cell migration modes in a 3D stroma mimetic composite hydrogel. Acta Biomater. 163, 378–391 (2023).

    Article 
    MATH 

    Google Scholar 

  • Wolf, K. & Friedl, P. Extracellular matrix determinants of proteolytic and non-proteolytic cell migration. Trends Cell Biol. 21, 736–744 (2011).

    Article 
    MATH 

    Google Scholar 

  • Xia, F. & Youcef-Toumi, K. Review: Advanced Atomic Force Microscopy Modes for Biomedical Research. Biosensors 12, 1116 (2022).

  • Ciasca, G. et al. Nano-mechanical signature of brain tumours. Nanoscale 8, 19629–19643 (2016).

  • Miroshnikova, Y. A. et al. Tissue mechanics promote IDH1-dependent HIF1α-tenascin C feedback to regulate glioblastoma aggression. Nat. Cell Biol. 18, 1336–1345 (2016).

  • Maller, O. et al. Tumour-associated macrophages drive stromal cell-dependent collagen crosslinking and stiffening to promote breast cancer aggression. Nat Mater 20, 548–559 (2021).

  • Plodinec, M. et al. The nanomechanical signature of breast cancer. Nat. Nanotechnol. 7, 757–765 (2012).

  • Fiore, V. F. et al. αvβ3 Integrin drives fibroblast contraction and strain stiffening of soft provisional matrix during progressive fibrosis. JCI Insight 3, e97597 (2018).

  • Laklai, H. et al. Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat. Med. 22, 497–505 (2016).

  • Northey, J. J. et al. Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217. J. Clin. Invest 130, 5721–5737 (2020).

    Article 
    MATH 

    Google Scholar 

  • Bonfanti, A., Kaplan, J. L., Charras, G. & Kabla, A. Fractional viscoelastic models for power-law materials. Soft Matter. 16, 6002-6020 (2020).

  • Tang, X. et al. Measuring the biomechanical properties of prostate tumor tissues by atomic force microscopy. In Eleventh International Conference on Information Optics and Photonics (COIP 2019) 11209, 910–916 (2019).

  • Levillain, A. et al. Mechanical properties of breast, kidney, and thyroid tumours measured by AFM: Relationship with tissue structure. Materialia (Oxf) 25, 101555 (2022).

  • Efremov, Y. M., Wang, W. H., Hardy, S. D., Geahlen, R. L. & Raman, A. Measuring nanoscale viscoelastic parameters of cells directly from AFM force-displacement curves. Sci. Rep. 7, 1541 (2017).

  • Abuhattum, S. et al. An explicit model to extract viscoelastic properties of cells from AFM force-indentation curves. iScience 25, 104016 (2022).

  • Mandal, S. S. Force Spectroscopy on Single Molecules of Life. ACS Omega 5, 11271–11278 (2020).

    Article 
    MATH 

    Google Scholar 

  • Kong, F., García, A. J., Mould, A. P., Humphries, M. J. & Zhu, C. Demonstration of catch bonds between an integrin and its ligand. J. Cell Biol. 185, 1275–1284 (2009).

    Article 

    Google Scholar 

  • Erickson, H. P. Protein unfolding under isometric tension — what force can integrins generate, and can it unfold FNIII domains? Curr. Opin. Struct. Biol. 42, 98–105 (2017).

  • Del Rio, A. et al. Stretching single talin rod molecules activates vinculin binding. Science (1979) 323, 638–641 (2009).

    Google Scholar 

  • Sun, Y., Liu, X., Huang, W., Le, S. & Yan, J. Structural domain in the Titin N2B-us region binds to FHL2 in a force-activation dependent manner. Nat. Commun. 15, 1–14 (2024).

    ADS 

    Google Scholar 

  • Baek, K. Y., Kim, S. & Koh, H. R. Molecular Tension Probes to Quantify Cell-Generated Mechanical Forces. Mol. Cells 45, 26–32 (2022).

    Article 

    Google Scholar 

  • Zhang, Y., Ge, C., Zhu, C. & Salaita, K. DNA-based digital tension probes reveal integrin forces during early cell adhesion. Nat. Commun. 5, 5167 (2014).

    Article 
    ADS 

    Google Scholar 

  • Ringer, P. et al. Multiplexing molecular tension sensors reveals piconewton force gradient across talin-1. Nat. Methods 14, 1090–1096 (2017).

    Article 
    MATH 

    Google Scholar 

  • Kumar, A. et al. Local Tension on Talin in Focal Adhesions Correlates with F-Actin Alignment at the Nanometer Scale. Biophys. J. 115, 1569–1579 (2018).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Tao, A. et al. Identifying constitutive and context-specific molecular-tension-sensitive protein recruitment within focal adhesions. Dev Cell 58, 522–534 (2023).

  • Lagendijk, A. K. et al. Live imaging molecular changes in junctional tension upon VE-cadherin in zebrafish. Nat. Commun. 8, 1402 (2017).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Eder, D., Basler, K. & Aegerter, C. M. Challenging FRET-based E-Cadherin force measurements in Drosophila. Sci. Rep. 7, 13692 (2017).

  • Paszek, M. J. et al. The cancer glycocalyx mechanically primes integrin-mediated growth and survival. Nature 511, 319–325 (2014).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Al Abdullatif, S. et al. Molecular Compressive Force Sensor for Mapping Forces at the Cell-Substrate Interface. J. Am. Chem. Soc. 146, 6830–6836 (2024).

    Article 
    MATH 

    Google Scholar 

  • Bergert, M. et al. Confocal reference free traction force microscopy. Nat. Commun. 7, 12814 (2016).

    Article 
    ADS 

    Google Scholar 

  • Lee, M. et al. High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy. Commun. Biol. 7, 115 (2024).

    Article 
    MATH 

    Google Scholar 

  • Bauer, A. et al. pyTFM: A tool for traction force and monolayer stress microscopy. PLoS Comput Biol. 17, e1008364 (2021).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Hirata, H. & Sokabe, M. Measurement and Manipulation of Cellular Forces Using Silicone Elastomers. In Material-based Mechanobiology. (2022).

  • Han, S. J., Bielawski, K. S., Ting, L. H., Rodriguez, M. L. & Sniadecki, N. J. Decoupling substrate stiffness, spread area, and micropost density: A close spatial relationship between traction forces and focal adhesions. Biophys. J. 103, 640–648 (2012).

    Article 
    ADS 

    Google Scholar 

  • Shroff, N. P. et al. Proliferation-driven mechanical compression induces signalling centre formation during mammalian organ development. Nat. Cell Biol. 26, 519–529 (2024).

    Article 

    Google Scholar 

  • Campàs, O. et al. Quantifying cell-generated mechanical forces within living embryonic tissues. Nat. Methods 11, 183–189 (2014).

    Article 

    Google Scholar 

  • Dolega, M. E. et al. Cell-like pressure sensors reveal increase of mechanical stress towards the core of multicellular spheroids under compression. Nat. Commun. 8, 14056 (2017).

  • Jain, K. et al. Ligand functionalization of titanium nanopattern enables the analysis of cell–ligand interactions by super-resolution microscopy. Nat. Protoc. 17, 2275–2306 (2022).

    Article 
    MATH 

    Google Scholar 

  • Jain, K. et al. TiO2 Nano-Biopatterning Reveals Optimal Ligand Presentation for Cell–Matrix Adhesion Formation. Adv. Mater. 36, 2309284 (2024).

  • Arnold, M. et al. Activation of integrin function by nanopatterned adhesive interfaces. ChemPhysChem 5, 383–388 (2004).

    Article 
    MATH 

    Google Scholar 

  • Turing, A. M. Computing machinery and intelligence. In Machine Intelligence: Persp. Comput. Model. (2012).

  • Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55 (2022).

  • Sarker, I. H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput. Sci. 2, 160 (2021).

  • Li, H. et al. Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images. Commun. Biol. 5, 1–9 (2022).

    ADS 

    Google Scholar 

  • Li, C. et al. Machine learning traction force maps for contractile cell monolayers. Extrem. Mech. Lett. 68, 102150 (2024).

    Article 
    MATH 

    Google Scholar 

  • SubramanianBalachandar, V. A., Islam, M. M. & Steward, R. L. A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation. Biophys. J. 122, 3413–3424 (2023).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Ayad, N. M. E., Lakins, J. N., Ghagre, A., Ehrlicher, A. J. & Weaver, V. M. Tissue tension permits β-catenin phosphorylation to drive mesoderm specification in human embryonic stem cells. bioRxiv (2023).

  • Giolando, P. et al. AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials. Soft Matter 19, 6710–6720 (2023).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Smith, M. G. et al. Machine learning opens a doorway for microrheology with optical tweezers in living systems. AIP Adv. 13, 75315 (2023).

    Article 

    Google Scholar 

  • Fanizzi, A. et al. A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis. BMC Bioinforma. 21, 1–11 (2020).

    Article 
    MATH 

    Google Scholar 

  • Xiao, F. et al. Cerebrospinal fluid biomarkers for brain tumor detection: clinical roles and current progress. Am. J. Transl. Res. 12, 1379 (2020).

    MATH 

    Google Scholar 

  • Kaya-Okur, H. S., Janssens, D. H., Henikoff, J. G., Ahmad, K. & Henikoff, S. Efficient low-cost chromatin profiling with CUT&Tag. Nat. Protoc. 15, 3264–3283 (2020).

    Article 

    Google Scholar 

  • Nakato, R. & Sakata, T. Methods for ChIP-seq analysis: A practical workflow and advanced applications. Methods 187, 44–53 (2021).

  • Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  • Oh, D. et al. CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection. Sci. Rep. 10, 7933 (2020).

    Article 
    ADS 

    Google Scholar 

  • Hentges, L. D. et al. LanceOtron: a deep learning peak caller for genome sequencing experiments. Bioinformatics 38, 4255–4263 (2022).

    Article 
    MATH 

    Google Scholar 

  • Scott, A. K., Rafuse, M. & Neu, C. P. Mechanically induced alterations in chromatin architecture guide the balance between cell plasticity and mechanical memory. Front Cell Dev. Biol. 11, 1084759 (2023).

    Article 

    Google Scholar 

  • Sommer, C. & Gerlich, D. W. Machine learning in cell biology-teaching computers to recognize phenotypes. J. Cell Sci. 126, 5529–5539 (2013).

    MATH 

    Google Scholar 

  • Allam, M. et al. Spatially variant immune infiltration scoring in human cancer tissues. npj Precis. Oncol. 6, 1–21 (2022).

    MATH 

    Google Scholar 

  • Bonnevie, E. D. et al. Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks. Sci. Rep. 11, 5950 (2021).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Elosegui-Artola, A. et al. Mechanical regulation of a molecular clutch defines force transmission and transduction in response to matrix rigidity. Nat. Cell Biol. 18, 540–548 (2016).

  • Challa, K. et al. Imaging and AI based chromatin biomarkers for diagnosis and therapy evaluation from liquid biopsies. npj Precis. Oncol. 7, 1–13 (2023).

    MATH 

    Google Scholar 

  • Duran, I. et al. Detection of senescence using machine learning algorithms based on nuclear features. Nat. Commun. 15, 1041 (2024).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016).

    Article 
    MATH 

    Google Scholar 

  • Sarkans, U. et al. The BioStudies database—one stop shop for all data supporting a life sciences study. Nucleic Acids Res. 46, D1266–D1270 (2018).

    Article 
    MATH 

    Google Scholar 

  • Williams, E. et al. Image Data Resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).

    Article 
    MATH 

    Google Scholar 

  • Ouyang, W. et al. ShareLoc — an open platform for sharing localization microscopy data. Nat. Methods 19, 1331–1333 (2022).

    Article 
    MATH 

    Google Scholar 

  • Abercrombie, M., Heaysman, J. E. M. & Pegrum, S. M. The locomotion of fibroblasts in culture. IV. Electron microscopy of the leading lamella. Exp. Cell Res. 67, 359–367 (1971).

    Article 

    Google Scholar 

  • Májovský, M., Černý, M., Kasal, M., Komarc, M. & Netuka, D. Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora’s Box Has Been Opened. J. Med. Internet Res. 25, e46924 (2023).

    Article 

    Google Scholar 

  • Heyndels, S. Technology and Neutrality. Philos. Technol. 36, 1–22 (2023).

    Article 

    Google Scholar 

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