Data Structures for Statistical Computing in Python

Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms

Bogue-Jimenez, B., Huang, X., Powell, D., & Doblas, A. (2022) · Sensors · doi:10.3390/s22093534 · Cited by 24

Integrating Phylogenetics With Intron Positions Illuminates the Origin of the Complex Spliceosome

Vosseberg, J., Stolker, D., von der Dunk, S. H. A., & Snel, B. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad011 · Cited by 24

Adaptive evolution of a minimal organism with a synthetic genome

Sandberg, T. E., Wise, K. S., Dalldorf, C., Szubin, R., Feist, A. M., Glass, J. I., & Palsson, B. O. (2023) · iScience · doi:10.1016/j.isci.2023.107500 · Cited by 23

Exploratory Data Analysis of Cell and Mitochondrial High-Fat, High-Sugar Toxicity on Human HepG2 Cells

Amorim, R., Simões, I., Veloso, C., Carvalho, A., Simões, R., Pereira, F., Thiel, T., Normann, A., Morais, C., Jurado, A., et al. (2021) · Nutrients · doi:10.3390/nu13051723 · Cited by 21

Plasticity of the binding pocket in peptide transporters underpins promiscuous substrate recognition

Kotov, V., Killer, M., Jungnickel, K. E. J., Lei, J., Finocchio, G., Steinke, J., Bartels, K., Strauss, J., Dupeux, F., Humm, A.-S., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112831 · Cited by 20

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

Wojciechowski, J. W., Tekoglu, E., Gąsior-Głogowska, M., Coustou, V., Szulc, N., Szefczyk, M., Kopaczyńska, M., Saupe, S. J., & Dyrka, W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010787 · Cited by 19

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2016) · doi:10.1101/043794 · Cited by 18

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Gomes, D. E. B., Yang, B., Vanella, R., Nash, M. A., & Bernardi, R. C. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c05869 · Cited by 18

A control theoretic approach to evaluate and inform ecological momentary interventions

Fechtelpeter, J., Rauschenberg, C., Jamalabadi, H., Boecking, B., van Amelsvoort, T., Reininghaus, U., Durstewitz, D., & Koppe, G. (2023) · doi:10.31234/osf.io/97teh · Cited by 16

KIDINS220 and InsP8 safeguard the stepwise regulation of phosphate exporter XPR1

(2025) · doi:10.1016/j.molcel.2025.08.003 · Cited by 17

Deep learning improves utility of tau PET in the study of Alzheimer's disease

Zou, J., Park, D., Johnson, A., Feng, X., Pardo, M., France, J., Tomljanovic, Z., Brickman, A. M., Devanand, D. P., Luchsinger, J. A., et al. (2021) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12264 · Cited by 15

Machine learning for accurate methane concentration predictions: short-term training, long-term results

Luo, R., Wang, J., & Gates, I. (2023) · Environmental Research Communications · doi:10.1088/2515-7620/acf0a3 · Cited by 14

How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers

Kratzke, N. (2023) · Computers · doi:10.3390/computers12030057 · Cited by 13

To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry

Abdul-Khalek, N., Picciani, M., Shouman, O., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00973 · Cited by 13

Microbiota profile and efficacy of probiotic supplementation on laxation in adults affected by Prader‐Willi Syndrome: A randomized, double‐blind, crossover trial

Alyousif, Z., Miller, J. L., Auger, J., Sandoval, M., Piano, A., Tompkins, T. A., & Dahl, W. J. (2020) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.1535 · Cited by 11

Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics

Gavalda-Garcia, J., Dixit, B., Díaz, A., Ghysels, A., & Vranken, W. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168900 · Cited by 12

Investigating interoperability of the LSST data management software stack with Astropy

Jenness, T., Bosch, J., Owen, R., Parejko, J., Sick, J., Swinbank, J., de Val-Borro, M., Dubois-Felsmann, G., Lim, K.-T., Lupton, R. H., et al. (2016) · SPIE Proceedings · doi:10.1117/12.2231313 · Cited by 14

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

(2025) · doi:10.1091/mbc.e24-10-0486 · Cited by 11

Evaluation of a retrieval-augmented generation-powered chatbot for pre-CT informed consent: a prospective comparative study

Busch, F., Kaibel, L., Nguyen, H., Lemke, T., Ziegelmayer, S., Graf, M., Marka, A. W., Endrös, L., Prucker, P., Spitzl, D., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01483-w · Cited by 10

Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects

Pendyala, S., Partington, K., Bradley, N., McEwen, A. E., Straub, G., Kim, H.-J., Fayer, S., Holmes, D. L., Sitko, K. A., Garge, R. K., et al. (2026) · Cell · doi:10.1016/j.cell.2026.04.031 · Cited by 10

The Structure and Stability of Extended, Inclined Circumplanetary Disc or Ring Systems

Speedie, J., & Zanazzi, J. J. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2068 · Cited by 10

Domainator, a flexible software suite for domain-based annotation and neighborhood analysis, identifies proteins involved in antiviral systems

Johnson, S. R., Weigele, P. R., Fomenkov, A., Ge, A., Vincze, A., Eaglesham, J. B., Roberts, R. J., & Sun, Z. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1175 · Cited by 10

Classification of likely functional class for ligand binding sites identified from fragment screening

Utgés, J. S., MacGowan, S. A., Ives, C. M., & Barton, G. J. (2024) · Communications Biology · doi:10.1038/s42003-024-05970-8 · Cited by 10

Genome-directed study reveals the diversity of Salmonella T6SS effectors and identifies a novel family of lipid-targeting antibacterial toxins

Nicastro, G. G., Sibinelli-Sousa, S., Hespanhol, J. T., Santos, T. W. C., Munoz, J. P., Santos, R. S., Perez-Sepulveda, B. M., Miyamoto, S., Aravind, L., de Souza, R. F., et al. (2024) · doi:10.1101/2024.09.27.615498 · Cited by 9

An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties

Ferraz, M. V. F., Neto, J. C. S., Lins, R. D., & Teixeira, E. S. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05644e · Cited by 9

Systematic identification of Salmonella T6SS effectors uncovers diverse new families and lipid-targeting activities

Nicastro, G. G., Sibinelli-Sousa, S., Hespanhol, J. T., Santos, T. W. C., Munoz, J. P., Santos, R. S., Perez-Sepulveda, B. M., Miyamoto, S., Aravind, L., de Souza, R. F., et al. (2026) · PLOS Biology · doi:10.1371/journal.pbio.3003680 · Cited by 9

Detecting fabrication in large-scale molecular omics data

Bradshaw, M. S., & Payne, S. H. (2021) · PLOS ONE · doi:10.1371/journal.pone.0260395 · Cited by 8

Machine learning methods applied to audit of surgical margins after curative surgery for facial (non-melanoma) skin cancer

Tighe, D., Tekeli, K., Gouk, T., Smith, J., Ho, M., Moody, A., Walsh, S., Provost, S., & Freitas, A. (2023) · British Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.bjoms.2022.11.280 · Cited by 8

Neural Network-Driven Exploration in Polymerization-Induced Self-Assembly: From 2D to 3D Pseudo-Phase Diagram

Fonseca Parra, E. P., Oumerri, J., Arteni, A. A., Six, J.-L., Armes, S. P., & Ferji, K. (2025) · Macromolecules · doi:10.1021/acs.macromol.4c01772 · Cited by 8

Analysis-ready VCF at Biobank scale using Zarr

(2025) · doi:10.1093/gigascience/giaf049 · Cited by 8

Spatially Resolved Recent Star Formation History in NGC 6946

Tran, D., Williams, B., Levesque, E., Lazzarini, M., Dalcanton, J., Dolphin, A., Koplitz, B., Smercina, A., & Telford, O. G. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/aced44 · Cited by 7

Measurement Integrity in Peer Prediction: A Peer Assessment Case Study

Burrell, N., & Schoenebeck, G. (2021) · arXiv · doi:10.48550/arxiv.2108.05521 · Cited by 7

System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method

Kim, T. J., Suh, J., Park, S.-H., Kim, Y., & Ko, S.-B. (2025) · Neurocritical Care · doi:10.1007/s12028-025-02222-3 · Cited by 7

Optimizing Water, Temperature, and Density Conditions for In Vitro Pea (Pisum sativum L.) Germination

Kende, Z., Piroska, P., Szemők, G. E., Khaeim, H., Sghaier, A. H., Gyuricza, C., & Tarnawa, Á. (2024) · Plants · doi:10.3390/plants13192776 · Cited by 6

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Degn, K., Beltrame, L., Tiberti, M., & Papaleo, E. (2023) · doi:10.1101/2023.05.06.539447 · Cited by 6

Regulation of adaptive growth decisions via phosphorylation of the TRAPPII complex in Arabidopsis

(2024) · doi:10.1083/jcb.202311125 · Cited by 6

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Degn, K., Beltrame, L., Tiberti, M., & Papaleo, E. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00884 · Cited by 6

Analysis of various climate change parameters in India using machine learning

Wamanse, R., & Patil, T. (2022) · arXiv · doi:10.48550/arxiv.2201.10123 · Cited by 6

Network integration and protein structural binding analysis of neurodegeneration-related interactome

Chen, H., Zhou, Y., Liu, Y., Zhang, P., & Chen, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad237 · Cited by 5

Selective removal of green pigments and associated proteins from clover-grass protein concentrates: molecular insights into a non-destructive, two-stage membrane-based biorefinery concept for high-quality food protein production

Gregersen Echers, S., Abdul-Khalek, N., Jensen, N. H., Jørgensen, A. K., Mattsson, T., Jørgensen, M. K., Lübeck, P. S., & Lübeck, M. (2026) · Sustainable Food Technology · doi:10.1039/d5fb00771b · Cited by 5

DNA/polysome phase separation and cell width confinement couple nucleoid segregation to cell growth in escherichia coli

Papagiannakis, A., Yu, Q., Govers, S. K., Lin, W.-H., Wingreen, N. S., & Jacobs-Wagner, C. (2025) · doi:10.7554/elife.104276.1 · Cited by 4

Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain

(2023) · doi:10.1261/rna.079552.122 · Cited by 4

Steady-state neuron-predominant LINE-1 encoded ORF1p protein and LINE-1 RNA increase with aging in the mouse and human brain

Bonnifet, T., Sinnassamy, S., Massiani-Beaudoin, O., Mailly, P., Monnet, H., Loew, D., Lombard, B., Servant, N., Joshi, R. L., & Fuchs, J. (2025) · eLife · doi:10.7554/elife.100687 · Cited by 4

User Sentiments and Dynamics in the Decentralized Web: Reddit Migration’s Impact on Lemmy

Nunes, T. A. (2023) · Journal of Multimedia Information System · doi:10.33851/jmis.2023.10.4.333 · Cited by 3

Accurate detection of convergent mutations in large protein alignments with ConDor

Morel, M., Zhukova, A., Lemoine, F., & Gascuel, O. (2024) · Genome Biology and Evolution · doi:10.1093/gbe/evae040 · Cited by 3

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories

(2025) · doi:10.1016/j.cels.2025.101265 · Cited by 3

The N-terminal order-disorder transition is a critical determinant for a metamorphosis of IscU

Na, J., Heo, J., Jeong, M., Kim, B. S., Ji, S., Ko, Y. H., Shafiei, A., Baldir, N., DeMirci, H., Yu, W., et al. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.146969 · Cited by 3

Predicting animal movement with deepSSF : A deep learning step selection framework

Forrest, S. W., Pagendam, D., Hassan, C., Potts, J. R., Drovandi, C., Bode, M., & Hoskins, A. J. (2026) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70136 · Cited by 3

Enhanced oxidative stress resistance in Ustilago maydis and its implications on the virulence

Cuamatzi-Flores, J., Colón-González, M., Requena-Romo, F., Quiñones-Galeana, S., Cervantes-Chávez, J. A., & Morales, L. (2024) · International Microbiology · doi:10.1007/s10123-024-00489-8 · Cited by 3

AI-assisted tumor board decision-making in pancreatic oncology

Mergen, M., Busch, F., Schwarberg, B., Koldeweihe, P., Jungwirth, D., Sydlik, J., Maurer, H. C., Makowski, M. R., Spitzl, D., & Gassert, F. T. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03444-x · Cited by 3

Crystal structure of FAD‐independent methylene‐tetrahydrofolate reductase from Mycobacterium hassiacum

(2023) · doi:10.1002/prot.26504 · Cited by 2

Agentic AI framework for accelerated catalyst design in biomass valorization via small language models and automated machine learning

Liu, A., Kim, G., & Song, H. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.175701 · Cited by 1

Does Few-Shot Learning Help LLM Performance in Code Synthesis?

Xu, D., Xie, T., Xia, B., Li, H., Bai, Y., Sun, Y., & Wang, W. (2024) · arXiv · doi:10.48550/arxiv.2412.02906 · Cited by 3

A Meta-Analysis of Spearman’s Hypothesis in the United States

Woodley of Menie, M., Peñaherrera-Aguirre, M., & Sarraf, M. (2026) · Intelligence & Cognitive Abilities · doi:10.65550/001c.171909

AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning

Gómez, P., Ruhberg, L. E., Nardone, M. T., & O'Ryan, D. (2025) · arXiv · doi:10.48550/arxiv.2505.03509

Mutations within the predicted fragment-binding region of FAM83G/SACK1G abolish its interaction with the Ser/Thr kinase CK1α

(2026) · doi:10.1098/rsob.250230

Residues neighboring an SH3-binding motif participate in the interaction in vivo

Jordan, D. F., Dubé, A. K., Dionne, U., Bradley, D., & Landry, C. R. (2025) · GENETICS · doi:10.1093/genetics/iyaf153