14 papers · ranked by Valyu relevance
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack…
Marcelo Hurtado, Vera Pancaldi
Machine learning approaches are increasingly applied to high-dimensional biological data in which features are often dataset-dependent. In many omics workflows, features are computed using information derived from the entire dataset, such as correlations between variables, clustering structures, or enrichment scores.…
Paulo Lyra, Junhao Qiu, Khai Dang, Alyssa Pybus + 6 more
Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machine learning tools more accessible to biomedical researchers while supporting best-practice approaches…
Alon Itzkovitch, Idan Sulami, Ronny Doron Efroni, Moni Shahar + 1 more
1. Microclimates are critical for understanding how organisms interact with their environments, influencing behaviour, physiology, and species distributions. However, traditional physical heat-balance models for predicting ground temperatures in microhabitats often exhibit biases due to unaccounted environmental…
Muhammad Muneeb, David B. Ascher, YooChan Myung
Identifying disease-associated genes enables the development of precision medicine and the understanding of biological processes. Genome-wide association studies (GWAS), gene expression data, biological pathway analysis, and protein network analysis are among the techniques used to identify causal genes. We propose a…
Muhammad Muneeb, David B. Ascher, YooChan Myung, Samuel F. Feng + 1 more
Genotype-phenotype prediction plays a crucial role in identifying disease-causing single nucleotide polymorphisms and precision medicine. In this manuscript, we benchmark the performance of various machine/deep learning algorithms and polygenic risk score tools on 80 binary phenotypes extracted from the openSNP…
Remy Tuyeras, Alvaro Morcuende, Claudia Llinares, Asa Segerstolpe + 3 more
Functional specialization in continuous systems requires balancing adaptation to environmental stress with the preservation of encoded information. Yet, the physical constraints governing how living systems reconcile selective information retention with energetic structural reorganization remain unclear–a trade-off…
Clémence Bolut, Anastasia Pacary, Laetitia Pieruccioni, Marielle Ousset + 3 more
Machine learning (ML) models are effective at classifying images across various fields, including biology. However, their performance on biomedical images is often limited by the small size of available datasets that are constrained by the time-consuming and costly nature of experimental data collection. A review of…
Yan Gao, Yan Cui
Large-scale clinical and biomedical datasets increasingly contain both diverse subgroup attributes (e.g., demographic or clinical subgroups) and multiple prediction targets. Although various machine learning approaches can address subgroup differences or multi-target prediction, they often consider these aspects…
S. Patel, V. Patel
Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process…
Eli Bixby, Gino Brunner, Daniel Danciu, Richard Dela Rosa + 19 more
Lead optimization remains the longest and most expensive step in pre-clinical drug discovery, typically consuming 12–36 months whilst costing $5M–$15M per candidate. We introduce ‘cradle-1’, an automated framework for protein engineering. While cradle-1 supports the full process of drug discovery and industrial protein…
Sebastian A.F. Mueller, Zuzana Vejlupkova, Molly Megraw, John E. Fowler
The ability to predict phenotypes from genotypes in multicellular organisms remains limited despite rapid advances in genotyping and phenotyping methods. Machine learning offers a promising way to model phenotype from genotype, but requires sizable datasets that quantitatively link phenotype to specific genes. Such…
Dhruva V. Raman, Christopher R. Dunne, Katie Davyson, Timothy O’Leary
Animals inhabit continually changing environments where it is not always possible to infer causes of relevant changes, such as the appearance of a new threat. In such nonstationary settings, learning a predictive model is challenging because a surprising observation could be due to chance, or due to systematic but…
Laura A. Wirth, Nassim Sadedin, Björn Meder, Daniel J. Schad
Pavlovian responding is a core component of behavior and can be measured via Pavlovian-instrumental transfer (PIT), where Pavlovian responses bias instrumental actions. Standard single-lever PIT paradigms, which assess responses using a single-choice option, cannot dissociate the contribution of model-free versus…