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Deep learning-based design and experimental validation of a medicine-like human antibody library.
Brief Bioinform
November 2024
Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
View Article and Find Full Text PDFNext-gen agriculture: integrating AI and XAI for precision crop yield predictions.
Front Plant Sci
January 2025
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, India.
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors.
View Article and Find Full Text PDFNovel clinical trial designs emerging from the molecular reclassification of cancer.
CA Cancer J Clin
January 2025
Medical College of Wisconsin Cancer Center, Milwaukee, Wisconsin, USA.
Next-generation sequencing has revealed the disruptive reality that advanced/metastatic cancers have complex and individually distinct genomic landscapes, necessitating a rethinking of treatment strategies and clinical trial designs. Indeed, the molecular reclassification of cancer suggests that it is the molecular underpinnings of the disease, rather than the tissue of origin, that mostly drives outcomes. Consequently, oncology clinical trials have evolved from standard phase 1, 2, and 3 tissue-specific studies; to tissue-specific, biomarker-driven trials; to tissue-agnostic trials untethered from histology (all drug-centered designs); and, ultimately, to patient-centered, N-of-1 precision medicine studies in which each patient receives a personalized, biomarker-matched therapy/combination of drugs.
View Article and Find Full Text PDFFinding a constrained number of predictor phenotypes for multiple outcome prediction.
BMJ Health Care Inform
January 2025
Johnson & Johnson LLC, Raritan, New Jersey, USA.
Background: Prognostic models help aid medical decision-making. Various prognostic models are available via websites such as MDCalc, but these models typically predict one outcome, for example, stroke risk. Each model requires individual predictors, for example, age, lab results and comorbidities.
View Article and Find Full Text PDFExplainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent.
PLoS One
January 2025
Faculty of Economics and Business (ICADE), Universidad Pontificia Comillas, Madrid, Spain.
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological sector or cryptocurrencies. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management (PM) is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches.
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