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Influence of Data Curation and Confidence Levels on Compound Predictions Using machine learning Models

JOURNAL OF CHEMICAL INFORMATION AND MODELING 2024年第24期64卷 9341-9349页

作者： Xerxa, Elena Vogt, Martin Bajorath, Juergen Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci B IT D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intellig D-53115 Bonn Germany Rhein Friedrich Wilhelms Univ Limes Inst Program Unit Chem Biol & Med Chem D-53115 Bonn Germany

While data curation principles and practices are a major topic in data science, they are often not explicitly considered in machine learning (ML) applications in chemistry. We have been interested in evaluating the potential effects of data curation on the performance of molecular ML models. Therefore, a sequential curation scheme was developed for compounds and activity data, and different ML classification models were generated at increasing data confidence levels and evaluated. Sequential data curation was found to systematically increase classification performance in an incremental manner due to cumulative effects of individual data curation criteria. The analysis of chemical space distributions of compound subsets at different data confidence levels revealed that the separation of compounds with different class labels in chemical space generally increased during sequential activity data curation, which was mostly due to subsequent elimination of singletons rather than compounds from analogue series. These findings provided a rationale for increasing the classification performance of ML models as a consequence of increasingly stringent data curation. Taken together, the results reported herein suggest that further attention should be paid to varying data curation and confidence levels when deriving and assessing ML models for chemical applications.

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Causal machine learning for single-cell genomics

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NATURE GENETICS 2025年第4期57卷 797-808页

作者： Tejada-Lapuerta, Alejandro Bertin, Paul Bauer, Stefan Aliee, Hananeh Bengio, Yoshua Theis, Fabian J. Helmholtz Munich Inst Computat Biol Munich Germany Tech Univ Munich Sch Comp Informat & Technol Munich Germany Mila Quebec AI Inst Montreal PQ Canada Univ Montreal Montreal PQ Canada Helmholtz Munich Munich Germany Munich Ctr Machine Learning MCML Munich Germany Wellcome Sanger Inst Hinxton England Canadian Inst Adv Res Learning Machines & Brains Program Toronto ON Canada Tech Univ Munich TUM Sch Life Sci Weihenstephan Munich Germany

Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes. In this Perspective, we delineate the application of causal machine learning to single-cell genomics and its associated challenges. We first present the causal model that is most commonly applied to single-cell biology and then identify and discuss potential approaches to three open problems: the lack of generalization of models to novel experimental conditions, the complexity of interpreting learned models, and the difficulty of learning cell dynamics.

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Finite-time thermodynamics: Multistage separation processes consuming mechanical energy

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CHEMICAL ENGINEERING SCIENCE 2022年第PartB期248卷 117250-117250页

作者： Sukin, Ivan Tsirlin, Anatoly Andresen, Bjarne Financial Univ Govt Russian Federat Dept Data Anal & Machine Learning Moscow Russia Russian Acad Sci Complex Syst Dept Ailamazyan Program Syst Inst Yaroslavl Oblast Russia Univ Copenhagen Niels Bohr Inst Copenhagen Denmark

We analyze limiting capabilities of mechanical separation processes using finite-time thermodynamics and obtain estimates for the lower bound of the energy consumption for systems of a given productivity. We show that this consumption does not tend to zero when the molar fraction of one of the components tends to unity. The estimates obtained are used to analyze multistage separation processes, containing recycles, especially, isotope separation systems. For such systems we obtain relations between fluxes, mass transfer surfaces, and stage number. These relations are deduced from the condition of minimum dissipation, assuming that the enrichment factor is constant. We also obtain the optimality condition for the sequence of separations needed for a multicomponent mixture in a mechanical separation systems. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Irreversible separation processes Mechanical systems Reachable set Multistage systems Isotope separation Surface distribution Entropy generation Separation sequence

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HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

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GENOME BIOLOGY 2024年第1期25卷 1-28页

作者： Myers, Matthew A. Arnold, Brian J. Bansal, Vineet Balaban, Metin Mullen, Katelyn M. Zaccaria, Simone Raphael, Benjamin J. Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ USA Princeton Univ Princeton Res Comp Princeton NJ USA UCL Canc Inst Computat Canc Genom Res Grp London England Mem Sloan Kettering Canc Ctr Human Oncol & Pathogenesis Program New York NY USA

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

关键词： Copy-number aberrations Cancer Genomics Tumor heterogeneity Clone Allele-specific Haplotype DNA sequencing

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Milestones in chemoinformatics: global view of the field

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JOURNAL OF CHEMINFORMATICS 2024年第1期16卷 124页

作者： Bajorath, Jurgen Univ Bonn Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich-Hirzebruch-Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

Over the past similar to 25 years, chemoinformatics has evolved as a scientific discipline, with a strong foundation in pharmaceutical research and scientific roots that can be traced back to the late 1950s. It covers a wide methodological spectrum and is perhaps best positioned in the greater context of chemical information science. Herein, the chemoinformatics discipline is delineated, characteristic (and partly problematic) features are discussed, and a global view of the field is provided, emphasizing key developments.

关键词： Chemoinformatics Drug discovery Information science Chemical data Molecular design

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Self-Supervised Vessel Detection from Low Resolution Satellite Imagery

Self-Supervised Vessel Detection from Low Resolution Satelli...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Farr, A. Petrunin, I Kakareko, G. Cappaert, J. Cranfield Univ Sch Aerosp Transport & Mfg Cranfield Central Bedford England Spire Global Inc Machine Learning Boulder CO USA Spire Global Inc Space Program Luxembourg Luxembourg

ISBN: (纸本)9781624106316

Maritime surveillance is a growing field of application for optical imaging satellites. In this paper, we aim to increase the practical effectiveness of vessel detection algorithms by developing a computationally inexpensive method that can discern vessels in low resolution optical satellite imagery. This creates an opportunity to host the algorithm on-board small, low-cost nanosatellites, generating feasibility for large surveillance constellations. The presented algorithm, which is based upon a deep convolutional autoencoder utilized as an anomaly detector, was found to achieve a detection precision of 98%, and a recall of 79%. Data used to test and train the algorithm was produced by augmenting ESA Sentinel-2 imagery, reducing the resolution to mimic that of a nanosatellite's. The results presented here are compared with comparable papers in the field, where it is demonstrated that the proposed algorithm is capable of outperforming classical techniques in terms of detection precision. For an equivalent resolution, the proposed algorithm provides a 34% increase in precision over the Constant False Alarm Rate (CFAR) technique.

关键词： Nanosatellites

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Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate

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Physical Review B 2025年第9期111卷 094113-094113页

作者： Pinchen Xie Yixiao Chen Weinan E Roberto Car Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA AI for Science Institute Beijing China and Center for Machine Learning Research and School of Mathematical Sciences Peking University Beijing China

We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases.

关键词： Ferroelectricity

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From scientific theory to duality of predictive artificial intelligence models

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CELL REPORTS PHYSICAL SCIENCE 2025年第4期6卷

作者： Bajorath, Jurgen Univ Bonn Dept Life Sci Informat & Data Sci B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artificial Intellig Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Limes Inst Program Unit Chem Biol & Med Chem Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany

In studies employing explainable artificial intelligence (XAI), model explanation, interpretation, and causality are often not clearly distinguished, leading to potential misunderstandings of model performance or relevance. For predictive AI models used in the natural sciences, the path leading from model explanation and interpretation to causal reasoning is of particular importance because it bridges theory and hypothesisdriven experimental design. Selected concepts from scientific theory can be taken into consideration to generate a conceptual framework for putting predictions into scientific perspective and recognizing potential caveats. For explainable models, it is argued that the scientific rationale underlying model derivation plays a decisive role in assessing and understanding predictions and exploring causal relationships, giving rise to the notion of model duality, as introduced herein.

关键词： explainable artificial intelligence machine learning science theory model explanation vs. interpretation correlation vs. causation hypothesis-driven experimental design scientific rationale model duality

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Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why

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JOURNAL OF CHEMICAL THEORY AND COMPUTATION 2024年第18期20卷 7708-7715页

作者： Xie, Pinchen Weinan, E. Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA Lawrence Berkeley Natl Lab Appl Math & Computat Res Div Berkeley CA 94720 USA AI Sci Inst Beijing 100080 Peoples R China Peking Univ Ctr Machine Learning Res Beijing 100871 Peoples R China Peking Univ Sch Math Sci Beijing 100084 Peoples R China

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term DC. Case studies of a toy polymer, with 20 CG sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling CG conformational dynamics in practice.

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A Neuroevolutionary Model to Estimate the Tensile Strength of Manufactured Parts Made by 3D Printing

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ALGORITHMS 2022年第8期15卷 263-263页

作者： da Silva, Matheus Alencar Junior, Bonfim Amaro Brito Medeiros, Ramon Ruda Pinheiro, Placido Rogerio Univ Fed Ceara Undergrad Mech Engn Program BR-62900000 Russas Brazil Univ Fed Ceara Nucleo Estudo Machine Learning & Otimizacao NEMO BR-62900000 Russas Brazil Fed Univ Paraiba UFPB Grad Program Mech Engn BR-58051900 Joao Pessoa Paraiba Brazil Univ Fortaleza UNIFOR Grad Program Appl Informat BR-60811905 Fortaleza Ceara Brazil

Three-dimensional printing has advantages, such as an excellent flexibility in producing parts from the digital model, enabling the fabrication of different geometries that are both simple or complex, using low-cost materials and generating little residue. Many technologies have gained space, highlighting the artificial intelligence (AI), which has several applications in different areas of knowledge and can be defined as any technology that allows a system to demonstrate human intelligence. In this context, machine learning uses artificial intelligence to develop computational techniques, aiming to build knowledge automatically. This system is responsible for making decisions based on experiences accumulated through successful solutions. Thus, this work aims to develop a neuroevolutionary model using artificial intelligence techniques, specifically neural networks and genetic algorithms, to predict the tensile strength in materials manufactured by fused filament fabrication (FFF)-type 3D printing. We consider the collection and construction of a database on three-dimensional instances to reach our objective. To train our model, we adopted some parameters. The model algorithm was developed in the Python programming language. After analyzing the data and graphics generated by the execution of the tests, we present that the model outperformed, with a determination coefficient superior to 90%, resulting in a high rate of assertiveness.

关键词： fused deposition modeling 3D printing artificial intelligence

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