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Meta-learning for transformer-based prediction of potent compounds

SCIENTIFIC REPORTS 2023年第1期13卷 1-13页

作者： Chen, Hengwei Bajorath, Juergen Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intelli Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med ChemB IT Friedrich Hirzebruch Allee 5-6 Bonn D-53115 Germany

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

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Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz's Theory of Basic Values

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JMIR MENTAL HEALTH 2024年第1期11卷 e55988页

作者： Hadar-Shoval, Dorit Asraf, Kfir Mizrachi, Yonathan Haber, Yuval Elyoseph, Zohar Max Stern Yezreel Valley Coll Psychol Dept Tel Adashim Israel Max Stern Yezreel Valley Coll Jane Goodall Inst Tel Adashim Israel Tel Aviv Univ Lab AI Machine Learning Business & Data Analyt Tel Aviv Israel Bar Ilan Univ PhD Program Hermeneut & Cultural Studies Interdisciplinary Studies Unit Ramat Gan Israel Max Stern Yezreel Valley Coll Ctr Psychobiol Res Psychol Dept Tel Adashim Israel Imperial Coll London Fac Med Dept Brain Sci Fulham Palace Rd London W6 8RF England

Background: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision -making have ethical importance. Schwartz's theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist -client dynamics. Objective: This study aimed to (1) evaluate whether the STBV can measure value -like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value -like patterns from humans and each other. Methods: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire-Revised (PVQ-RR) to assess value -like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs' value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. Results: The PVQ-RR showed good reliability and validity for quantifying value -like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs' value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self -direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs' distinct value profiles. Further examination found the biased value

关键词： large language models LLMs large language model LLM machine learning ML natural language processing NLP deep learning ChatGPT Chat-GPT chatbot chatbots chat-bot chat-bots Claude values Bard artificial intelligence AI algorithm algorithms predictive model predictive models predictive analytics predictive system practical model practical models mental health mental illness mental illnesses mental disease mental diseases mental disorder mental disorders mobile health mHealth eHealth mood disorder mood disorders

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Proteomic Differences in Colonic Epithelial Cells in Ulcerative Colitis Have an Epigenetic Basis

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GASTRO HEP ADVANCES 2024年第6期3卷 830-841页

作者： Jelinsky, Scott Lee, Isac Monetti, Mara Breitkopf, Susanne Martz, Flora Kongala, Ramya Culver, Jeffrey Vo, Vanessa Xue, Liang Gieseck III, Richard Dickinson, Caitlyn Kasaian, Marion Lord, James D. Pfizer Dept Inflammat & Immunol 1 Portland St Cambridge MA 02139 USA Pfizer Internal Med Res Unit Cambridge MA USA Benaroya Res Inst Translat Res Program Seattle WA USA Pfizer Machine Learning & Computat Sci Early Clin Dev Cambridge MA USA Mem Sloan Kettering Canc Ctr Prote Core New York NY USA Kymera Therapeut Watertown MA USA Univ Washington Dept Gen Surg Seattle WA USA Novartis Inst Biomed Res Cambridge MA USA

BACKGROUND AND AIMS: The colonic epithelium serves as both a barrier to lumenal contents and a gatekeeper of infl ammatory responses. In ulcerative colitis (UC), epithelial dysfunction is a core feature, but little is known about the cellular changes that may underlie disease pathology. We therefore evaluated how the chromatin epigenetics and proteome of epithelial cells differs between health and UC. METHODS: We sorted live CD326+ epithelial cells from colon biopsies of healthy control (HC) screening colonoscopy recipients and from inflamed or uninflamed colon segments of UC patients on no biologic nor immunomodulator therapy (n = 5-7 subjects per group). Cell lysates were analyzed by proteomic evaluation and nuclei were analyzed for open chromatin with assay for transposase-accessible chromatin using sequencing. RESULTS: Proteins most highly elevated in inflamed UC biopsies relative to HC were those encoded by the HLA-DRA (P = 3.1 x 10-33) and CD74 (P = 1.6 x 10-27), genes associated with antigen presentation, and the antimicrobial dual oxidase 2 (DUOX2) (P = 3.2 x 10-28) and lipocalin-2 (P = 2.2 x 10-26) genes. Conversely, the water channel aquaporin 8 was strikingly less common with inflammation (P = 1.9 x 10-18). Assay for transposase-accessible chromatin using sequencing revealed more open chromatin around the aquaporin 8 gene in HCs (P = 2.0 x 10-2) and more around the DUOX2/DUOXA2 locus in inflamed UC colon (P = 5.7 x 10-4), suggesting an epigenetic basis for differential protein expression by epithelial cells in health and disease. CONCLUSION: Numerous differences exist between the proteome and chromatin of colonic epithelial cells in UC patients and HCs, some of which correlate to suggest specific epigenetic mechanisms regulating the epithelial proteome.

关键词： Proteomics ATAC-Seq DUOX2 AQP8

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PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web

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DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2022年第2022期2022卷 baac042-baac042页

作者： Le, Tuan-Dung Nguyen, Phuong D. Korkin, Dmitry Thieu, Thanh Oklahoma State Univ Dept Comp Sci Stillwater OK 74078 USA Oklahoma State Univ Dept Biochem & Mol Biol Stillwater OK 74078 USA Worcester Polytech Inst Dept Comp Sci Worcester MA 01609 USA Worcester Polytech Inst Bioinformat & Computat Biol Program Worcester MA 01609 USA H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL 33612 USA

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research *** COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface;scientists can also download the entire database in bulk for offline processing.

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Empowering Optimal Control with machine learning: A Perspective from Model Predictive Control 25th

Empowering Optimal Control with Machine Learning: A Perspect...

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25th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Weinan, E. Han, Jiequn Long, Jihao AI Sci Inst Beijing Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China Peking Univ Sch Math Sci Beijing Peoples R China Flatiron Inst Ctr Computat Math New York NY 10010 USA Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms. Copyright (C) 2022 The Authors.

关键词： machine learning Optimal Control Model Predictive Control

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Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

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BUILDING AND ENVIRONMENT 2022年 219卷

作者： Zaidan, Esmat Abulibdeh, Ammar Alban, Ahmad Jabbar, Rateb Qatar Univ Coll Arts & Sci Dept Int Affairs Policy Planning & Dev Program Doha Qatar Qatar Univ Coll Arts & Sci Dept Humanities Appl Geog & GIS Program POB 2713 Doha Qatar Qatar Univ Coll Engn Machine Learning Doha Qatar

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing humanbuilding interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population.

关键词： Human -building interactions Preference Motivation Socioeconomic characteristics Building features Energy consumption

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

arXiv

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arXiv 2024年

作者： Xie, Pinchen Chen, Yixiao Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Materials Institute Princeton University PrincetonNJ08544 United States

We report an extensive 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 upon crossing the transition temperature. We find that the macroscopic polarization appears/disappears due to dipolar switching at the nanometer scale. We also compute the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. For a long time, 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. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation due to the 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. © 2024, CC BY.

关键词： Ferroelectricity

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A Sensitivity and Performance Analysis of Word2Vec Applied to Emotion State Classification Using a Deep Neural Architecture 16th

A Sensitivity and Performance Analysis of Word2Vec Applied t...

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16th International Symposium on Distributed Computing and Artificial Intelligence (DCAI)

作者： Pasti, Rodrigo Vilasboas, Fabricio G. Roque, Isabela R. de Castro, Leandro N. Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab LCoN Grad Program Elect Engn & Comp PPGEEC Sao Paulo Brazil

ISBN: (纸本)9783030238872;9783030238865

Word2Vec has become one of the most relevant neural networks to generate word embeddings for NLP applications. Despite that, little has been investigated in terms of its sensitivity to the word vectors' length (n) and the window size (w). Thus, the present paper performs a sensitivity analysis of Word2Vec when applied to generate word embeddings for a deep neural architecture used to classify emotion states in tweets. Furthermore, we present a computational performance analysis to investigate how the system scales as a function of n and w in different computing environments. The results show that a window size of approximately half the tweet length (8 words) and a value of n = 50 suffices to find good performances. Also, by increasing these values one may unnecessarily increase the computational cost.

关键词： Embeddings

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A Developer Recommendation Method Based on Code Quality

A Developer Recommendation Method Based on Code Quality

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： da Silva, Matheus Camilo Janino Cizotto, Andre Armstrong Paraiso, Emerson Cabrera Pontificia Univ Catolica Parana Grad Program Informat Curitiba Parana Brazil Siemens Ltd Machine Learning Comm Curitiba Parana Brazil

ISBN: (纸本)9781728169262

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer's profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.

关键词： Software quality clustering developer recommendation developer profile

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Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models 10th

Diagnosis of Schizophrenia in EEG Signals Using dDTF Effecti...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Shoeibi, Afshin Khodatars, Marjane Alinejad-Rorky, Hamid Heras, Jonathan Bagherzadeh, Sara Beheshti, Amin Gorriz, Juan M. Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW Australia UNSW Sydney UNSW BioMed Machine Learning Lab Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ La Rioja Dept Math & Comp Sci La Rioja Spain Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Macquarie Univ Sch Comp Data Sci Lab Sydney NSW 2109 Australia

ISBN: (纸本)9783031611391;9783031611407

Schizophrenia (SZ) is a multifaceted mental disorder that typically emerges in early adulthood, characterized by a spectrum of physiological and cognitive deficits. Electroencephalography (EEG) recordings are pivotal in SZ diagnosis, necessitating the expertise of specialist doctors and psychologists. However, the analysis of EEG signals is labor-intensive and susceptible to human error. This study introduces a deep learning (DL) pipeline for the early detection of SZ using EEG signals. The pipeline includes stages of dataset selection, preprocessing, feature extraction, and classification. For this study, the RepOD dataset, consisting of EEG recordings from 14 subjects with SZ and healthy controls (HC), was utilized. The preprocessing phase involves normalizing and segmenting the EEG data. Subsequently, the EEG signals are divided into various sub-bands via Discrete Wavelet Transform (DWT), and effective connectivity matrices are derived using the directed Directed Transfer Function (dDTF) technique. Following this, state-of-the-art pretrained DL models based on CNNs and transformers are applied to extract features and classify the 2D dDTF images obtained from different EEG sub-bands. Notably, the ConvNext-Tiny architecture demonstrated superior performance, achieving an accuracy of 96% in the beta sub-band. Furthermore, this model surpassed the performance of other DL models in terms of accuracy across additional EEG sub-bands.

关键词： Schizophrenia EEG Detection DWT dDTF Transformers

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