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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Al Aradi Nabil Hewahi Big Data Science & Analytics Master Program Collage of Science Univestity of Bahain Sakhir Kingdom of Bahrain Collage of Information Technology Univestity of Bahain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [ 75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock.

关键词： Predictive models Machine learning Companies Neural networks Market research data models Recurrent neural networks

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Antioxidant and Antidiabetic Activities of Melastoma Malabathricum Leaves Extracts

引用

Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2022年第7期49卷 144-153页

作者： Idris, Muddatstsir Purnomo, Adi Setyo Martak, Fahimah Fatmawati, Sri Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia Program of Pharmacy Study Faculty of Mathematics and Natural Sciences University of Lambung Mangkurat A. Yani KM. 35.8 Banjarbaru South Kalimantan70714 Indonesia Agri-Food and Biotechnology Research Center Institut Teknologi Sepuluh Nopember Jalan Arif Rahman Hakim Sukolilo Surabaya East Java60111 Indonesia

Currently, no effective medication is available to treat diabetes despite this disease is a serious health problem. As part of our project to explore Indonesian medicinal plants for antidiabetic agents, this study aimed to investigate the total phenolic and flavonoid contents, antioxidant and antidiabetic properties of Melastoma malabathricum leaves extracts. Spectrophotometric methods were used to determine the total phenolic and flavonoid contents. Antioxidant activity was performed using DPPH, ABTS, and FRAP methods. The in vitro antidiabetic test was conducted through an inhibitory evaluation of α-glucosidase and α-amylase, while STZ-induced diabetic rats were used for in vivo study. The highest value of total phenolic (183.71 ± 0.11 mg GAE/g Extract) was recorded in the methanolic extract and flavonoid (24.10 ± 0.04 mg QE/g Extract) contents were recorded in the EtOAc extract. The methanolic extract has the highest DPPH and ABTS activities with IC50 values of 8.58 ± 0.03 and 4.59 ± 0.03 μg/mL, respectively. It also showed the highest FRAP activity with a 51.15 ± 0.10 µM Fe2+/g. In vitro antidiabetic testing of the methanolic extract of leaves against α-glucosidase and α-amylase was reported for the first time. This novel result showed that the methanolic extract inhibited α-glucosidase and α-amylase with IC50 values of 75.25±1.60 and 52.38±1.32 μg/mL, respectively. A dose of 200 mg/Kg body weight of the methanolic extract reduced rats’ blood glucose rate and serum blood glucose by 51.01% and 37.82%, respectively, after 15 days of treatments. These findings suggested that the methanolic extract of M. malabathricum leaves can be used as a potential source of antioxidant and antidiabetic agents. © 2022 Hunan University. All rights reserved.

关键词： Antioxidants

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Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

arXiv

引用

arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

引用

data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Mawra T. Homeed Zahra S. Mohamed Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.

关键词： Support vector machines Business Bayes methods Hidden Markov models Logistics data models Credit cards

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Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors

arXiv

引用

arXiv 2020年

作者： Cao, Jian Durante, Daniele Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Saudi Arabia Department of Decision Sciences Bocconi Institute for Data Science and Analytics Bocconi University Italy Statistics Program King Abdullah University of Science and Technology Saudi Arabia

Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling the relationship between the observed predictors and the binary responses. A widely-implemented solution is to express the probability parameter via a probit mapping of a Gaussian process indexed by predictors. However, unlike for continuous settings, there is a lack of closed-form results for predictive distributions in binary models with Gaussian process priors. Markov chain Monte Carlo methods and approximation strategies provide common solutions to this problem, but state-of-the-art algorithms are either computationally intractable or inaccurate in moderate-to-high dimensions. In this article, we aim to cover this gap by deriving closed-form expressions for the predictive probabilities in probit Gaussian processes that rely either on cumulative distribution functions of multivariate Gaussians or on functionals of multivariate truncated normals. To evaluate these quantities we develop novel scalable solutions based on tile-low-rank Monte Carlo methods for computing multivariate Gaussian probabilities, and on mean-field variational approximations of multivariate truncated normals. Closed-form expressions for the marginal likelihood and for the posterior distribution of the Gaussian process are also discussed. As shown in simulated and real-world empirical studies, the proposed methods scale to dimensions where state-of-the-art solutions are impractical. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Recent trend of metal promoter role for CO₂ hydrogenation to C₁ and C₂₊ products

引用

South African Journal of Chemical Engineering 2023年 44卷 14-30页

作者： Sholeha, Novia Amalia Holilah, Holilah Bahruji, Hasliza Ayub, Athirah Widiastuti, Nurul Ediati, Ratna Jalil, Aishah Abdul Ulfa, Maria Masruchin, Nanang Nugraha, Reva Edra Prasetyoko, Didik College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Keputih Surabaya Sukolilo 60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Darussalam Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Johor Bahru Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia Skudai 81310 UTM Skudai Johor Johor Bahru Malaysia Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Research Collaboration Center for Biomass and Biorefinery between BRIN and Universitas Padjadjaran Jatinangor 45363 Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur East Java Surabaya 60294 Indonesia

CO2 hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO2 methanation is one of the promising solutions, whi... 详细信息

CO₂ hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO₂ methanation is one of the promising solutions, which only requires reactions at atmospheric pressure while utilizing metal catalysts to overcome kinetic limitations. Metal catalysts can be promoted to alter reducibities, CO₂ adsorption capacities, and H_2[sbnd]CO₂ dissociation potential. The role of metal promoter such as rare earth elements (Ce, Mn, Co, and La) and alkali and alkali earth metal (Li, Na, Ca, and K) will be discussed within the scope of CO₂ methanation. The aspect of catalysts modification towards hydrogen dissociation potential and surface oxygen vacancy will be emphasized to enhance selectivity for methane. Another pathway for CO₂ hydrogenation is via further conversion into longer chain molecules such as olefin and ethanol. The benefit of metal promoter will be discussed in this review on the effect towards promoting C[sbnd]C coupling reaction for producing longer chain alcohol and light olefin. The strategies to develop active catalysts for the coupling reaction of C[sbnd]C will be emphasized with the promoter introduction. For the hydrogenation of CO₂ to longer chain molecules, the main metal catalysts Ni, Pd, Rh, and Co, and their modification with promoter such as Ga, Cu, and alkali metal Na, K will be discussed. A critical analysis of the CO₂ methanation mechanism and further C[sbnd]C reaction to longer chain molecules will be discussed, particularly the effect of metal promoters to stabilize the intermediate and maneuver the catalytic reaction pathway into the desired products. © 2023 The Author(s)

关键词： C1 products C2+ products CO2 Hydrogenation Metal promoter

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Study on structure and morphology of silicon-substituted LiFePO4/C doped material

引用

AIP Conference Proceedings 2023年第1期2604卷

作者： Fahmi Astuti Vera L. Maghfirohtuzzoimah Sahara H. Intifadhah Pelangi Az-Zahra Muhammad R. Ramadhan Mochamad Zainuri Darminto Darminto Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Study Program of Physics Faculty of Mathematics and Natural Sciences Republic of Indonesia Defense University Bogor Indonesia

Elemental doping and carbon coating on the electrode are able to increase the battery’s performance to be a good energy storage. In this case, some studies are necessary to confirm the quality of the cathode material, LiFePO4 on the Li-ion based battery material (LIB). Here we report the study of structure and morphology of LiFePO4 by silicon doping at the P-atom atomic position and carbon coating (henceforth called as LFP-Si) utilizing the X-Ray diffractometer, the Fourier-transform infrared technique, and both the scanning and transmission electron microscopy (SEM & TEM), as a preliminary investigation to know the structure and morphology of LiFePO4 after elemental doping and carbon coating. All characterization results are essential for the improvement of the performance of LiFePO4/C doped material.

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Macrophages direct location-dependent recall of B cell memory to vaccination

引用

Cell 2025年

作者： Dhenni, Rama Hoppé, Alexandra Carey Reynaldi, Arnold Kyaw, Wunna Handoko, Nathalie Tricia Grootveld, Abigail K. Keith, Yuki Honda Bhattacharyya, Nayan Deger Ahel, Holly I. Telfser, Aiden Josiah McCorkindale, Andrew N. Yazar, Seyhan Bui, Christina H.T. Smith, James T. Khoo, Weng Hua Boyd, Mollie Obeid, Solange Milner, Brad Starr, Mitchell Brilot, Fabienne Milogiannakis, Vanessa Akerman, Anouschka Aggarwal, Anupriya Davenport, Miles P. Deenick, Elissa K. Chaffer, Christine L. Croucher, Peter I. Brink, Robert Goldstein, Leonard D. Cromer, Deborah Turville, Stuart G. Kelleher, Anthony D. Venturi, Vanessa Munier, C. Mee Ling Phan, Tri Giang Precision Immunology Program Garvan Institute of Medical Research Sydney NSW Australia St. Vincent's Healthcare Clinical Campus School of Clinical Medicine Faculty of Medicine and Health UNSW Sydney Kensington Sydney NSW Australia Immunovirology and Pathogenesis Program Kirby Institute UNSW Sydney Sydney NSW Australia Infection Analytics Program Kirby Institute UNSW Sydney Sydney NSW Australia Data Science Platform Garvan Institute of Medical Research Sydney 2010 NSW Australia St. Vincent's Hospital Sydney Sydney NSW Australia Cancer Plasticity and Dormancy Program The Kinghorn Cancer Centre Garvan Institute of Medical Research Sydney 2010 NSW Australia St. Vincent's Centre for Applied Medical Research Sydney NSW Australia Immune Biotherapies Program Garvan Institute of Medical Research Sydney 2010 NSW Australia Brain Autoimmunity Group Kids Neuroscience Centre The Children's Hospital at Westmead Faculty of Medicine and Health School of Medical Sciences Sydney NSW Australia School of Medical Science Faculty of Medicine and Health University of Sydney Sydney NSW Australia

Vaccines generate long-lived plasma cells and memory B cells (Bmems) that may re-enter secondary germinal centers (GCs) to further mutate their B cell receptor upon boosting and re-exposure to antigen. We show in mouse models that lymph nodes draining the site of primary vaccination harbor a subset of Bmems that reside in the subcapsular niche, generate larger recall responses, and are more likely to re-enter GCs compared with circulating Bmems in non-draining lymph nodes. This location-dependent recall of Bmems into the GC in the draining lymph node was dependent on CD169⁺ subcapsular sinus macrophages (SSMs) in the subcapsular niche. In human participants, boosting of the BNT162b2 vaccine in the same arm generated more rapid secretion of broadly neutralizing antibodies, GC participation, and clonal expansion of SARS-CoV-2-specific B cells than boosting of the opposite arm. These data reveal an unappreciated role for primed draining lymph node SSMs in Bmem cell fate determination. © 2025 The Author(s)

关键词： affinity maturation broadly neutralizing antibodies germinal center immune imprinting memory original antigenic sin plasma cells SARS-CoV-2 subcapsular sinus macrophage vaccine

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EMBEDDING SPACE AUGMENTATION FOR WEAKLY SUPERVISED LEARNING IN WHOLE-SLIDE IMAGES

arXiv

引用

arXiv 2022年

作者： Zaffar, Imaad Jaume, Guillaume Rajpoot, Nasir Mahmood, Faisal Department of Computer Science University College London United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry United Kingdom The Alan Turing Institute London United Kingdom

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EMBAUGMENTER, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster. © 2022, CC BY.

关键词： Generative adversarial networks

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Investigation of structural and electronic properties of boron carbide based on SCAN meta-GGA functional

引用

AIP Conference Proceedings 2023年第1期2604卷

作者： Muhammad R. Ramadhan Imam M. Al-Khafiiz Jihan Anavisha Jofim Y. Napitupulu Wanda R. Tiana Fahmi Astuti M. Zuhnir Piliang Physics Study Program Republic of Indonesia Defense University Kawasan Indonesia Peace and Security Center Sentul Bogor 16810 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

Boron Carbide (B4C) is one of the known superhard material that can be utilized in many engineering and scientific applications. Here we investigated B4C system in its rhombohedral structure (a=5.17 Å, α=65.52°) by using the meta-GGA functional, SCAN, within density functional theory framework. We observed that the optimized lattice parameters given by the SCAN functional is more realistic compared to the LDA and GGA calculations. From the optimized structure of B4C, we determined the bulk modulus value of 255 GPa which only differs by ∼3 % from experimental data. Furthermore, the underestimation of band gap in LDA and GGA implementations is also remedied, where the meta-GGA, SCAN, functional estimates 1.98 eV insulating gap from density of states calculation. This value is very close to the known experimental observations (2.09 eV).

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