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Prognostic and therapeutic value of LSM5 gene in human brain cancer Glioma: An omics database exploration approach

Informatics in Medicine Unlocked 2022年 34卷

作者： Ullah, Md. Asad Alam, Sayka Farzana, Maisha Tayab Moin, Abu Binte Sayed Prapty, Chowdhury Nusaiba Zohora, Umme Salma Rahman, Mohammad Shahedur Department of Biotechnology and Genetic Engineering Faculty of Biological Sciences Jahangirnagar University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Genetic Engineering and Biotechnology Faculty of Biological Sciences University of Chittagong Chattogram Bangladesh Wazed Miah Science Research Center Jahangirnagar University Dhaka Bangladesh

This study explored the diagnostic and therapeutic values of the LSM5 gene in two commonest types of gliomas, i.e., lower-grade glioma (LGG) and glioblastoma multiforme (GBM) by exploiting a bioinformatics-based database mining pipeline. LSM5 gene was reported to be upregulated at both mRNA and protein levels in both LGG and GBM tissues compared to adjacent normal tissues. Moreover, overexpression of LSM5 was later observed to be linked with glioma patients' demographic status and advancing cancer stages. Additionally, a differential and distinct methylation pattern of the LSM5 promoter and coding sequence in glioma tissues was reported. The gene of our interest was also discovered to undergo multiple amplification events (frequency of alteration: 1.1%) in both LGG and GBM patients. Furthermore, LSM5 overexpression was reported to be correlated with the poor overall and relapse-free survival of glioma patients. Subsequently, the immune phenotype analysis revealed that the expression of LSM5 correlates with the abundance levels of various immune cells i.e., B cell, T cell, dendritic cell, and immunomodulators, i.e., IL10RB, CD48, CD274 in glioma microenvironment. Finally, functional enrichment analysis of the neighbor genes of LSM5 showed that most of the genes are involved in different pivotal cellular processes like RNA splicing, DNA mismatch repair, and DNA replication. Overall, this study suggests that LSM5 and its downstream products are potential candidates for glioma diagnosis and treatment. © 2022

关键词： Biomarker Diagnosis Glioma LSM5 Treatment

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal 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 Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates

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Chem Catalysis 2025年第6期5卷

作者： Shao, Qianzhen Hollenbeak, Asher C. Jiang, Yaoyukun Ran, Xinchun Bachmann, Brian O. Yang, Zhongyue J. Department of Chemistry Vanderbilt University Nashville 37235 TN United States Center for Structural Biology Vanderbilt University Nashville 37235 TN United States The Vanderbilt Institute of Chemical Biology Vanderbilt University Nashville 37235 TN United States Data Science Institute Vanderbilt University Nashville 37235 TN United States Department of Chemical and Biomolecular Engineering Vanderbilt University Nashville 37235 TN United States The Chemical and Physical Biology Program Vanderbilt University Nashville 37232 TN United States

We developed SubTuner, a physics-based computational tool that tackles the challenge of identifying enzyme mutants with enhanced activity for specified non-native substrates. To test the performance of SubTuner, we designed three tasks, all aiming to identify beneficial anion methyltransferase mutants for synthesis of non-native S-adenosyl-L-methionine analogs: first, in the conversion of ethyl iodide from a pool of 190 Arabidopsis thaliana Harmless to Ozone Layer 1 (AtHOL1) single-point mutants for an initial test of accuracy and speed;second, of ethyl, n-propyl, cyclopropylmethyl, and phenethyl iodides from a pool of 600 Aspergillus clavatus methyltransferase multi-point mutants for a test of generalizability;and, eventually, of bulkier substrates for AtHOL1 combined with experimental characterization for a test of a priori predictivity. All tests demonstrated SubTuner's ability to accelerate enzyme engineering for non-native substrates, superior to existing bioinformatics and machine-learning-based tools. SubTuner, with its physical hypothesis, quantitative accuracy, and mechanism-informing ability, holds significant potential to aid enzyme engineering for substrate scope expansion. © 2025 Elsevier Inc.

关键词： anion methyltransferase artificial intelligence biocatalysis computational enzyme engineering EnzyHTP enzyme high-throughput enzyme modeling physics-guided modeling SDG9: Industry, innovation, and infrastructure

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Fabrication a sustainable adsorbent nanocellulose-mesoporous hectorite bead for methylene blue adsorption

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Holilah Asranudin El Messaoudi, Noureddine Ulfa, Maria Hamzah, Amir Hamid, Zuratul Ain Abdul Ramadhani, Dini Viandi Suryanegara, Lisman Mahardika, Melbi Melenia, Alvina Tata Pratama, Agus Wedi Prasetyoko, Didik Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Research Collaboration Center for Nanocellulose BRIN-Andalas University Research Collaboration Center for Biomass and Biorefinery Between BRIN and Universitas Padjadjaran Laboratory of Applied Chemistry and Environment Faculty of Sciences Ibn Zohr University Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Deputy for Infrastructure Research and Innovation National Research and Innovation Agency of Indonesia (BRIN) School of Materials & Mineral Resources Engineering Universiti Sains Malaysia Department of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Sukolilo

In this study, fabrication of mesoporous nanocellulose-hectorite-alginate bead (B-NcH) composites and their application for adsorption of MB from water was carried out. The synthesis of nanocellulose (NC) was conducted from mangosteen waste peel (Garcinia mangostana L.). Garcinia mangostana L. peel had 58.9 ± 0,41 % content of cellulose. NCs was successfully isolated using the acid hydrolysis method. NC from Garcinia mangostana L. peel had a diameter of 24.5 ± 0.2 nm and a length of 381.2 ± 10.4 nm. The superabsorbent was composed of nanocrystalline cellulose, mesoporous hectorite, and alginate. The functional group, phase, and morphology of composite was investigated. The composite with 5 % NC had excellent mechanical strength. The B-NcH5 showed a high adsorption capacity of methylene blue (MB), reaching 98.64 % at an adsorbent dose of 30 mg for 90 min. The presence of hectorite can increase the adsorption ability compared to NCs-alginate. The equilibrium adsorption data followed the Freundlich isotherm;however, the kinetics of adsorption showed the usefulness of adopting the PSO kinetic model. The highest adsorption capacity of B-NcH5 for MB was determined to be 57.59 mg/g. For thermodynamic studies, it was revealed that the adsorption of MB on BNcH-5 is a spontaneous and endothermic process. The removal efficiency was observed in six consecutive cycles, indicating at the favorable reusability of this adsorbent. These results validate the potential use of this B-NcH5 adsorbent for water treatment applications. © 2024

关键词： Adsorption Bead Hectorite Methylene blue Nanocellulose Water treatment

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning

Interactive Ultrasound Prostate Cancer Segmentation using De...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tao Peng Hui Mao Caishan Wang Gongye Di Ting Hu Lei Zhang School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Dermatology Huangshi Central Hospital Hubei China Department of Ultrasound Second Affiliated Hospital of Soochow University Suzhou China Department of Ultrasonic Taizhou People’s Hospital Affiliated to Nanjing Medical University Taizhou China Department of Obstetrics and Gynaecology Ultrasonography The First Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan China

Segmentation of prostate cancer on ultrasound (US) images is challenging because of the low-intensity contrast around the region of interest (ROI) contour caused by the intestinal gas. To the best of our knowledge, our work is one of the extremely rare groups to segment prostate cancer on US images, which designs a coarse-to-refinement segmentation framework with four merits: 1) it fuses the advantages of the deep learning- and principal curve-based algorithms to locate the ROI and fit the data center automatically, respectively; 2) a principal curve-based polygon searching method is designed by newly adding some constraints so that the performance of our method could be enhanced; 3) the quantum characteristics are added into evolution network, while newly adding numerous- operator scheme and global optimum search schemes; 4) an interpretable mathematical mapping model is developed to denote the ROI contour. Segmentation results show that our network outperforms other medical image segmentation models.

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

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Cell 2025年第12期188卷 3310-3328.e27页

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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Improving access to housing and supportive services for runaway and homeless youth: Reducing vulnerability to human trafficking in New York City

A I I E Transactions

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A I I E Transactions 2024年第3期56卷

作者： Yaren Bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

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