46 results on '"Computational Biology"'
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2. Pentagon Found Daily, Metagenomic Detection of Novel Bioaerosol Threats to Be Cost-Prohibitive: Can Virtualization and AI Make It Cost-Effective?
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Srikrishna D
- Subjects
- United States, Humans, Cost-Benefit Analysis, Computational Biology, Government Agencies, Artificial Intelligence, Military Personnel
- Abstract
In 2022, the Pentagon Force Protection Agency found threat agnostic detection of novel bioaerosol threats to be "not feasible for daily operations" due to the cost of reagents used for metagenomics, cost of sequencing instruments, and cost of labor for subject matter experts to analyze bioinformatics. Similar operational difficulties might extend to many of the 280,000 buildings (totaling 2.3 billion square feet) at 5,000 secure US Department of Defense military sites, 250 Navy ships, as well as many civilian buildings. These economic barriers can still be addressed in a threat agnostic manner by dynamically pooling samples from dry filter units, called spike-triggered virtualization, whereby pooling and sequencing depth are automatically modulated based on novel biothreats in the sequencing output. By running at a high average pooling factor, the daily and annual cost per dry filter unit can be reduced by 10 to 100 times depending on the chosen trigger thresholds. Artificial intelligence can further enhance the sensitivity of spike-triggered virtualization. The risk of infection during the 12- to 24-hour window between a bioaerosol incident and its detection remains, but in some cases it can be reduced by 80% or more with high-speed indoor air cleaning exceeding 12 air changes per hour, which is similar to the rate of air cleaning in passenger airplanes in flight. That level of air changes per hour or higher is likely to be cost-prohibitive using central heating ventilation and air conditioning systems, but it can be achieved economically by using portable air filtration in rooms with typical ceiling heights (less than 10 feet) for a cost of approximately $0.50 to $1 per square foot for do-it-yourself units and $2 to $5 per square foot for high-efficiency particulate air filters.
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- 2024
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3. 2022 IEEE conference on computational intelligence in bioinformatics and computational biology (IEEE CIBCB, 2022).
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Brown JA, Houghten S, and Fogel GB
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- Computational Biology, Artificial Intelligence
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- 2024
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4. Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.
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Wang Y, Chen S, Bao S, Yao L, Wen Z, Xu L, Chen X, Guo S, Pang H, Zhou Y, and Zhou P
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- Humans, Epigenesis, Genetic, Quality of Life, Fibrosis, Transforming Growth Factor beta metabolism, Artificial Intelligence, Radiation Injuries genetics
- Abstract
This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-β) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Chen, Bao, Yao, Wen, Xu, Chen, Guo, Pang, Zhou and Zhou.)
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- 2024
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5. Global trends and hotspots of ulcerative colitis based on bibliometric and visual analysis from 1993 to 2022.
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Wang J, Mao T, Zhou H, Jiang X, Zhao Z, and Zhang X
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- Humans, Bibliometrics, China, Computational Biology, Artificial Intelligence, Colitis, Ulcerative epidemiology, Colitis, Ulcerative therapy
- Abstract
Ulcerative colitis (UC) has seen a significant increase over the past 3 decades. However, our understanding of its etiology, pathogenesis, and pharmacological treatment remains limited. This comprehensive review aims to address these gaps by analyzing trends, evaluating previous research, and providing insights for future investigations. We conducted a bibliometric analysis of UC-related papers indexed in the Web of Science from 1993 to 2022. The author, organization, country, and keyword networks in the field of UC were visualized. A total of 36,483 papers were included, showing a continuous upward trend. Most research on UC was conducted in universities, with hospitals leading in high-quality studies. The United States emerged as the primary contributor, followed by China and the United Kingdom. The overall quality of UC-related publications improved, indicating sustained interest in the field. The keywords related to UC was classified into 9 clusters. Keywords detection revealed that UC research focused mainly on the discovery of its etiology and exploration of treatment methods, with research directions evolving from initial treatment of UC and related diseases to clinical trials of UC and subsequently incorporating genomics and bioinformatics techniques to study UC and explore new therapeutic methods and drugs, including recent advances in gut microbiota. Our study identified gaps in understanding the etiology, pathogenesis, and treatment of UC. Future research in UC should focus on genomics, personalized treatment, microbial therapy and leveraging machine learning and artificial intelligence. These areas hold the potential for improving UC diagnosis, treatment, and management., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)
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- 2024
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6. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.
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Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, and Williams MS
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- Humans, Computational Biology, Genomics, Artificial Intelligence, Medicine
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Objective: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space., Process: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members., Conclusions: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
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- 2024
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7. Historical perspective and future directions: computational science in immuno-oncology.
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Ricker CA, Meli K, and Van Allen EM
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- Humans, Prospective Studies, Quality of Life, Medical Oncology, Antigen-Antibody Complex, Tumor Microenvironment, Artificial Intelligence, Neoplasms therapy
- Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life., Competing Interests: Competing interests: CAR and KM declare no conflicts of interest. EMVA disclosures: Advisory/consulting: Tango Therapeutics, Genome Medical, Genomic Life, Enara Bio, Manifold Bio, Monte Rosa, Novartis Institute for Biomedical Research, Riva Therapeutics, Serinus Bio. Research support: Novartis, Bristol-Myers Squibb, Sanofi. Equity: Tango Therapeutics, Genome Medical, Genomic Life, Syapse, Enara Bio, Manifold Bio, Microsoft, Monte Rosa, Riva Therapeutics, Serinus Bio. Travel reimbursement: None. Patents: Institutional patents filed on chromatin mutations and immunotherapy response, and methods for clinical interpretation; intermittent legal consulting on patents for Foaley & Hoag., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2024
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8. A journey from omics to clinicomics in solid cancers: Success stories and challenges.
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Mehrotra S, Sharma S, and Pandey RK
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- Humans, Female, Algorithms, Computational Biology, Machine Learning, Artificial Intelligence, Breast Neoplasms
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The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse., (Copyright © 2024. Published by Elsevier Inc.)
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- 2024
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9. Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data.
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Duffy G, Christensen K, and Ouyang D
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- Humans, Algorithms, Artificial Intelligence, Computational Biology
- Abstract
Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficient tools for assessing cardiac function. AI diagnostics claims to improve upon the human-to-human variation that is known to be significant. However, when put in practice, for cardiac ultrasound, AI models are being run on images acquired by human sonographers whose quality and consistency may vary. With more variation than other medical imaging modalities, variation in image acquisition may lead to out-of-distribution (OOD) data and unpredictable performance of the AI tools. Recent advances in ultrasound technology has allowed the acquisition of both 3D as well as 2D data, however 3D has more limited temporal and spatial resolution and is still not routinely acquired. Because the training datasets used when developing AI algorithms are mostly developed using 2D images, it is difficult to determine the impact of human variation on the performance of AI tools in the real world. The objective of this project is to leverage 3D echos to simulate realistic human variation of image acquisition and better understand the OOD performance of a previously validated AI model. In doing so, we develop tools for interpreting 3D echo data and quantifiably recreating common variation in image acquisition between sonographers. We also developed a technique for finding good standard 2D views in 3D echo volumes. We found the performance of the AI model we evaluated to be as expected when the view is good, but variations in acquisition position degraded AI model performance. Performance on far from ideal views was poor, but still better than random, suggesting that there is some information being used that permeates the whole volume, not just a quality view. Additionally, we found that variations in foreshortening didn't result in the same errors that a human would make.
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- 2024
10. Session Introduction: Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.
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Fouladvand S, Pierson E, Jankovic I, Ouyang D, Chen JH, and Daneshjou R
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- Humans, Computational Biology, Algorithms, Artificial Intelligence, Clinical Medicine
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Artificial Intelligence (AI) models are substantially enhancing the capability to analyze complex and multi-dimensional datasets. Generative AI and deep learning models have demonstrated significant advancements in extracting knowledge from unstructured text, imaging as well as structured and tabular data. This recent breakthrough in AI has inspired research in medicine, leading to the development of numerous tools for creating clinical decision support systems, monitoring tools, image interpretation, and triaging capabilities. Nevertheless, comprehensive research is imperative to evaluate the potential impact and implications of AI systems in healthcare. At the 2024 Pacific Symposium on Biocomputing (PSB) session entitled "Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface", we spotlight research that develops and applies AI algorithms to solve real-world problems in healthcare.
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- 2024
11. AI-Driven Enhancements in Drug Screening and Optimization.
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Serghini A, Portelli S, and Ascher DB
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- Drug Evaluation, Preclinical, Drug Discovery, Drug Industry, Artificial Intelligence, Drug Development
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The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
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- 2024
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12. LARGE LANGUAGE MODELS (LLMS) AND CHATGPT FOR BIOMEDICINE.
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Arighi C, Brenner S, and Lu Z
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- Humans, Language, Artificial Intelligence, Computational Biology
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Large Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate new content, making them highly desirable in many biomedical applications and beyond. In this workshop, we aim to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.
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- 2024
13. From genome to clinic: The power of translational bioinformatics in improving human health.
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Singh S, Pandey AK, and Prajapati VK
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- Humans, Quantum Theory, Computational Biology, Genomics, Artificial Intelligence, Computing Methodologies
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Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes., (Copyright © 2024. Published by Elsevier Inc.)
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- 2024
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14. Integration between Bioinformatics Algorithms and Neutrosophic Theory.
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Farag, Romany M., Shams, Mahmoud Y., Aldawody, Dalia A., Khalid, Huda E., El-Bakry, Hazem M., and Salama, Ahmed A.
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COMPUTATIONAL biology , *ARTIFICIAL intelligence , *BIOINFORMATICS , *DATA mining , *DATABASES , *NUCLEIC acids , *BIOINFORMATICS software , *SYNTHETIC biology - Abstract
This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. [ABSTRACT FROM AUTHOR]
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- 2024
15. Immunoinformatics and Vaccine Development
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Shukla, Shruti, Mani, Ashutosh, Chaudhary, Amit, editor, Sethi, Sushanta K., editor, and Verma, Akarsh, editor
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- 2024
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16. Vaccine design and development: Exploring the interface with computational biology and AI.
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Ananya, Panchariya, Darshan C., Karthic, Anandakrishnan, Singh, Surya Pratap, Mani, Ashutosh, Chawade, Aakash, and Kushwaha, Sandeep
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VACCINE development , *COMPUTATIONAL biology , *ARTIFICIAL intelligence , *DRUG design , *LIFE sciences - Abstract
Abstract\nPLAIN LANGUAGE SUMMARYComputational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design. [ABSTRACT FROM AUTHOR]
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- 2024
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17. The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare.
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Patel, Ankush U., Gu, Qiangqiang, Esper, Ronda, Maeser, Danielle, and Maeser, Nicole
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ARTIFICIAL intelligence , *MACHINE learning , *MEDICAL care , *COMPUTATIONAL biology , *MEDICAL informatics - Abstract
As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine. [ABSTRACT FROM AUTHOR]
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- 2024
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18. ECCB2024: The 23rd European Conference on Computational Biology.
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Kukkonen-Macchi, Anu, Hautaniemi, Sampsa, Heil, Katharina F, Heinäniemi, Merja, Jensen, Lars Juhl, Junttila, Sini, Käll, Lukas, Laiho, Asta, Maccallum, Peter, Nykter, Matti, Persson, Bengt, Suomi, Tomi, Bossche, Tim Van Den, Nyrönen, Tommi H, and Elo, Laura L
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LIFE sciences , *ARTIFICIAL intelligence , *DATA privacy , *SCIENTIFIC literature , *COMPUTATIONAL biology , *DEEP learning - Abstract
The article provides information about the 23rd European Conference on Computational Biology (ECCB2024) held in Turku, Finland. The conference focuses on data and algorithms for health and science and attracts scientists and industry professionals from various disciplines. It showcases cutting-edge developments in computational biology, including systems biology, artificial intelligence, single-cell and spatial technologies, and data integration. The conference features keynote lectures, scientific debates, poster presentations, workshops, and tutorials. The ECCB2024 proceedings include 24 papers selected through a peer-review process, and the conference promotes gender equity and adheres to a code of conduct to ensure a safe and respectful environment for all attendees. [Extracted from the article]
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- 2024
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19. AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology.
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Weber, Andreas, Enderle-Ammour, Kathrin, Kurowski, Konrad, Metzger, Marc C., Poxleitner, Philipp, Werner, Martin, Rothweiler, René, Beck, Jürgen, Straehle, Jakob, Schmelzeisen, Rainer, Steybe, David, and Bronsert, Peter
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HEAD & neck cancer diagnosis , *TISSUE analysis , *DEEP learning , *MOUTH tumors , *PREDICTIVE tests , *STAINS & staining (Microscopy) , *INTRAOPERATIVE care , *ARTIFICIAL intelligence , *HEAD & neck cancer , *RAMAN spectroscopy , *EPITHELIUM , *RESEARCH funding , *COMPUTER-aided diagnosis , *ARTIFICIAL neural networks , *SQUAMOUS cell carcinoma , *ADIPOSE tissues ,RESEARCH evaluation - Abstract
Simple Summary: Stimulated Raman Histology (SRH) is a technique that uses laser light to create detailed images of tissues without the need for traditional staining. This study aimed to use deep learning to classify oral squamous cell carcinoma (OSCC) and different non-malignant tissue types using SRH images. The performances of the classifications between SRH images and the original images obtained from stimulated Raman scattering (SRS) were compared. A deep learning model was trained on 64 images and tested on 16, showing that it could effectively identify tissue types during surgery, potentially speeding up decision making in oral cancer surgery. Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k1 = 2845 cm−1 and k2 = 2930 cm−1. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images. [ABSTRACT FROM AUTHOR]
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- 2024
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20. m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.
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Suleman, Muhammad Taseer, Alturise, Fahad, Alkhalifah, Tamim, and Khan, Yaser Daanial
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NUCLEOTIDE sequence , *SITE-specific mutagenesis , *COMPUTATIONAL biology , *MASS spectrometry , *METABOLITES , *IDENTIFICATION - Abstract
Background: 1-methyladenosine (m1A) is a variant of methyladenosine that holds a methyl substituent in the 1st position having a prominent role in RNA stability and human metabolites. Objective: Traditional approaches, such as mass spectrometry and site-directed mutagenesis, proved to be time-consuming and complicated. Methodology: The present research focused on the identification of m1A sites within RNA sequences using novel feature development mechanisms. The obtained features were used to train the ensemble models, including blending, boosting, and bagging. Independent testing and k-fold cross validation were then performed on the trained ensemble models. Results: The proposed model outperformed the preexisting predictors and revealed optimized scores based on major accuracy metrics. Conclusion: For research purpose, a user-friendly webserver of the proposed model can be accessed through https://taseersuleman-m1a-ensem1.streamlit.app/. [ABSTRACT FROM AUTHOR]
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- 2024
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21. Editorial trend: adverse outcome pathway (AOP) and computational strategy — towards new perspectives in ecotoxicology.
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Baudiffier, Damien, Audouze, Karine, Armant, Olivier, Frelon, Sandrine, Charles, Sandrine, Beaudouin, Remy, Cosio, Claudia, Payrastre, Laurence, Siaussat, David, Burgeot, Thierry, Mauffret, Aourell, Degli Esposti, Davide, Mougin, Christian, Delaunay, Delphine, and Coumoul, Xavier
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ENVIRONMENTAL toxicology ,ARTIFICIAL intelligence ,PHARMACODYNAMICS ,COMPUTATIONAL neuroscience ,TOXICOLOGY ,SYSTEMS biology ,RISK assessment - Abstract
The adverse outcome pathway (AOP) has been conceptualized in 2010 as an analytical construct to describe a sequential chain of causal links between key events, from a molecular initiating event leading to an adverse outcome (AO), considering several levels of biological organization. An AOP aims to identify and organize available knowledge about toxic effects of chemicals and drugs, either in ecotoxicology or toxicology, and it can be helpful in both basic and applied research and serve as a decision-making tool in support of regulatory risk assessment. The AOP concept has evolved since its introduction, and recent research in toxicology, based on integrative systems biology and artificial intelligence, gave it a new dimension. This innovative in silico strategy can help to decipher mechanisms of action and AOP and offers new perspectives in AOP development. However, to date, this strategy has not yet been applied to ecotoxicology. In this context, the main objective of this short article is to discuss the relevance and feasibility of transferring this strategy to ecotoxicology. One of the challenges to be discussed is the level of organisation that is relevant to address for the AO (population/community). This strategy also offers many advantages that could be fruitful in ecotoxicology and overcome the lack of time, such as the rapid identification of data available at a time t, or the identification of "data gaps". Finally, this article proposes a step forward with suggested priority topics in ecotoxicology that could benefit from this strategy. [ABSTRACT FROM AUTHOR]
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- 2024
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22. A Survey of Recent Practice of Artificial Life in Visual Art.
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Wu, Zi-Wei, Qu, Huamin, and Zhang, Kang
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ART , *LITERATURE reviews , *SYNTHETIC biology , *COMPUTATIONAL biology , *ARTIFICIAL intelligence , *PRODUCTIVE life span - Abstract
Nowadays, interdisciplinary fields between Artificial Life, artificial intelligence, computational biology, and synthetic biology are increasingly emerging into public view. It is necessary to reconsider the relations between the material body, identity, the natural world, and the concept of life. Art is known to pave the way to exploring and conveying new possibilities. This survey provides a literature review on recent works of Artificial Life in visual art during the past 40 years, specifically in the computational and software domain. Having proposed a set of criteria and a taxonomy, we briefly analyze representative artworks of different categories. We aim to provide a systematic overview of how artists are understanding nature and creating new life with modern technology. [ABSTRACT FROM AUTHOR]
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- 2024
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23. Artificial Intelligence and Computational Biology in Gene Therapy: A Review
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Danaeifar, Mohsen and Najafi, Ali
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- 2024
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24. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances
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Gallo, Eugenio
- Published
- 2024
- Full Text
- View/download PDF
25. Conectado Partners with Hollister High School to Launch Artificial Intelligence and Bioinformatics Education
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Computational biology ,Artificial intelligence ,High schools ,Educational services industry ,Education ,Artificial intelligence ,Business ,Business, international - Abstract
Innovative Bootcamps Designed to Equip Students with Future-Ready Skills for Tomorrow's Workforce SILICON VALLEY, Calif. -- Conectado Inc., a provider of immersive education services, is thrilled to announce a dynamic [...]
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- 2024
26. Editorial: Computational drug discovery of medicinal compounds for cancer management, volume II.
- Author
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Khurshid Ahmad, Sibhghatulla Shaikh, Faez Iqbal Khan, Mohammad Ehtisham Khan, and Kassiou, Michael
- Subjects
- *
DRUG discovery , *ARTIFICIAL intelligence , *COMPUTATIONAL biology , *MOLECULAR docking , *PHARMACODYNAMICS , *ANTINEOPLASTIC agents - Published
- 2024
- Full Text
- View/download PDF
27. The 2024 ISCB Overton Prize Award—Dr Martin Steinegger.
- Author
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Wiper, Mallory L
- Subjects
- *
ARTIFICIAL intelligence , *COMPUTATIONAL biology , *COMPUTER operating systems , *EDUCATORS , *MOLECULAR biology - Abstract
Dr. Martin Steinegger has been awarded the 2024 ISCB Overton Prize for his significant contributions to computational biology. Despite facing challenges in his academic journey, including being dyslexic and being placed in an educational track that limited his options for university, Steinegger pursued his passion for computers and technology. He eventually attended university and conducted independent research projects, which led him to the field of computational biology. Steinegger's research focuses on protein structures and he hopes to answer big questions in this field. He also emphasizes the importance of training and mentoring new scientists, encouraging exploration and scientific discovery. Steinegger is humbled to receive the Overton Prize and hopes to continue making strides in computational biology. [Extracted from the article]
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- 2024
- Full Text
- View/download PDF
28. The 2024 ISCB Accomplishments by a Senior Scientist Award—Dr Tandy Warnow.
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Wiper, Mallory L
- Subjects
- *
ARTIFICIAL intelligence , *EDUCATORS , *HORIZONTAL gene transfer , *COMPUTATIONAL biology , *MOLECULAR biology - Abstract
Dr Tandy Warnow has been awarded the Accomplishments by a Senior Scientist Award by the International Society for Computational Biology (ISCB) for her significant contributions to computational biology. Dr Warnow's interest in math and science began at a young age and was nurtured by supportive teachers and mentors throughout her academic career. Her research focuses on combining theory and data to gain a comprehensive understanding of problems in computational biology, particularly in the field of phylogenetic tree estimation. Dr Warnow also emphasizes the importance of mentoring and supporting students in their research endeavors. Despite not expecting the award, Dr Warnow is honored to receive it. [Extracted from the article]
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- 2024
- Full Text
- View/download PDF
29. The 2024 ISCB Innovator Award—Dr Su-In Lee.
- Author
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Wiper, Mallory L
- Subjects
- *
ARTIFICIAL neural networks , *ARTIFICIAL intelligence , *COMPUTATIONAL biology , *MOLECULAR biology , *MICROARRAY technology - Abstract
Dr. Su-In Lee has been awarded the 2024 ISCB Innovator Award for her contributions to computational biology. Her interest in math and science began at a young age, and she conducted her undergraduate thesis on developing a deep neural network. During her PhD, she shifted her focus to computational biology and explored the use of AI in molecular biology and genetics. Currently, her research focuses on the intersection of AI, biology, and clinical medicine. Dr. Lee's work emphasizes the importance of model interpretability and explainability, leading to the development of the SHAP framework. She is also interested in AI auditing frameworks and the importance of understanding the reasoning processes of different AI models. As a PI, Dr. Lee values mentoring and encourages her students to explore different areas of research. She is honored to receive the ISCB Innovator Award and is grateful for the recognition. [Extracted from the article]
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- 2024
- Full Text
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30. The 2024 Outstanding Contributions to ISCB Award—Dr Scott Markel.
- Author
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Wiper, Mallory L
- Subjects
- *
ARTIFICIAL intelligence , *JOB fairs , *COMPUTATIONAL biology , *GRANT writing , *MOLECULAR biology - Abstract
Dr. Scott Markel has been awarded the Outstanding Contributions to ISCB Award by the International Society for Computational Biology (ISCB) for his exemplary leadership, education, and service. He has been involved with ISCB since the early 2000s and has held various positions within the organization, including Secretary for ISCB's Board of Directors. Markel has played a key role in bringing structure and continuity to the society's leadership and administration. He advises junior scientists and trainees to be curious, gain experience, and share their experiences when seeking service opportunities. Markel believes that ISCB has a bright future and can continue to support the computational biology community by providing infrastructure, promoting cutting-edge research, and facilitating knowledge sharing. [Extracted from the article]
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- 2024
- Full Text
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31. Advancing biomolecular simulation through exascale HPC, AI and quantum computing.
- Author
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Pyzer-Knapp, Edward O. and Curioni, Alessandro
- Subjects
- *
QUANTUM computing , *SCIENTIFIC method , *WORKFLOW software , *COMPUTATIONAL biology , *ARTIFICIAL intelligence , *SYNTHETIC biology , *QUANTUM computers - Abstract
Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations. • The latest advances in large scale biomolecular simulation make it a powerful tool for both post-fact rationalisation and prediction of biomolecular phenomena. • The largest systems can enable paradigm shifting simulation capabilities, but only when the software and workflow capabilities are optimized to fully utilise the system. • Properly deployed, AI enables significant improvements in the speed / accuracy ratio through powerful predictive models, intelligent steering and faster, highly accurate, potentials. • Quantum computing is an emerging paradigm which has the potential to further revolutionise the field, although larger high-fidelity systems will be required to fulfil this potential. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
32. Research Reports on Bioinformatics from PHENIKAA University Provide New Insights (Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways)
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Computational biology ,Artificial intelligence ,Genetic research ,Neural networks ,Neural network ,Artificial intelligence ,Biotechnology industry ,Pharmaceuticals and cosmetics industries - Abstract
2024 MAY 1 (NewsRx) -- By a News Reporter-Staff News Editor at Biotech Week -- Data detailed on bioinformatics have been presented. According to news reporting out of PHENIKAA University [...]
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- 2024
33. BIOINFORMATICS EXPERT TO MED STUDENTS: IGNORE THE AI REVOLUTION AT THE PERIL OF PATIENTS, PROVIDERS
- Subjects
Students ,Computational biology ,Artificial intelligence ,Medical colleges ,Revolutions ,Artificial intelligence ,News, opinion and commentary ,Brown University - Abstract
PROVIDENCE, R.I. -- The following information was released by Brown University: In a presentation at Brown's Warren Alpert Medical School, Dr. Isaac Kohane, who has worked on medical AI since [...]
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- 2024
34. Calendar of Events.
- Subjects
- *
ARTIFICIAL intelligence , *COMPUTATIONAL intelligence , *COMPUTATIONAL biology , *EVOLUTIONARY computation , *COMPUTER assisted instruction , *VIRTUAL reality - Abstract
The International Journal of Computational Intelligence & Applications provides a calendar of upcoming international conferences and events. Some of the conferences listed include the IEEE International Conference on Development and Learning in Austin, USA; the IEEE International Conference on Evolving and Adaptive Intelligent Systems in Madrid, Spain; and the IEEE Conference on Artificial Intelligence in Singapore. Other conferences are scheduled to take place in China, Japan, Brazil, and Norway. The journal provides website links for more information on each event. [Extracted from the article]
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- 2024
- Full Text
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35. New Middle Cerebral Artery Study Findings Have Been Reported by Investigators at Shanghai Jiao Tong University (Mcas-gp: Deep Learning-empowered Middle Cerebral Artery Segmentation and Gate Proposition).
- Subjects
CEREBRAL arteries ,COMPUTATIONAL biology ,DATA analytics ,ARTIFICIAL intelligence ,ELECTRONIC records - Abstract
Researchers at Shanghai Jiao Tong University in China have developed a deep learning-empowered framework called MCAS-GP to automate the segmentation and gate proposition of the middle cerebral artery (MCA) in fetal MCA Doppler assessment. The framework utilizes a novel learnable atrous spatial pyramid pooling (LASPP) module to adaptively learn multi-scale features and introduces a new evaluation metric called the Affiliation Index. Extensive experiments on a large-scale MCA dataset and two public surgical datasets demonstrated that MCAS-GP significantly improved accuracy and inference time. This research has been peer-reviewed and published in IEEE/ACM Transactions on Computational Biology and Bioinformatics. [Extracted from the article]
- Published
- 2024
36. Albert Einstein College of Medicine Researcher Has Published New Data on Artificial Intelligence (Improving viral annotation with artificial intelligence).
- Subjects
LANGUAGE models ,ARTIFICIAL intelligence ,COMPUTATIONAL biology ,TECHNOLOGICAL innovations ,MACHINE learning - Abstract
A recent study conducted by researchers at the Albert Einstein College of Medicine in Bronx, New York, explores the use of artificial intelligence (AI) to improve the annotation of viral sequences in metagenomes. The study highlights the limitations of current methods in annotating viral diversity and proposes the use of self-supervised representation learning to supplement statistical sequence representations for remote viral protein homology detection. The researchers discuss the potential and challenges of large language models for viral annotation and suggest future directions for developing better models. This research contributes to the understanding of viruses and their role in microbial communities, with implications for human and environmental health. [Extracted from the article]
- Published
- 2024
37. Findings from Wuhan University in Cancer Reported (Sgda: Towards 3-d Universal Pulmonary Nodule Detection Via Slice Grouped Domain Attention).
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PULMONARY nodules ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,MULTIMEDIA computer applications ,COMPUTATIONAL biology - Abstract
A study conducted at Wuhan University in China has focused on the early detection of lung cancer, which is the leading cause of cancer death globally. The researchers propose a new method called slice grouped domain attention (SGDA) to enhance the generalization capability of pulmonary nodule detection networks. The SGDA module works in multiple directions and combines the outputs from different domains to modulate the input group. The study concludes that SGDA significantly improves the performance of multi-domain pulmonary nodule detection compared to existing methods. This research has been peer-reviewed and published in the IEEE/ACM Transactions on Computational Biology and Bioinformatics journal. [Extracted from the article]
- Published
- 2024
38. Discovery Sciences Researchers Broaden Understanding of Machine Learning (Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation).
- Subjects
MACHINE learning ,TECHNOLOGICAL innovations ,ARTIFICIAL intelligence ,DRUG discovery ,COMPUTATIONAL biology - Abstract
A recent report discusses the use of artificial intelligence in drug discovery, specifically focusing on the role of phytochemicals. The researchers highlight the long history of phytochemicals in drug discovery and the recent advancements in analytical techniques that have made it easier to discover bioactive leads from natural compounds. They emphasize the importance of computational techniques such as molecular docking, QSAR modeling, machine learning, and network pharmacology in predicting the potential targets of natural products. The researchers also mention the use of LC-MS and LC-NMR to streamline compound identification. They conclude by discussing the emerging approach of machine learning, which involves interrelating phytochemical properties with diverse physiological activities. The article provides more information on this research and can be accessed for free. [Extracted from the article]
- Published
- 2024
39. Leading computational scientist & oncology researcher Elana Fertig appointed new Director of the Institute for Genome Sciences.
- Subjects
COMPUTATIONAL biology ,BIOLOGICAL systems ,NUMERICAL weather forecasting ,HAEMOPHILUS influenzae ,TECHNOLOGICAL innovations ,ONCOLOGY - Abstract
Elana J. Fertig, an internationally-recognized computational scientist and oncology researcher, has been appointed as the new Director of the Institute for Genome Sciences (IGS) at the University of Maryland School of Medicine. Dr. Fertig's work focuses on integrating spatial multi-omics technologies with mathematical models to develop a new predictive medicine paradigm in cancer. The IGS conducts research in genomics, microbiome, and systems biology to better understand health issues, evolutionary biology, and various diseases. Dr. Fertig's expertise in computational methods and artificial intelligence will contribute to the advancement of genomic science and its application in clinical research. [Extracted from the article]
- Published
- 2024
40. Machine learning based computational biology and digital medicine.
- Subjects
TECHNOLOGICAL innovations ,ARTIFICIAL intelligence ,COMPUTATIONAL biology ,MACHINE learning ,BIOMEDICAL engineering - Abstract
The field of machine learning is rapidly advancing and has significant implications for healthcare and biological research. A special thematic issue titled "Recent Trends in Machine Learning Based Computational Biology and Digital Medicine" will explore these developments and how they are addressing challenges in these fields. The issue invites researchers to contribute review articles on machine learning approaches in areas such as e-health, biomedical engineering, and medical image analysis. This special issue aims to showcase the transformative potential of artificial intelligence in improving medical diagnostics, treatment, and overall healthcare delivery. It will serve as a valuable resource for academics, practitioners, and policymakers, fostering collaboration and driving innovation in computational biology and digital medicine. [Extracted from the article]
- Published
- 2024
41. CMU researchers outline promises, challenges of understanding AI for biological discovery.
- Subjects
MACHINE learning ,LANGUAGE models ,ARTIFICIAL intelligence ,COMPUTATIONAL biology ,TECHNOLOGICAL innovations - Abstract
Researchers at Carnegie Mellon University's School of Computer Science have outlined the promises and challenges of using interpretable machine learning methods in computational biology. In an article published in Nature Methods, the researchers propose guidelines for the use of interpretable machine learning methods to uncover underlying biological mechanisms in health and disease. They emphasize the importance of understanding model behavior and recommend using multiple interpretable machine learning methods with diverse sets of hyperparameters to obtain a comprehensive understanding of the model behavior. The researchers also caution against cherry-picking results and highlight the need for interdisciplinary collaborations to facilitate the broader use of AI for scientific impact. [Extracted from the article]
- Published
- 2024
42. GSK signs AI pact with Ochre to pinpoint source of liver diseases.
- Author
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Armstrong, Annalee
- Subjects
LIVER diseases ,ARTIFICIAL intelligence ,COMPUTATIONAL biology ,HEPATITIS B ,MACHINE learning - Abstract
GSK and Ochre Bio will work together in a $37.5 million partnership to pinpoint the drivers of liver disease. [ABSTRACT FROM AUTHOR]
- Published
- 2024
43. Merck KGaA taps AI-focused biotech for new ADC pact worth $376M.
- Author
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Armstrong, Annalee
- Subjects
DRUG discovery ,COMPUTATIONAL biology ,ARTIFICIAL intelligence ,SPROUTS ,IMMUNOLOGY - Abstract
Merck KGaA is hoping to sprout some new oncology and immunology therapeutics with Biolojic Design's AI-driven drug discovery engine. [ABSTRACT FROM AUTHOR]
- Published
- 2024
44. Bonds and bytes: The odyssey of structural biology.
- Author
-
Hoff, S.E., Zinke, M., Izadi-Pruneyre, N., and Bonomi, M.
- Subjects
- *
COMPUTATIONAL biology , *BIOLOGICAL systems , *MULTISCALE modeling , *BIOLOGY , *ARTIFICIAL intelligence , *COMPUTATIONAL neuroscience - Abstract
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively reveals the behavior of biological systems across various spatiotemporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative structural biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in structural biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatiotemporal modeling, and provides an outlook into future directions of this field. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. FROM CODE TO CROPS: HARNESSING BIOINFORMATICS AND ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURAL OMICS
- Author
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Anand, Lakshay
- Subjects
- artificial intelligence, machine learning, Agri-omics, bioinformatics, Bioinformatics, Computational Biology, Genomics, Horticulture, Viticulture and Oenology
- Abstract
Global agricultural faces numerous challenges, such as climate change, resource limitations, novel pests and diseases, increasing costs, and the ever-increasing human population. To tackle these challenges, we need innovative strategies that combine new technologies and data analytics approaches to enhance agricultural output, promote sustainable methods, and optimize resource allocation. The key to this innovation lies in understanding the complex molecular web within plants that governs their growth, defense, and adaptability mechanisms. By mastering this molecular network, we can cultivate crops that are more resilient, sustainable, and suitable for different climatic terrains. Moreover, studying the symbiotic relationship between plants and microorganisms can help us develop unique agricultural strategies that promote higher yield and robustness. In recent times, multi-omics research has gained popularity as it aims to provide a comprehensive understanding of plant molecular biology by combining various fields such as genomics, transcriptomics, metabolomics, proteomics, epigenomics and metagenomics. However, the complex and extensive data generated by these studies can be both advantageous and challenging. To derive meaningful insights from this data, bioinformatics plays a crucial role. Additionally, the increase in open-source bioinformatics software has further fueled this revolution, providing solutions to specific research inquiries. Artificial Intelligence (AI) is a potential game-changer in agriculture. It has already made significant contributions to the industry, but its application on agricultural multi-omics data is less explored. With insightful utilization of AI, we can develop more effective strategies to combat agricultural challenges such as abiotic and biotic stress and even better understand the interactions between plants and microbes. This dissertation revolves around the utilization of these two domains , bioinformatics and AI, and showcases their potential in the realm of agricultural omics. This thesis presents chromoMap, an open-source bioinformatics tool that offers interactive visualizations of chromosomes and their genomic features. It displays ideograms, allowing comparison across species, and is versatile in its application to any organism with a genome assembly. It is capable of producing both publication-ready, high-resolution, static images as well as web-viewable interactive HTML documents. Additionally, I present the application of chromoMap R to one agricultural model and a JavaScript variant of this tool to another. Finally, this thesis explores the ability of several machine learning (ML) algorithms, including deep learning methods, to predict the planted cultivar (genotype of both scion and rootstock) of a given vineyard, irrespective of its geographic location, using soil microbiome data. Collectively, this dissertation describes two major contributions to the field of agricultural omics using the interdisciplinary fields of bioinformatics and AI, hence showcasing their potential in agriculture.
- Published
- 2024
46. $5 million grant bets on computational biology, AI to change the future of cancer.
- Subjects
COMPUTATIONAL biology ,ARTIFICIAL intelligence ,CANCER genetics ,DEVELOPMENTAL biology ,RESEARCH grants - Abstract
A multidisciplinary research team at Gladstone Institutes has received a $5 million grant to fund the Biswas Center for Transformative Computational Cancer Biology. The center will use computational biology and artificial intelligence to accelerate cancer research and develop personalized therapies. The team will focus on colorectal and skin cancers, with an emphasis on rare cancer pathways and non-European genetic ancestries. The grant is part of the Transformative Computational Biology Grant Program, which is providing a total of nearly $14 million to five research groups. [Extracted from the article]
- Published
- 2024
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