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25th DAE-BRNS High Energy Physics Symposium, HEPS 2022

作者： Bhaskar, Arvind Madathil, Anirudhan A. Mandal, Tanumoy Mitra, Subhadip Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad500 032 India Indian Institute of Science Education and Research Thiruvananthapuram Kerala Vithura695 551 India

ISBN: (纸本)9789819702886

The recent discrepancy in the measurement of W boson mass as reported by the CDF collaboration and the longstanding anomalies in muon g-2, RK(∗) and RD(∗) observables suggest a promising path towards new physics. Leptoquarks (LQs) are known for explaining the anomalies separately in the current literature. However, these anomalies may be intertwined at a fundamental level, and there is a single new physics explanation. We present a simple model with two TeV-scale scalar LQs, a weak-singlet S1 and a weak-triplet S3, and an economic flavour ansatz that can explain all the above anomalies simultaneously without violating experimental observations, including the bounds from the LHC. For our purpose, the LQs can be as light as 1.5 TeV, making the model directly testable at the LHC. In addition to this, we discuss their collider phenomenology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bosons

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Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

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Genomics, Proteomics & bioinformatics 2022年第1期20卷 147-162页

作者： Yikai Luo Han Liang Graduate Program in Quantitative and Computational Biosciences Baylor College of MedicineHoustonTX 77030USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Systems Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA

Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The Cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

关键词： Amino acid usage Tissue development Biosynthetic energy Diagnostic biomarker

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Using Machine Learning to Understand the Relationships Between Audiometric Data, Speech Perception, Temporal Processing, And Cognition 48

Using Machine Learning to Understand the Relationships Betwe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Khalil, Rana M. Papanicolaou, Alexandra Chou, Renee Ti Gibbs, Bobby E. Anderson, Samira Gordon-Salant, Sandra Cummings, Michael P. Goupell, Matthew J. University of Maryland Center for Bioinformatics and Computational Biology College Park United States University of Maryland Department of Hearing and Speech Sciences College Park United States

ISBN: (纸本)9781728163277

Aging and hearing loss cause communication difficulties, particularly for speech perception in demanding situations, which have been associated with factors including cognitive processing and extended high-frequency (>8 kHz) hearing. Quantifying such associations and finding other (possibly unintuitive) associations is well suited to machine learning. We constructed ensemble models for 443 participants who varied in age and hearing loss. Audiometric, perceptual, electrophysiological, and cognitive data were used to predict speech perception in noise, reverberation, and with time compression. Speech perception was best predicted by variables associated with audiometric thresholds (including new across-frequency composite variables) between 1-4 kHz, followed by basic temporal processing ability. Cognitive factors and extended high-frequency thresholds had little to no predictive ability of speech perception. Future associations or lack thereof will inform the field as we attempt to better understand the intertwined effects of speech perception, aging, hearing loss, and cognition. © 2023 IEEE.

关键词： Audiology Cognition Machine Learning Speech Perception Temporal Processing

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In-Silico Molecular Docking Analysis of Black Currant Berries (Ribes Nigrum L.) Derived Compounds as Anti-Cervical Cancer Agents 10

In-Silico Molecular Docking Analysis of Black Currant Berrie...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Amanpreet Goel, Anika Rawat, Geetika Santoshi, Seneha Rashida, Shazia Amity University Center for Computational Biology and Bioinformatics Uttar Pradesh Noida201313 India Amity Insitute of Biotechnolgy Amity University Center for Medical Biotechnology Uttar Pradesh Noida201313 India

ISBN: (纸本)9789380544472

Ribes nigrum L., commonly known as blackcurrant berries, is a species of the family Grossulariaceae. The extract of blackcurrant berries is abundant in polyphenols and can promote apoptosis in a variety of cancer cells, thus can act as a potential anticancer agent and shows promising results to treat various cancers. The various compounds present in the blackcurrant berries have already been identified as having anticancer properties in breast cancer and gastric cancer. In order to explore its potential further, we have performed a comprehensive In Silico analysis to study its effect on cervical cancer. A validated epigenetics-related enzyme, protein arginine methyltransferase 5 (PRMT5), has been studied recently as a promising therapeutic target for Cervical cancer. Also, caspase-3 and caspase-9, apoptotic signaling proteins, can also act as potential target proteins for cervical cancer. With an endeavor to study the effect of blackcurrant berries, we have targeted the PRMT5, caspase-3, and caspase-9 for in silico for interaction study. We have used 12 volatile compounds of Ribes nigrum L. for the current study. Molecular docking and further free energy of binding calculations were performed falling in the range of (-5 to -9Kcal/mol) and (-40 to -70Kcal/mol) respectively which suggested strong interactions of selected compounds with the target proteins. The results showed a promising profile of the associated compounds which can be further investigated and evaluated for cervical cancer treatment and management studies. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Binding energy

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computational Identification of Potential Phytochemicals as Inhibitors of Endometriosis using Molecular Docking 10

Computational Identification of Potential Phytochemicals as ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Srivastav, Medha Bhan, Shivam Zeba, Tanzeem Kohli, Reet Kaur Rashid, Shazia Santoshi, Seneha Amity University Center for Computational Biology and Bioinformatics Uttar Pradesh Noida201313 India Amity University Centre for Medical Biotechnology Uttar Pradesh Noida201313 India

ISBN: (纸本)9789380544472

Endometriosis is an enervating condition, which affects very high number of women in their youth worldwide. Although the drugs have definitely evolved during the past decades, still there is no considerable cure or in-depth knowledge of the mechanisms and treatment. Literature suggests that it can be a progressive condition and can be associated with habitual pain in the pelvic region, vaginal bleeding, gravidity, or malignant degenerescence. With conventional methods of treatment there lies a possibility of various side effects, and the symptoms may persist despite treatment. In recent times, experimental substantiation suggested that plant-based drugs could be beneficial for endometriosis and its related symptoms. As a result, molecular docking was used to search for possible plant-based drug targets against vital proteins responsible for endometriosis. The information on endometriosis was acquired from the literature, and 3D structures of proteins were curated from PDB, whereas phytochemical information and 3D structures were retrieved from existing structural databases. Molecules were docked against three selected target proteins. Molecular docking and further free energy of binding calculations were performed which were in a promising range and suggested strong interactions between selected phytochemicals and the target proteins. We anticipate that the phytochemicals will be potential candidates for in vitro and in vivo research, followed by therapeutic treatment of endometriosis. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Proteins

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Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers

Self-Supervised Modality-Agnostic Pre-Training of Swin Trans...

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IEEE International Symposium on Biomedical Imaging

作者： Abhiroop Talasila Maitreya Maity U. Deva Priyakumar Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant obstacle. To address this, we augment the Swin Transformer to learn from different medical imaging modalities, enhancing downstream performance. Our model, dubbed SwinFUSE (Swin Multi-Modal Fusion for UnSupervised Enhancement), offers three key advantages: (i) it learns from both Computed Tomography (CT) and Magnetic Resonance Images (MRI) during pre-training, resulting in complementary feature representations; (ii) a domain-invariance module (DIM) that effectively highlights salient input regions, enhancing adaptability; (iii) exhibits remarkable generalizability, surpassing the confines of tasks it was initially pre-trained on. Our experiments on two publicly available 3D segmentation datasets show a modest 1-2% performance trade-off compared to single-modality models, yet significant out-performance of up to 27% on out-of-distribution modality. This substantial improvement underscores our proposed approach’s practical relevance and real-world applicability. Code is available at: https://***/devalab/SwinFUSE

关键词： Image segmentation Adaptation models Solid modeling Three-dimensional displays Computed tomography Magnetic resonance imaging Magnetic resonance

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SELF-SUPERVISED MODALITY-AGNOSTIC PRE-TRAINING OF SWIN TRANSFORMERS

arXiv

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arXiv 2024年

作者： Talasila, Abhiroop Maity, Maitreya Priyakumar, U. Deva Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India

Unsupervised pre-training has emerged as a transformative paradigm, displaying remarkable advancements in various domains. However, the susceptibility to domain shift, where pre-training data distribution differs from fine-tuning, poses a significant obstacle. To address this, we augment the Swin Transformer to learn from different medical imaging modalities, enhancing downstream performance. Our model, dubbed SwinFUSE (Swin Multi-Modal Fusion for UnSupervised Enhancement), offers three key advantages: (i) it learns from both Computed Tomography (CT) and Magnetic Resonance Images (MRI) during pre-training, resulting in complementary feature representations;(ii) a domain-invariance module (DIM) that effectively highlights salient input regions, enhancing adaptability;(iii) exhibits remarkable generalizability, surpassing the confines of tasks it was initially pre-trained on. Our experiments on two publicly available 3D segmentation datasets show a modest 1-2% performance trade-off compared to single-modality models, yet significant out-performance of up to 27% on out-of-distribution modality. This substantial improvement underscores our proposed approach’s practical relevance and real-world applicability. Code is available at: https://***/devalab/SwinFUSE © 2024, CC BY.

关键词： Magnetic resonance

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Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

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computational and Structural Biotechnology Journal 2025年 27卷 1719-1731页

作者： Tseng, Tzu-Yang Hsieh, Chiao-Hui Liu, Jie-Yu Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies. © 2025 The Authors

关键词： Drug infusion

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Two-step model of paleohexaploidy, ancestral genome reshuffling and plasticity of heat shock response in Asteraceae

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Horticulture Research 2023年第6期10卷 82-95页

作者： Xiangming Kong Yan Zhang Ziying Wang Shoutong Bao Yishan Feng Jiaqi Wang Zijian Yu Feng Long Zejia Xiao Yanan Hao Xintong Gao Yinfeng Li Yue Ding Jianyu Wang Tianyu Lei Chuanyuan Xu Jinpeng Wang Department of Bioinformatics School of Life Sciencesand Center for Genomics and Computational BiologyNorth China University of Science and TechnologyTangshanHebei 063000China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of ScienceBeijing 100093China University of Chinese Academy of Sciences Beijing 100049China

An ancient hexaploidization event in the most but not all Asteraceae plants,may have been responsible for shaping the genomes of many horticultural,ornamental,and medicinal plants that promoting the prosperity of the largest angiosperm family on the ***,the duplication process of this hexaploidy,as well as the genomic and phenotypic diversity of extant Asteraceae plants caused by paleogenome reorganization,are still poorly *** analyzed 11 genomes from 10 genera in Asteraceae,and redated the Asteraceae common hexaploidization(ACH)event∼70.7–78.6 million years ago(Mya)and the Asteroideae specific tetraploidization(AST)event∼41.6–46.2 ***,we identified the genomic homologies generated from the ACH,AST and speciation events,and constructed a multiple genome alignment framework for ***,we revealed biased fractionations between the paleopolyploidization produced subgenomes,suggesting the ACH and AST both are allopolyplodization ***,the paleochromosome reshuffling traces provided clear evidence for the two-step duplications of ACH event in ***,we reconstructed ancestral Asteraceae karyotype(AAK)that has 9 paleochromosomes,and revealed a highly flexible reshuffling of Asteraceae *** specific significance,we explored the genetic diversity of Heat Shock Transcription Factors(Hsfs)associated with recursive whole-genome polyploidizations,gene duplications,and paleogenome reshuffling,and revealed that the expansion of Hsfs gene families enable heat shock plasticity during the genome evolution of *** study provides insights on polyploidy and paleogenome remodeling for the successful establishment of Asteraceae,and is helpful for further communication and exploration of the diversification of plant families and phenotypes.

关键词： shuffling shock plasticity

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BAYESIAN ORACLE FOR BOUNDING INFORMATION GAIN IN NEURAL ENCODING MODELS 11

BAYESIAN ORACLE FOR BOUNDING INFORMATION GAIN IN NEURAL ENCO...

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11th International Conference on Learning Representations, ICLR 2023

作者： Lurz, Konstantin-Klemens Bashiri, Mohammad Walker, Edgar Y. Sinz, Fabian H. Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Department of Physiology Computational Neuroscience Center University of Washington United States Department of Computer Science University Göttingen Germany

In recent years, deep learning models have set new standards in predicting neural population responses. Most of these models currently focus on predicting the mean response of each neuron for a given input. However, neural variability around this mean is not just noise and plays a central role in several theories on neural computation. To capture this variability, we need models that predict full response distributions for a given stimulus. However, to measure the quality of such models, commonly used correlation-based metrics are not sufficient as they mainly care about the mean of the response distribution. An interpretable alternative evaluation metric for likelihood-based models is Normalized Information Gain (NInGa) which evaluates the likelihood of a model relative to a lower and upper bound. However, while a lower bound is usually easy to obtain, constructing an upper bound turns out to be challenging for neural recordings with relatively low numbers of repeated trials, high (shared) variability, and sparse responses. In this work, we generalize the jack-knife oracle estimator for the mean-commonly used for correlation metrics-to a flexible Bayesian oracle estimator for NInGa based on posterior predictive distributions. We describe and address the challenges that arise when estimating the lower and upper bounds from small datasets. We then show that our upper bound estimate is data-efficient and robust even in the case of sparse responses and low signal-to-noise ratio. We further provide the derivation of the upper bound estimator for a variety of common distributions including the state-of-the-art zero-inflated mixture models, and relate NInGa to common mean-based metrics. Finally, we use our approach to evaluate such a mixture model resulting in 90% NInGa performance. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Signal to noise ratio

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