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arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

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Evolution and Medicine: An Inquiry-Based High School Curriculum Supplement

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Evolution: Education and Outreach 2011年第4期4卷 603-612页

作者： Beardsley, Paul M. Stuhlsatz, Molly A.M. Kruse, Rebecca A. Eckstrand, Irene A. Gordon, Shefa D. Odenwald, Ward F. BSCS (Biological Sciences Curriculum Study) 5415 Mark Dabling Blvd Colorado Springs 80918 CO United States National Institute of General Medical Sciences Division of Genetics and Developmental Biology Center for Bioinformatics and Computational Biology 45 Center Drive Bethesda 20892-6200 MD United States National Eye Institute Office of Program Planning and Analysis 9000 Rockville Pike Bethesda 20892-6200 MD United States National Institute of Neurological Disorders and Stroke Neural Cell-Fate Determinants Section Bethesda 20892-6200 MD United States Center for Excellence in Mathematics and Science Teaching and Department of Biological Sciences CEMaST 4-2-515 California State Polytechnic University 3801 W. Temple Ave. Pomona 91768 CA United States

Evolution and Medicine is a curriculum supplement designed by the National Institutes of Health (NIH) and the Biological Sciences Curriculum Study (BSCS) for high school students. The supplement is freely available from NIH's Office of Science Education (OSE) as a part of the NIH curriculum supplement series. Development of the supplement was a collaborative effort that included input from a panel of experts in medicine, evolution, education, and educational technology. In total, the curriculum supplement includes five inquiry-based lessons that are integrated into the BSCS 5E instructional model (based on constructivist learning theory). The goal was to develop a 2-week curriculum to help students understand major concepts of evolution using the dynamic, modern, and relevant context of medicine. A diverse group of students and teachers across the US participated in a formative evaluation of a field test version of the curriculum. High school students made significant learning gains from pretest to posttest, with a relatively large effect size for student understanding of common ancestry and a relatively small effect size for student understanding of natural selection. There was no statistically significant difference in achievement gains between white students and all other racial/ethnic categories. Overall, the evaluation suggests that a curriculum that emphasizes the role of evolution in medicine, uses a constructivist instructional model, and is grounded in inquiry is relatively well-received by teachers and students and shows promise for increasing student learning in evolution. © 2011 Springer Science+Business Media, LLC.

关键词： Common ancestry Evolution Evolution and medicine High school Inquiry Instructional model Natural selection

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Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors

Research Square

引用

Research Square 2021年

作者： Palmer, Adam C. Izar, Benjamin Hwangbo, Haeun Sorger, Peter K. Department of Pharmacology Computational Medicine Program Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel HillNC United States Laboratory of Systems Pharmacology Harvard Program in Therapeutic Science Harvard Medical School BostonMA United States Department of Medicine Division of Hematology and Oncology Columbia University Medical Center Columbia Center for Translational Tumor Immunology New YorkNY United States Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel HillNC United States

Hundreds of clinical trials are currently underway testing combinations of Immune Checkpoint Inhibitors (ICIs) with other cancer therapies in the hope that combination therapy will be more effective than monotherapy. Enhanced efficacy is often equated with drug additivity or synergy arising from mechanisms such as immune priming. In this paper we re-analyze progression free survival data from thirteen recent Phase III trials of ICI combinations and conclude that observed benefits are accurately accounted for by an increased chance that each patient will respond to a single agent. This is consistent with the predictions of drug independence not additivity or synergy. Using independence calculations, the efficacy of new ICI combinations can be predicted from monotherapy data (Pearson r=0.98, P -9, n = 4173 patients and 8 types of cancer), although predicting adverse effects is not yet possible. The benefits from true pharmacological additivity or synergy exceed those of drug independence, but achieving them is likely to require methods that identify patients in whom multiple constituents of a drug combination are active. © 2021, CC BY.

关键词： Oncology

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Characterizing the mechanical response of metallic glasses to uniaxial tension using a spring network model

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Physical Review Materials 2023年第7期7卷 073605-073605页

作者： Aya Nawano Weiwei Jin Jan Schroers Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

A coarse-grained spring network model is proposed for the prediction of the mechanical response of metallic glasses as a function of the microstructure prior to loading. This model describes the mechanical response of metallic glasses using a network of parallel springs that can break and reform, mimicking atomic rearrangements during deformation. We compare predictions of the spring network model for stress versus strain to results from numerical simulations of athermal quasistatic, uniaxial tensile deformation of Cu50Zr50 metallic glasses using Lennard-Jones (LJ) and embedded atom method (EAM) atomic interactions. We show that both the LJ and EAM models possess qualitatively similar stress σ versus strain γ curves. By specifying five parameters [ultimate strength, strain at ultimate strength, slopes of σ(γ) at γ=0 and at large strain, and strain at fracture where σ=0], we demonstrate that the spring network model can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu50Zr50, as well as recent experimental studies of several Zr-based metallic glasses.

关键词： Alloys Amorphous materials Glasses Packing & jamming problems Molecular dynamics

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ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins

arXiv

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arXiv 2022年

作者： Castro, Egbert Godavarthi, Abhinav Rubinfien, Julian Givechian, Kevin Bhaskar, Dhananjay Krishnaswamy, Smita Computational Biology and Bioinformatics Program Yale University New HavenCT06511 United States Department of Applied Mathematics Yale University New HavenCT06511 United States Department of Genetics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information. © 2022, CC BY-NC-ND.

关键词： Proteins

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Structural insights into the role of N-terminal integrity in PhoSL for core-fucosylated N-glycan recognition

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International Journal of Biological Macromolecules 2024年 255卷 128309页

作者： Lou, Yuan-Chao Tu, Cheng-Fen Chou, Chun-Chi Yeh, Hsin-Hong Chien, Chia-Yu Sadotra, Sushant Chen, Chinpan Yang, Ruey-Bing Hsu, Chun-Hua Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Department of Agricultural Chemistry National Taiwan University Taipei106 Taiwan Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Biomedical Translation Research Center Academia Sinica Taipei115 Taiwan Ph.D. Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei110 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei106 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei106 Taiwan

PhoSL (Pholiota squarrosa Lectin) has an exceptional binding affinity for biomolecules with core-fucosylated N-glycans. This modification involves the addition of fucose to the inner N-acetylglucosamine within the N-glycan structure and is known to influence many physiological processes. Nevertheless, the molecular interactions underlying high-affinity binding of native PhoSL to core-fucosylated N-glycans remain largely unknown. In this study, we devised a strategy to produce PhoSL with the essential structural characteristics of the native protein (n-PhoSL). To do so, a fusion protein was expressed in E. coli and purified. Then, enzymatic cleavage and incubation with glutathione were utilized to recapitulate the native primary structure and disulfide bonding pattern. Subsequently, we identified the residues crucial for n-PhoSL binding to core-fucosylated chitobiose (N2F) via NMR spectroscopy. Additionally, crystal structures were solved for both apo n-PhoSL and its N2F complex. These analyses suggested a pivotal role of the N-terminal amine in maintaining the integrity of the binding pocket and actively contributing to core-fucose recognition. In support of this idea, the inclusion of additional residues at the N-terminus considerably reduced binding affinity and PhoSL cytotoxicity toward breast cancer cells. Taken together, these findings can facilitate the utilization of PhoSL in basic research, diagnostics and therapeutic strategies. © 2023 Elsevier B.V.

关键词： Proteins

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Spectral Top-Down Recovery of Latent Tree Models

arXiv

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arXiv 2021年

作者： Aizenbud, Yariv Jaffe, Ariel Wang, Meng Hu, Amber Amsel, Noah Nadler, Boaz Chang, Joseph T. Kluger, Yuval Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Computer Science Weizmann Institute of Science Rehovot76100 Israel Department of Statistics Yale University New HavenCT06520 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Pathology Yale University New HavenCT06511 United States

Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, recover the structure separately of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop Spectral Top-Down Recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

arXiv

引用

arXiv 2020年

作者： Kadelka, Claus Butrie, Taras-Michael Hilton, Evan Kinseth, Jack Schmidt, Addison Serdarevic, Haris Department of Mathematics Iowa State University AmesIA50011 United States Department of Aerospace Engineering Iowa State University AmesIA50011 United States Department of Computer Science Iowa State University AmesIA50011 United States Bioinformatics and Computational Biology Program Iowa State University AmesIA50011 United States

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Inhibition of topoisomerase IIα and induction of DNA damage in cholangiocarcinoma cells by altholactone and its halogenated benzoate derivatives

引用

Biomedicine and Pharmacotherapy 2020年 127卷 110149-110149页

作者： Kitdumrongthum, Sarunya Reabroi, Somrudee Suksen, Kanoknetr Tuchinda, Patoomratana Munyoo, Bamroong Mahalapbutr, Panupong Rungrotmongkol, Thanyada Ounjai, Puey Chairoungdua, Arthit Toxicology Graduate Program Faculty of Science Mahidol University Bangkok Thailand Excellent Center for Drug Discovery (ECDD) Mahidol University Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Department of Physiology Faculty of Science Mahidol University Bangkok Thailand Department of Chemistry Faculty of Science Mahidol University Bangkok Thailand Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand

Topoisomerase IIα enzyme (Topo IIα) plays a critical function in DNA replication process and is considered to be a promising target of anti-cancer drugs. In the present study, we reported that the altholactone derivatives modified by adding a halogenated benzoate group showed greater inhibitory activity on Topo IIα enzyme in cell-free system concomitant with cytotoxicity against the CCA cell lines (KKU-M055 and KKU-M213) than those of the parent altholactone. However, the cytotoxic activities of four halogenated benzoate altholactone derivatives including iodo-, fluoro-, chloro-, and bromobenzoate derivatives (compound 1, 2, 3, and 4, respectively) were of equal potency. The fluorobenzoate derivative (compound 2) was chosen for investigating the underlying mechanism in CCA cells. Compound 2 arrested CCA cell cycle at sub G1 phase and induced apoptotic cell death. It markedly inhibited Topo IIα protein expression in both KKU-M055 and KKU-M213 cells, which was accompanied by DNA double-strand breaks demonstrated by an increase in phosphorylated H2A.X protein. Interestingly, KKU-M055 cells, which express higher Topo IIα mRNA compared to KKU-M213 cells, showed greater sensitivity to the compound, indicating the selectivity of the compound to Topo IIα enzyme. By computational docking analysis, the binding affinity of altholactone (-52.5 kcal/mol) and compound 2 (-56.7 kcal/mol) were similar to that of the Topo II poison salvicine (-53.7 kcal/mol). The aromatic moiety of both altholactones embedded in a hydrophobic pocket of Topo II ATPase domain. In addition, compound 2 induced the formation of linear DNA in Topo II-mediated cleavage assay. Collectively, our results demonstrate that the addition of fluorobenzoyl group to altholactone enhances potency and selectivity to inhibit type IIα topoisomerases. Atholactone and fluorobenzoate derivative act as Topo II cleavage complexes stabilizing compounds or Topo II poisons preferentially through binding at ATPase domain of Topo

关键词： Altholactone Cholangiocarcinoma Halogenated benzoate Topoisomerase IIα

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

引用

arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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