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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Feihong Wu Fadi Towfic Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We analyzed the structural properties and the local surface environment of surface amino acid residues of proteins using a large, non-redundant dataset of 2383 protein chains in dimeric complexes from PDB. We compared the interface residues and non-interface residues based on six properties: side chain orientation, surface roughness, solid angle, ex value, hydrophobicity and interface cluster size. The results of our analysis show that interface residues have side chains pointing inward; interfaces are rougher, tend to be flat, moderately convex or concave and protrude more relative to non-interface surface residues. Interface residues tend to be surrounded by hydrophobic neighbors and tend to form clusters consisting of three or more interfaces residues. These findings are consistent with previous published studies using much smaller datasets, while allowing for more qualitative conclusions due to our larger dataset. Preliminary results suggest the possibility of using the six the properties to identify putative interface residues.

关键词： Proteins Rough surfaces Surface roughness bioinformatics Amino acids Solids Sequences Biomedical computing Computer interfaces Biomedical measurements

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Doubly-stochastic normalization of the Gaussian kernel is robust to heteroskedastic noise

arXiv

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arXiv 2020年

作者： Landa, Boris Coifman, Ronald R. Kluger, Yuval Program in Applied Mathematics Yale University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Pathology Yale University School of Medicine

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic normalization or its symmetric variant). We demonstrate that the doubly-stochastic normalization of the Gaussian kernel with zero main diagonal (i.e. no self loops) is robust to heteroskedastic noise. That is, the doubly-stochastic normalization is advantageous in that it automatically accounts for observations with different noise variances. Specifically, we prove that in a suitable high-dimensional setting where heteroskedastic noise does not concentrate too much in any particular direction in space, the resulting (doubly-stochastic) noisy affinity matrix converges to its clean counterpart with rate m−1/2, where m is the ambient dimension. We demonstrate this result numerically, and show that in contrast, the popular row-stochastic and symmetric normalizations behave unfavourably under heteroskedastic noise. Furthermore, we provide a prototypical example of simulated single-cell RNA sequence data with strong intrinsic heteroskedasticity, where the advantage of the doubly-stochastic normalization for exploratory analysis is evident. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Cover Feature: Diversity Oriented Strategy (DOS) for the Efficient Synthesis of Benzofuro[2,3-b]pyridine Derivatives with Anticancer Activity (ChemMedChem 3/2025)

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ChemMedChem 2025年第3期20卷

作者： Reymark Ereje Jantana Yahuafai Theeranuch Jaroenchuensiri Patcharaporn Supakijjanusorn Sukanya Unson Borwornlak Toopradab Thanyada Rungrotmongkol Somsak Pianwanit Chanat Aonbangkhen Tanatorn Khotavivattana Center of Excellence in Natural Products Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand Department of Physical Sciences College of Science University of the Philippines Baguio Baguio City 2600 Philippines Clinical Research Section Division of Research and Academic Support National Cancer Institute Bangkok 10400 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Pathumwan Bangkok 10330 Thailand Center of Excellence in Computational Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand

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Comparing Kernels for Predicting Protein Binding Sites from Amino Acid Sequence

Comparing Kernels for Predicting Protein Binding Sites from ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Feihong Wu B. Olson D. Dobbs V. Honavar Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA USA

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of three types of kernels functions: identity kernel, sequence-alignment kernel, and amino acid substitution matrix kernel in the case of SVM classifiers for predicting protein-protein, protein-DNA and protein-RNA binding sites. The results show that the identity kernel is quite effective in on all three tasks. The substitution kernel based on amino acid substitution matrices that take into account structural or evolutionary conservation or physicochemical properties of amino acids yields modest improvement.

关键词： Kernel Proteins Amino acids Support vector machines Support vector machine classification Matrices Drugs Signal design Signal analysis Signal processing

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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

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Influence of conformational change of chain unit on the intrinsic negative thermal expansion of polymers

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Chinese Chemical Letters 2021年第4期32卷 1515-1518页

作者： Qiangsheng Sun Ke Jin Yuhui Huang Jia Guo Thanyada Rungrotmongkol Phornphimon Maitarad Changchun Wang State Key Laboratory of Molecular Engineering of Polymers and Department of Macromolecular Science Fudan UniversityShanghai 200433.China Biocatalyst and Environmental Biotechnology Research Unit Department of BiochemistryFaculty of Science and Program in Bioinformatics and Computational BiologyGraduate SchoolChulalongkom UniversityBangkok 10330Thailand Research Center of Nano Science and Technology Shanghai UniversityShanghai 200444China

Negative thermal expansion(NTE) behavior has roused wide interest for the control of thermomechanical properties of functional *** NTE behaviors have been found in kinds of compounds,it remains challenging for polymers to achieve intrinsic NTE *** this work,we systematically studied the conformational change of dibenzocyclooctadiene(DBCOD) derivatives between chair(C) and twist-boat(TB) forms based on density-functional theo ry(DFT) calculations,and found clear evidence of the relationship between the structure of DBCOD units and the thermal contraction behavior of the related *** order to obtain the polymer with NTE property,two conditions should be met for the thermal contracting DBCOD related units as follows:(i) the TB conformation can turn into C conformation as the temperature increases,and(ii) the volume of C conformation is smaller than that of TB *** rule should offer a guidance to exploration of the new intrinsic NTE polymers in the future.

关键词： Negative thermal expansion Conformational change Polyarylamides Density-functional theory Dibenzocyclooctadiene

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Structural characterization of RNA-binding sites of proteins: Preliminary results

Structural characterization of RNA-binding sites of proteins...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Fadi Towfic David C. Gemperline Cornelia Caragea Feihong Wu Drena Dobbs Vasant Honavar Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA Department of Biology Department of Chemistry Carthage College Kenosha WI USA Department of Computer Science Iowa State University Ames IA USA Department of Genetics Development and Cell Biology Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA Department of Computer Science Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA USA

We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.

关键词： RNA Protein engineering biology computing Amino acids bioinformatics Computer interfaces Rough surfaces Surface roughness Solids Machine learning

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Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

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BMC proceedings 2011年第9-sup期5 Suppl 9卷 S37页

作者： Jia Kang Wei Zheng Lun Li Joon Sang Lee Xiting Yan Hongyu Zhao Interdepartmental Program in Computational Biology and Bioinformatics Yale University PO Box 208009 New Haven CT 06520-8114 USA. hongyu.zhao@yale.edu.

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome.

关键词： Bayesian Network Disease Phenotype Area Under Curve Conditional Independence Risk Prediction Model

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Hyperbolic procrustes analysis using riemannian geometry 21

Hyperbolic procrustes analysis using riemannian geometry

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ya-Wei Eileen Lin Yuval Kluger Ronen Talmon Viterbi Faculty of Electrical and Computer Engineering Technion Program in Applied Mathematics Yale University and Interdepartmental Program in Computational Biology and Bioinformatics Yale University and Department of Pathology Yale University

ISBN: (纸本)9781713845393

Label-free alignment between datasets collected at different times, locations, or by different instruments is a fundamental scientific task. Hyperbolic spaces have recently provided a fruitful foundation for the development of informative representations of hierarchical data. Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA). HPA consists of new implementations of the three prototypical Procrustes analysis components: translation, scaling, and rotation, based on the Riemannian geometry of the Lorentz model of hyperbolic space. We analyze the proposed components, highlighting their useful properties for alignment. The efficacy of HPA, its theoretical properties, stability and computational efficiency are demonstrated in simulations. In addition, we showcase its performance on three batch correction tasks involving gene expression and mass cytometry data. Specifically, we demonstrate high-quality unsupervised batch effect removal from data acquired at different sites and with different technologies that outperforms recent methods for label-free alignment in hyperbolic spaces.

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BRAINLM: A FOUNDATION MODEL FOR BRAIN ACTIVITY RECORDINGS 12

BRAINLM: A FOUNDATION MODEL FOR BRAIN ACTIVITY RECORDINGS

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12th International Conference on Learning Representations, ICLR 2024

作者： Caro, Josue Ortega de Fonseca, Antonio H. Rizvi, Syed A. Rosati, Matteo Averill, Christopher Cross, James L. Mittal, Prateek Zappala, Emanuele Dhodapkar, Rahul M. Abdallah, Chadi G. van Dijk, David Wu Tsai Institute United States Interdepartmental Neuroscience Program Yale University United States Baylor College of Medicine United States Department of Computer Science United States Yale School of Medicine United States University of Southern California United States Interdepartmental Program in Computational Biology & Bioinformatics United States Internal Medicine United States Cardiovascular Research Center Yale University United States Department of Mathematics and Statistics Idaho State University United States

We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Brain

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