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We live in an era of big data. Voluminous datasets are generated and have to be processed in every area of science and engineering. This is especially true in biology. Efficient techniques are needed to process these data. In particular, we need tools to extract useful information from massive data sets. Society at large can benefit immensely from advances in this arena. For example, information extracted...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Accurate characterization of pathogenic microbes that may be present in food or clinical samples is essential in the design of appropriate intervention strategies. Inherent genomic patterns (codon-biases and rate of evolution) do simplify the classification of microbes at most taxonomic levels (genus and above), but mostly blur classification at Species/Strain levels. Hence, their classification at...
The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative...
I will attempt to cover several interrelated analysis topics, spending more time on parts that resonate with the audience. First, I will introduce our recent study analyzing phenotypic data harvested from over 150 million unique patients. Curiously, these non-genetic large-scale data can be used for genetic inferences. We discovered that complex diseases are associated with unique sets of rare Mendelian...
A major goal in computational biology is the development algorithms, analysis techniques, and tools towards deep mechanistic understanding of life at a molecular level. In the process, computational biology must take advantage of the new developments in artificial intelligence and machine learning, and then move beyond pattern analysis to provide testable hypotheses for experimental scientists. This...
In recent years, research in Artificial Neural Networks (ANNs) has resurged, now under the Deep-Learning umbrella, and grown extremely popular due to major breakthroughs in methodological and computing capabilities. Deep-Learning methods are part of representation-learning algorithms that attempt to extract and organize discriminative information from the data. Recently reported success of DL techniques...
The energy landscape underscores the inherent nature of proteins as dynamic systems interconverting between structures with varying energies. Recently, our laboratory has developed a computational framework that feasibly reconstructs energy landscapes of any forms of a protein of interest, thus allowing investigating in silico the impact of pathogenic mutations on equilibrium structure and dynamics...
Radiomics refers to the computation, analysis and selection of advanced quantitative imaging features with high throughput from standard-of-care medical images acquired using, for instance, CT, PET or MRI. Indeed, the increasing adoption of electronic patient records as well as the diffused use of PACS have made available heterogeneous patient data, spanning different spatial and temporal scales,...
Transcription factors (TFs), as the key regulatory elements of gene transcription, can activate or suppress the transcription by binding to specific sets of DNA sequences. In the past, the introduction of ChIP-seq sequencing technologies provides immense opportunities for precise categorization of TF binding sites. In this talk, we will introduce several novel computational models for integrative...
The remarkable development of deep learning in healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, and biomedical images. In this talk, we concentrate on recent research on differential privacy preserving deep learning. Differential privacy ensures that the adversary cannot infer...
Protein-DNA docking is an important computational technique for generating native or near-native complex models. A docking program typically generates a number of complex conformations and predicts the docking solution based on interaction energies. However, incomplete sampling and energy function deficiencies can result in false positive protein-DNA complex models, which hampers its application in...
In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy...
Many pathogenic mutations percolate to protein dysfunction by altering dynamics. Reconstructing protein energy landscapes promises to relate dynamics to function but is generally infeasible due to the disparate spatio-temporal scales involved. Recent algorithmic innovation allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure data...
Functional region identification is of fundamental importance for protein sequences analysis for a protein family. Such knowledge not only provides a better scientific understanding but also assists drug discovery. Domain annotation is one approach but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally similar sub-sequences and represents them...
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