Machine Learning Based Disease Gene Identification and MHC Immune Protein-peptide Binding Prediction

Machine learning and deep learning methods have been increasingly applied to solve challenging and important bioinformatics problems such as protein structure prediction, disease gene identification, and drug discovery. However, the performances of existing machine learning based predictive models are still not satisfactory. The question of how to exploit the specific properties of bioinformatics data and couple them with the unique capabilities of the learning algorithms remains elusive. In this dissertation, we propose advanced machine learning and deep learning algorithms to address two important problems: mislocation-related cancer gene identification and major histocompatibility complex-peptide binding affinity prediction. Our first contribution proposes a kernel-based logistic regression algorithm for identifying potential mislocation-related genes among known cancer genes. Our algorithm takes protein-protein interaction networks, gene expression data, and subcellular location gene ontology data as input, which is particularly lightweight comparing with existing methods. The experiment results demonstrate that our proposed pipeline has a good capability to identify mislocation-related cancer genes. Our second contribution addresses the modeling and prediction of human leukocyte antigen (HLA) peptide binding of human immune system. We present an allelespecific convolutional neural network model with one-hot encoding. With extensive evaluation over the standard IEDB datasets, it is shown that the performance of our model is better than all existing prediction models. To achieve further improvement, we propose a novel pan-specific model on peptide-HLA class I binding affinities prediction, which allows us to exploit all the training samples of different HLA alleles. iv Our sequence based pan model is currently the only algorithm not using pseudo sequence encoding — a dominant structure-based encoding method in this area. The benchmark studies show that our method could achieve state-of-the-art performance. Our proposed model could be integrated into existing ensemble methods to improve their overall prediction capabilities on highly diverse MHC alleles. Finally, we present a LSTM-CNN deep learning model with attention mechanism for peptide-HLA class II binding affinities and binding cores prediction. Our model achieved very good performance and outperformed existing methods on half of tested alleles. With the help of attention mechanism, our model could directly output the peptide binding core based on attention weight without any additional post- or preprocessing

Novel silica filled deep eutectic solvent based nanofluids for energy transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2- butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

Novel Silica Filled Deep Eutectic Solvent Based Nanofluids for Energy Transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2-butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

CHSMiner: a GUI tool to identify chromosomal homologous segments

<p>Abstract</p> <p>Background</p> <p>The identification of chromosomal homologous segments (CHS) within and between genomes is essential for comparative genomics. Various processes including insertion/deletion and inversion could cause the degeneration of CHSs.</p> <p>Results</p> <p>Here we present a Java software CHSMiner that detects CHSs based on shared gene content alone. It implements fast greedy search algorithm and rigorous statistical validation, and its friendly graphical interface allows interactive visualization of the results. We tested the software on both simulated and biological realistic data and compared its performance with similar existing software and data source.</p> <p>Conclusion</p> <p>CHSMiner is characterized by its integrated workflow, fast speed and convenient usage. It will be useful for both experimentalists and bioinformaticians interested in the structure and evolution of genomes.</p

Interplay between multiple charge-density waves and the relationship with superconductivity in Pdx_xHoTe3_{3}

HoTe$_{3}$, a member of the rare-earth tritelluride ($R$Te$_{3}$) family, and its Pd-intercalated compounds, Pd$_x$HoTe$_{3}$, where superconductivity (SC) sets in as the charge-density wave (CDW) transition is suppressed by the intercalation of a small amount of Pd, are investigated using angle-resolved photoemission spectroscopy (ARPES) and electrical resistivity. Two incommensurate CDWs with perpendicular nesting vectors are observed in HoTe$_{3}$ at low temperatures. With a slight Pd intercalation ($x$ = 0.01), the large CDW gap decreases and the small one increases. The momentum dependence of the gaps along the inner Fermi surface (FS) evolves from orthorhombicity to near tetragonality, manifesting the competition between two CDW orders. At $x$ = 0.02, both CDW gaps decreases with the emergence of SC. Further increasing the content of Pd for $x$ = 0.04 will completely suppress the CDW instabilities and give rise to the maximal SC order. The evolution of the electronic structures and electron-phonon couplings (EPCs) of the multiple CDWs upon Pd intercalation are carefully scrutinized. We discuss the interplay between multiple CDW orders, and the competition between CDW and SC in detail.Comment: 6 pages, 5 figure