Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity.

In silico modelling takes the advantage of accelerating ecotoxicological assessments on hazardous chemicals without conducting risky in vivo experiments under ethic regulation. To date, the prevailing strategy of one model for one species cannot be well generalized to multi-species modelling. In this work, we propose a new strategy of one model for multiple species to facilitate knowledge transfer across aquatic species.

A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters.

Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space.

A machine learning framework for predicting drug-drug interactions.

Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug-drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug-drug interactions via the associations between genes that two drugs target.

In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks.

Pathogen-host protein interactions are fundamental for pathogens to manipulate host signaling pathways and subvert host immune defense. For most pathogens, very few or no experimental studies have been conducted to investigate their signaling cross-talks with host. In this study, we propose a computational framework to validate the biological assumption that human protein-protein interaction (PPI) networks alone are sufficient to infer pathogen-host PPIs via pathogen functional mimicry.

A Computational Framework for Predicting Direct Contacts and Substructures within Protein Complexes.

Understanding the physical arrangement of subunits within protein complexes potentially provides valuable clues about how the subunits work together and how the complexes function. The majority of recent research focuses on identifying protein complexes as a whole and seldom studies the inner structures within complexes. In this study, we propose a computational framework to predict direct contacts and substructures within protein complexes.

Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.

Rapid reconstruction of genome-scale protein-protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e.

A Multi-Label Learning Framework for Drug Repurposing.

Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions.

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.

Background: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.

Methods: In this work, taking M.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks.

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M.

A computational framework for distinguishing direct versus indirect interactions in human functional protein-protein interaction networks.

Recognition of indirect interactions is instrumental to in silico reconstruction of signaling pathways and sheds light on the exploration of unknown physical paths between two indirectly interacting genes. However, very limited computational methods have explicitly exploited the indirect interactions with experimental evidence thus far. In this work, we attempt to distinguish direct versus indirect interactions in human functional protein-protein interaction (PPI) networks via a predictive l-regularized logistic regression model built on the experimental data.

Multi-label ℓ-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks.

Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated.

Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.

Epstein-Barr virus (EBV) plays important roles in the origin and the progression of human carcinomas, e.g. diffuse large B cell tumors, T cell lymphomas, etc.

Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways.

Background: Signaling pathways play important roles in the life processes of cell growth, cell apoptosis and organism development. At present the signal transduction networks are far from complete. As an effective complement to experimental methods, computational modeling is suited to rapidly reconstruct the signaling pathways at low cost.

A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks.

Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods.

AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between Salmonella and human proteins.

Pathogen-host protein-protein interaction (PPI) plays an important role in revealing the underlying pathogenesis of viruses and bacteria. The need of rapidly mapping proteome-wide pathogen-host interactome opens avenues for and imposes burdens on computational modeling. For Salmonella typhimurium, only 62 interactions with human proteins are reported to date, and the computational modeling based on such a small training data is prone to yield model overfitting.

Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens.

Background: Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small.

Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction.

SVM ensemble based transfer learning for large-scale membrane proteins discrimination.

Membrane proteins play important roles in molecular trans-membrane transport, ligand-receptor recognition, cell-cell interaction, enzyme catalysis, host immune defense response and infectious disease pathways. Up to present, discriminating membrane proteins remains a challenging problem from the viewpoints of biological experimental determination and computational modeling. This work presents SVM ensemble based transfer learning model for membrane proteins discrimination (SVM-TLM).

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning.

Recent years have witnessed much progress in computational modeling for protein subcellular localization. However, there are far few computational models for predicting plant protein subcellular multi-localization. In this paper, we propose a multi-label multi-kernel transfer learning model for predicting multiple subcellular locations of plant proteins (MLMK-TLM).

Multi-label multi-kernel transfer learning for human protein subcellular localization.

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated.

Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization.

Protein sub-organelle localization, e.g. submitochondria, seems more challenging than general protein subcellular localization, because the determination of protein's micro-level localization within organelle by fluorescent imaging technique would face up with more difficulties.

Gene ontology based transfer learning for protein subcellular localization.

Background: Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component.

Amino acid classification based spectrum kernel fusion for protein subnuclear localization.

Background: Prediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models for protein subnuclear localization thus far, to the best of our knowledge. Two models were based on protein primary sequence only.