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PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.jl functions as a find-and-replace algorithm on crystal structures by leveraging (i) Ullmann's algorithm to search for subgraphs of the crystal structure graph that are isomorphic to the graph of a query fragment and (ii) the orthogonal Procrustes algorithm to align a replacement fragment with a targeted substructure of the crystal structure for installation. The prominent application of PoreMatMod.jl is to generate libraries of hypothetical structures for virtual screenings. For example, one can install functional groups on the linkers of a parent MOF, mimicking postsynthetic modification. Other applications of PoreMatMod.jl to modify crystal structure models include introducing defects with precision and correcting artifacts of X-ray structure determination (adding missing hydrogen atoms, resolving disorder, and removing guest molecules). The find-and-replace operations implemented by PoreMatMod.jl can be applied broadly to diverse atomistic systems for various in silico structural modification tasks.
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Algoritmos , Estruturas Metalorgânicas , Estruturas Metalorgânicas/química , PorosidadeRESUMO
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a dataset of 382 molecules with toxicity labels from honey bee exposure experiments, we train a support vector machine (SVM) to predict the toxicity of pesticides to honey bees. We compare two representations of the pesticide molecules: (i) a random walk feature vector listing counts of length-L walks on the molecular graph with each vertex- and edge-label sequence and (ii) the Molecular ACCess System (MACCS) structural key fingerprint (FP), a bit vector indicating the presence/absence of a list of pre-defined subgraph patterns in the molecular graph. We explicitly construct the MACCS FPs but rely on the fixed-length-L random walk graph kernel (RWGK) in place of the dot product for the random walk representation. The L-RWGK-SVM achieves an accuracy, precision, recall, and F1 score (mean over 2000 runs) of 0.81, 0.68, 0.71, and 0.69, respectively, on the test data set-with L = 4 being the mode optimal walk length. The MACCS-FP-SVM performs on par/marginally better than the L-RWGK-SVM, lends more interpretability, but varies more in performance. We interpret the MACCS-FP-SVM by illuminating which subgraph patterns in the molecules tend to strongly push them toward the toxic/non-toxic side of the separating hyperplane.
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Praguicidas , Animais , Abelhas , Praguicidas/análise , Praguicidas/toxicidade , Máquina de Vetores de SuporteRESUMO
In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host- and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants consumed a single serving of broccoli (n = 16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites. Mass spectrometry analysis indicated 133 metabolites in broccoli sprouts and 139 metabolites in the alfalfa sprouts were labeled with deuterium isotopes. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5,600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13 deuterium-incorporated metabolomic features were detected in urine representing 8 urine metabolites. Plasma was analyzed among collard green consumers and 11 labeled features were detected representing 5 plasma metabolites. These deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-O-glucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated. This work presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.
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Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.
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Acústica , Aves/classificação , Vocalização Animal/classificação , Algoritmos , Animais , Aves/fisiologia , Ruído/prevenção & controle , Mascaramento Perceptivo/fisiologia , Reprodutibilidade dos Testes , Espectrografia do Som , Gravação em Fita , Vocalização Animal/fisiologiaRESUMO
A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.
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Microbiota , Microbiota/fisiologia , Aprendizado de MáquinaRESUMO
Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH4/N2/CO2/C2H6mixtures relevant to natural gas sensing.
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Histopathological image analysis is a challenging task due to a diverse histology feature set as well as due to the presence of large non-informative regions in whole slide images. In this paper, we propose a multiple-instance learning (MIL) method for image-level classification as well as for annotating relevant regions in the image. In MIL, a common assumption is that negative bags contain only negative instances while positive bags contain one or more positive instances. This asymmetric assumption may be inappropriate for some application scenarios where negative bags also contain representative negative instances. We introduce a novel symmetric MIL framework associating each instance in a bag with an attribute which can be either negative, positive, or irrelevant. We extend the notion of relevance by introducing control over the number of relevant instances. We develop a probabilistic graphical model that incorporates the aforementioned paradigm and a corresponding computationally efficient inference for learning the model parameters and obtaining an instance level attribute-learning classifier. The effectiveness of the proposed method is evaluated on available histopathology datasets with promising results.
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Processamento de Imagem Assistida por Computador , Modelos EstatísticosRESUMO
Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our method using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.