Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.

Mounting evidence suggests that glycans, rather than merely serving as a "shield", contribute critically to antigenicity of the HIV envelope (Env) glycoprotein, representing critical antigenic determinants for many broadly neutralizing antibodies (bNAbs). While many studies have focused on defining the role of individual glycans or groups of proximal glycans in bNAb binding, little is known about the effects of changes in the overall glycan landscape in modulating antibody access and Env antigenicity. Here we developed a systems glycobiology approach to reverse engineer the complexity of HIV glycan heterogeneity to guide antigenicity-based de novo glycoprotein design. bNAb binding was assessed against a panel of 94 recombinant gp120 monomers exhibiting defined glycan site occupancies. Using a Bayesian machine learning algorithm, bNAb-specific glycan footprints were identified and used to design antigens that selectively alter bNAb antigenicity as a proof-of concept. Our approach provides a new design strategy to predictively modulate antigenicity via the alteration of glycan topography, thereby focusing the humoral immune response on sites of viral vulnerability for HIV.

MOTIVATION: RNA expression signals detected by high-density genomic tiling microarrays contain comprehensive transcriptomic information of the target organism. Current methods for determining the RNA transcription units are still computation intense and lack the discriminative power. This article describes an efficient and accurate methodology to reveal complicated transcriptional architecture, including small regulatory RNAs, in microbial transcriptome profiles. RESULTS: Normalized microarray data were first subject to support vector regression to estimate the profile tendency by reducing noise interruption. A hybrid supervised machine learning algorithm, hidden Markov support vector machines, was then used to classify the underlying state of each probe to 'expression' or 'silence' with the assumption that the consecutive state sequence was a heterogeneous Markov chain. For model construction, we introduced a profile geometry learning method to construct the feature vectors, which considered both intensity profiles and changes of intensities over the probe spacing. Also, a robust strategy was used to dynamically evaluate and select the training set based only on prior computer gene annotation. The algorithm performed better than other methods in accuracy on simulated data, especially for small expressed regions with lower (<1) SNR (signal-to-noise ratio), hence more sensitive for detecting small RNAs. AVAILABILITY AND IMPLEMENTATION: Detail implementation steps of the algorithm and the complete result of the transcriptome analysis for a microbial genome Porphyromonas gingivalis W83 can be viewed at http://bioinformatics.forsyth.org/mtd.