A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement

Learning multiple views with orthogonal denoising autoencoders

Multi-view learning techniques are necessary when data is described by multiple distinct feature sets because single-view learning algorithms tend to overt on these high-dimensional data. Prior successful approaches followed either consensus or complementary principles. Recent work has focused on learning both the shared and private latent spaces of views in order to take advantage of both principles. However, these methods can not ensure that the latent spaces are strictly independent through encouraging the orthogonality in their objective functions. Also little work has explored representation learning techniques for multiview learning. In this paper, we use the denoising autoencoder to learn shared and private latent spaces, with orthogonal constraints | disconnecting every private latent space from the remaining views. Instead of computationally expensive optimization, we adapt the backpropagation algorithm to train our model

Scuba:Scalable kernel-based gene prioritization

Abstract Background The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. Results We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Conclusions Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba

FOLFIRINOX Induction Therapy for Stage 3 Pancreatic Adenocarcinoma

ABSTRACT Background. Reports show that FOLFIRINOX therapy for pancreatic ductal adenocarcinoma (PDAC) results in objective response rates two to threefold higher than those of other regimens. This study aimed to assess response and resection rates for locally unresectable (stage 3) patients initially treated with induction FOLFIRINOX. Methods. The institutional cancer database was queried for patients treated with induction FOLFIRINOX therapy between 2010 and 2013. Patients were included in the study if they were treated at the authors' institution for stage 3 PDAC (locally unresectable) that had been adjudicated at a weekly multidisciplinary tumor board. Results. The study identified 101 patients. The median age was 64 years (range 37-81 years), and the median followup period was 12 months (range 3-37 months). The patients received a median of six cycles (range 1-20 cycles) of induction FOLFIRINOX. No grade 4 or 5 toxicity was recorded. At the initial restaging (median of 3 months after diagnosis), 23 patients (23 %) had developed distant metastases, 15 patients (15 %) had undergone resection, and 63 patients (63 %) had proceeded to chemoradiation. In the group of 63 patients who had proceeded to chemoradiation (median of 9 months after diagnosis), an additional 16 patients (16 %) had undergone resection, and 5 patients (5 %) had developed metastases. A partial radiographic response was observed in 29 % of all the patients, which was associated with ability to perform resection (p = 0.004). The median overall survival time was 11 months for the group that progressed with FOLFIRINOX and 26 months for the group that did not progress. Conclusion. Nearly one third of the patients who had been initially identified as having stage 3 pancreatic carcinoma and had been treated with FOLFIRINOX responded radiographically and underwent tumor resection. A recently completed phase 3 randomized trial for stage 4 pancreatic ductal adenocarcinoma (PDAC) identified FOL-FIRINOX as superior to gemcitabine in terms of radiographic response together with improved progression-free and overall survival. 1 Patients who received FOLFIRINOX experienced a 32 % objective response rate (ORR) compared with 9 % in the gemcitabine arm of the study, which correlated with survival benefit (median overall and progression-free survival, 11 and 6 versus 7 and 3 months, respectively). Retrospective studies of patients with both borderline resectable PDAC (stages 1 and 2) and stage 3 disease (locally unresectable) also have suggested an ORR of approximately 30 % with FOLFIRINOX. 2,3 The reported ORR from non-FOLFIRINOX regimens has generally been in the range of 10 %, including the results of a phase