New book chapter on Springer LNBI book post-CIBB 2015

Our chapter entitled “Validation Pipeline for Computational Prediction of Genomics Annotations”, written by me and Marco Masseroli, has been published in the Springer Lecture Notes in Bioinformatics (LNBI) book of the Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) 2015 conference selected papers.

New journal paper published on BMC Bioinformatics

A paper that I wrote with Pietro Pinoli and Marco Masseroli from Politecnico di Milano just got published by the pre-eminent scientific journal BMC Bioinformatics. It is entitled “Computational algorithms to predict Gene Ontology annotations” and it’s been selected among the papers presented at the CIBB 2013 conference.

It is contained in the Volume 6 Supplement 6 of BMC Bioinformatics, entitled Selected articles from the 10th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Take a look to it!

My poster at NIPS 2014

This year I will be again at NIPS 2014 international conference, and I will defend a poster entitled “Deep Autoencoder Neural Networks for Prediction of Biomolecular Annotations” at two workshops. One is MCLB 2014 – Workshop on Machine Learing in Computational Biology, and the other one is MLCDA 2014 – Workshop on Machine Learning for Clinical Data Analysis, Healthcare and Genomics.

NIPS 2014 will be held in Montreal (Quebec, Canada) from 8th to 13th of December 2014.

See you in Montreal!

Our paper accepted at ACM BCB 2014

I just got notified that our paper entitled “Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions” has been accepted for ACM BCB 2014, the 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics.

This article was written by me, Peter J. Sadowski and Pierre Baldi from University of California Irvine, and reports my project developed during my six month stay in the Orange County.

ACM BCB 2014 conference will be held in Newport Beach (Southern California, USA) in late September 2014. See you there!

Our paper accepted at CIBCB 2014

We just got notified that our paper entitled “Latent Dirichlet Allocation based on Gibbs Sampling for Gene Function Prediction” just got accepted at the IEEE Computational Intelligence in Bioinformatics and Computational Biology CIBCB 2014 conference.

The idea and the implementation of the article ideas were mainly conceived by my colleague Pietro Pinoli. The paper was written by him, me, and our supervisor Marco Masseroli.

The conference will be at Hawaii (Pacific ocean) in May. We’ll see you there, maybe!

Yan LeCun: Proposal for a new publishing model in computer science

I signal to you all this interesting article written by Yan LeCun (New York University Courant Institute & Facebook), in which he proposes a new publishing model for the computer science papers. This strategy seems excellent to me, and it has been adopted for the recent ICLR 2014 conference paper submission. I hope it will be used in the future for the computer science conferences. Here’s the proposal:

Our current publication system should be redesigned to maximize the rate of progress in our field. This means accelerating the speed at which new ideas and results are exchanged, disseminated, and evaluated. This also means minimizing the amount of time each of us spends evaluating other people’s work through reviewing and sifting through the literature. A major issue is that our current system, with its emphasis on highly-selective conferences, is highly biased against innovative ideas and favors incremental tweaks on well-established methods. Ideas that turn out to be highly influential are sometimes held up for months (if not years) in reviewing purgatory, particularly if they require several years to come to maturity (there are a few famous examples, mentioned). The friction in our publication system is slowing the progress of our field. It makes progress incremental. And it makes our conferences somewhat boring.

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My favourite papers and talks at Nips 2013

I just came back from NIPS 2013, an illustrious world conference on machine learning in South Lake Tahoe, California.

Here’s the best papers and talks from the conference:

  • “Dropout training as adaptive regularization” by Stefan Wager, Sida Wang, Percy Liang (Stanford). Interesting research in which authors attribute the dropout algorithm training to an adaptive regularizer, and find common aspects with AdaGrad, an online learning method based on the adaptive gradient descent.
  • “Adaptive dropout for training deep neural networks” by Jimmy Ba, Brendan Frey (University of Toronto). Again on the dropout algorithm, authors investigate some alternatives to picking 0.5 as unity dropout probability, during the dropout training.
  • “Understanding dropout” by Pierre Baldi, Peter J. Sadowski (University of California Irvine). Authors investigate important mathematical issues of the dropout algorithm.
  • “Training and Analysing Deep Recurrent Neural Networks”  by Michiel Hermans, Benjamin Schrauwen (Universiteit Gent). Authors apply deep recurrent neural networks to sequence time series prediction, and show some interesting applications to the character sequence prediction of English Wikipedia text.
  • “Deep supervised and convolutional generative stochastic network for protein secondary structure prediction” by Jian Zhou and Olga Troyanskaya (Princeton), from the Deep Learning workshop. An interesting application of generative stochastic network (GSN) to the important issue of the protein secondary structure prediction.
  • “Tissue-dependent alternative splicing prediction using deep neural network” by Michaek K. K. Leung, Hui Yuan Xiong, Leo J. Lee and Brendan J. Frey (University of Toronto), from the Machine Learning in Computational Biology workshop. Authors apply the dropout algorithm in a deep neural network to predict new regions of tissue alternative splicing.

A really top-level conference with intriguing workshops… thanks a lot to all the organizers!

[EDIT: check out these blog posts by Paul Mineiro, hundalhh, Yisong Yue, Sebastien Bubeck, Memming]