7,528 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
A Multiple Classifier System Identifies Novel Cannabinoid CB2 Receptor Ligands
open access articleDrugs have become an essential part of our lives due to their ability to improve people’s
health and quality of life. However, for many diseases, approved drugs are not yet available
or existing drugs have undesirable side effects, making the pharmaceutical industry strive to
discover new drugs and active compounds. The development of drugs is an expensive
process, which typically starts with the detection of candidate molecules (screening) for an
identified protein target. To this end, the use of high-performance screening techniques has
become a critical issue in order to palliate the high costs. Therefore, the popularity of
computer-based screening (often called virtual screening or in-silico screening) has rapidly
increased during the last decade. A wide variety of Machine Learning (ML) techniques has
been used in conjunction with chemical structure and physicochemical properties for
screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently
(iii) Multiple Classifier Systems (MCS). In this work, we apply an MCS for virtual screening
(D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid
CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine
(1.834.362 compounds), was virtually screened to identify 48.432 potential active molecules
using D2-MCS. This list was subsequently clustered based on circular fingerprints and from
each cluster, the most active compound was maintained. From these, the top 60 were kept,
and 21 novel compounds were purchased. Experimental validation confirmed six highly
active hits (>50% displacement at 10 μM and subsequent Ki determination) and an
additional five medium active hits (>25% displacement at 10 μM). D2-MCS hence provided a
hit rate of 29% for highly active compounds and an overall hit rate of 52%
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization
Undetected overfitting can occur when there are significant redundancies
between training and validation data. We describe AVE, a new measure of
training-validation redundancy for ligand-based classification problems that
accounts for the similarity amongst inactive molecules as well as active. We
investigated seven widely-used benchmarks for virtual screening and
classification, and show that the amount of AVE bias strongly correlates with
the performance of ligand-based predictive methods irrespective of the
predicted property, chemical fingerprint, similarity measure, or
previously-applied unbiasing techniques. Therefore, it may be that the
previously-reported performance of most ligand-based methods can be explained
by overfitting to benchmarks rather than good prospective accuracy
Applications of Machine Learning to Optimizing Polyolefin Manufacturing
This chapter is a preprint from our book by , focusing on leveraging machine
learning (ML) in chemical and polyolefin manufacturing optimization. It's
crafted for both novices and seasoned professionals keen on the latest ML
applications in chemical processes. We trace the evolution of AI and ML in
chemical industries, delineate core ML components, and provide resources for ML
beginners. A detailed discussion on various ML methods is presented, covering
regression, classification, and unsupervised learning techniques, with
performance metrics and examples. Ensemble methods, deep learning networks,
including MLP, DNNs, RNNs, CNNs, and transformers, are explored for their
growing role in chemical applications. Practical workshops guide readers
through predictive modeling using advanced ML algorithms. The chapter
culminates with insights into science-guided ML, advocating for a hybrid
approach that enhances model accuracy. The extensive bibliography offers
resources for further research and practical implementation. This chapter aims
to be a thorough primer on ML's practical application in chemical engineering,
particularly for polyolefin production, and sets the stage for continued
learning in subsequent chapters. Please cite the original work [169,170] when
referencing
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